Search, filter, and sort every tracked model by provider, use case, pricing tier, speed, and context window — all in one place.
Rankings refresh dailyScored on 6 criteriaNo paid rankings
Instant answer
If you want the shortest answer: Claude Opus 4.7 for coding and writing, Mistral Small 3.1 for cost-sensitive work, and Anthropic: Claude 3 Haiku when latency and throughput matter most.
Use the directory to compare by the thing that actually changes the decision: coding benchmark score, writing quality, cost per million tokens, speed, or context window size. That usually narrows the right model in under a minute.
The current directory includes 119 models across multiple providers, with all entries mapped to the same pricing, speed, and use-case structure.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
Comparison table
Compare the tradeoffs
This table compares the defaults most people actually need to understand first: best overall, best budget, fastest broad-use option, and the strongest cheap coding specialist.
Ultra-cheap multimodal model for massive-volume, low-complexity pipelines.
When to use what
Use this as a practical filter before you start browsing the whole directory. It shows which leading option fits each common decision style and where it becomes the wrong pick.
Gemini 2.0 Flash Lite is Google's ultra-budget, high-speed model designed for high-volume, cost-sensitive applications. It sits below Gemini 2.0 Flash in capability but offers the lowest price point in the Gemini 2.0 family with a massive 1M token context window.
Verdict
The go-to model when cost and throughput are everything and task complexity is low.
Quality score
57%
Pricing
$0.07/1M in
$0.30/1M out
Speed
Very fast
Best for high-throughput, cost-sensitive pipelines where speed and price matter more than top-tier reasoning quality.
Context
1.0M tokens
Pricing is among the lowest available in any major provider's lineup as of mid-2025. Context window of 1M tokens is a significant differentiator at this price tier. Check Google AI Studio and Vertex AI for rate limits on high-volume usage.
Gemini 2.0 Flash is Google's high-speed, cost-efficient multimodal model built for high-volume production workloads, offering a massive 1M token context window at near-throwaway pricing. It supports text, image, audio, and video inputs with strong instruction-following and tool-use capabilities.
Verdict
The best bang-for-buck multimodal workhorse for developers who need speed, scale, and a massive context window.
Quality score
76%
Pricing
$0.10/1M in
$0.40/1M out
Speed
Very fast
Best for high-throughput pipelines and agentic tasks where speed and cost matter more than peak reasoning quality.
Context
1.0M tokens
Pricing listed is for standard (non-cached) input/output. Context caching is available and can reduce costs significantly for repeated long-context calls. Image and audio inputs are priced separately. Free tier available via Google AI Studio.
BudgetFastLong ContextMultimodalGoogle
Best for
High-throughput pipelines and agentic tasks where speed and cost matter more than peak reasoning quality.
Gemini 2.5 Flash Lite is Google's lightest and most cost-efficient model in the 2.5 family, designed for high-throughput tasks where speed and price matter more than peak intelligence. It retains the massive 1M token context window from its larger siblings while cutting costs to a fraction of Gemini 2.5 Pro.
Verdict
The best cheap model for long-document pipelines, but don't expect flagship-level reasoning.
Quality score
57%
Pricing
$0.10/1M in
$0.40/1M out
Speed
Very fast
Best for high-volume, latency-sensitive applications like document triage, chatbot pipelines, and content classification at scale.
Context
1.0M tokens
Pricing is approximate based on listed rates. As a 'Lite' model, it may not support all multimodal features available in full Flash or Pro variants. Check Google AI Studio for feature availability and rate limits.
BudgetFastLong ContextHigh VolumeGoogle
Best for
High-volume, latency-sensitive applications like document triage, chatbot pipelines, and content classification at scale.
Gemini 2.5 Flash Lite Preview 09-2025 is Google's most cost-optimized variant of the Gemini 2.5 Flash family, designed for high-throughput, latency-sensitive applications at near-commodity pricing. It offers a massive 1M token context window at just $0.10/1M input tokens, positioning it as one of the cheapest long-context models available.
Verdict
The go-to model for cost-sensitive, high-volume pipelines that need a massive context window without breaking the budget.
Quality score
62%
Pricing
$0.10/1M in
$0.40/1M out
Speed
Very fast
Best for high-volume document processing, classification pipelines, and lightweight coding tasks where cost per token matters more than peak quality.
Context
1.0M tokens
This is a preview model (09-2025 versioned) and may be subject to breaking changes or deprecation. Pricing is approximate based on listed rates. Not recommended for production systems requiring SLA guarantees. Check Google AI Studio or Vertex AI for GA alternatives.
budgetlong-contextfasthigh-throughputpreview
Best for
High-volume document processing, classification pipelines, and lightweight coding tasks where cost per token matters more than peak quality.
GPT-4.1 Nano is OpenAI's smallest and most cost-efficient model in the GPT-4.1 family, designed for high-throughput, latency-sensitive tasks at near-commodity pricing. It offers a 1M token context window at just $0.10/1M input tokens, making it one of the cheapest large-context models available.
Verdict
The best pick for budget-conscious, high-volume workloads that don't demand frontier intelligence.
Quality score
54%
Pricing
$0.10/1M in
$0.40/1M out
Speed
Very fast
Best for high-volume production workloads like classification, extraction, summarization, and simple q&a where cost and speed matter more than frontier reasoning.
Context
1.0M tokens
Pricing is $0.10/1M input and $0.40/1M output tokens. Officially supersedes GPT-4o in OpenAI's lineup for lightweight use cases. Context window of ~1.047M tokens is one of the largest available at this price tier.
BudgetFastLong ContextHigh VolumeOpenAI
Best for
High-volume production workloads like classification, extraction, summarization, and simple Q&A where cost and speed matter more than frontier reasoning.
Gemini 2.5 Flash is Google's fast, cost-efficient multimodal model built for high-throughput tasks requiring a million-token context window at budget pricing. It balances speed and capability across text, code, and vision tasks without the cost of flagship models like Gemini 2.5 Pro.
Verdict
The go-to budget model for long-context and multimodal workloads where speed and scale matter.
Quality score
76%
Pricing
$0.30/1M in
$2.50/1M out
Speed
Very fast
Best for high-volume document processing, summarization, and coding assistance where cost and speed matter more than peak accuracy.
Context
1.0M tokens
Output cost ($2.5/1M) is disproportionately higher than input cost ($0.3/1M), so generation-heavy use cases may see costs add up faster than expected. Thinking/reasoning mode may be available but incurs additional cost.
BudgetFastLong ContextMultimodalGoogle
Best for
High-volume document processing, summarization, and coding assistance where cost and speed matter more than peak accuracy.
GPT-4.1 Mini is OpenAI's cost-optimized small model from the GPT-4.1 family, designed to deliver strong instruction-following and coding performance at a fraction of flagship pricing. It targets high-volume, latency-sensitive applications where cost efficiency matters more than peak capability.
Verdict
The go-to budget workhorse for high-volume OpenAI API users who need GPT-4.1 quality at GPT-3.5 prices.
Quality score
65%
Pricing
$0.40/1M in
$1.60/1M out
Speed
Very fast
Best for high-volume production workloads that need reliable gpt-4-class instruction following without flagship pricing.
Context
1.0M tokens
Pricing shown is $0.40 input / $1.60 output per 1M tokens. Cached input tokens are significantly cheaper. The 1M token context window is a standout feature at this price tier — few competitors match it. Supersedes GPT-4o as the recommended default for cost-conscious applications.
BudgetFastLong ContextOpenAIProduction
Best for
High-volume production workloads that need reliable GPT-4-class instruction following without flagship pricing.
Gemini 3 Flash Preview is Google's budget-tier multimodal model optimized for high-throughput, low-latency tasks at scale. It offers a massive 1M token context window at aggressive pricing, making it a strong contender for cost-sensitive production workloads.
Verdict
A fast, affordable workhorse for long-context and high-volume tasks — just don't build critical systems on a Preview model.
Quality score
74%
Pricing
$0.50/1M in
$3.00/1M out
Speed
Very fast
Best for high-volume document processing, summarization pipelines, and long-context tasks where cost efficiency matters more than frontier-level reasoning.
Context
1.0M tokens
This is a preview model and may have limited availability, unstable rate limits, and pricing that changes before general availability. Output cost at $3/1M is notably higher than input cost, so applications generating long outputs should budget accordingly.
BudgetLong ContextFastMultimodalPreview
Best for
High-volume document processing, summarization pipelines, and long-context tasks where cost efficiency matters more than frontier-level reasoning.
Google's flagship with the largest context window of any frontier model at 2M tokens, Deep Think reasoning, and the best price-to-performance among premium models.
Verdict
Best for research and deep document analysis — 2M context at the best premium price.
Quality score
88%
Pricing
$2.00/1M in
$12.00/1M out
Speed
Balanced
Best for research, deep document analysis, and long-context reasoning at competitive pricing
Context
2M tokens
The 2M context window is a genuine competitive advantage — no other frontier model gets close for document-heavy workflows.
Research leader2M contextBest value premiumDeep Think
Best for
Research, deep document analysis, and long-context reasoning at competitive pricing
GPT-5 Nano is OpenAI's smallest and fastest model in the GPT-5 family, optimized for high-throughput, low-latency tasks at near-minimal cost. It supersedes GPT-4o as the go-to option for lightweight inference at scale.
Verdict
The fastest and cheapest way into the GPT-5 ecosystem, built for scale rather than depth.
Quality score
58%
Pricing
$0.05/1M in
$0.40/1M out
Speed
Very fast
Best for high-volume, latency-sensitive applications like classification, autocomplete, summarization, and lightweight chat where cost-per-token matters most.
Context
400k tokens
Output cost of ~$0.40/1M tokens means output-heavy workloads (long generations) will accumulate cost faster than input-heavy ones. Best suited for tasks where outputs are short-to-medium length. No image generation capability.
BudgetFastHigh VolumeLong ContextGPT-5 Family
Best for
High-volume, latency-sensitive applications like classification, autocomplete, summarization, and lightweight chat where cost-per-token matters most.
GPT-5 Mini is OpenAI's budget-tier distillation of GPT-5, designed for high-volume, cost-sensitive tasks that don't require full flagship reasoning depth. It supersedes GPT-4o with improved instruction following and a massively expanded 400K context window at a fraction of the cost.
Verdict
The new budget default for OpenAI API users: faster, cheaper, and smarter than GPT-4o with a context window that punches well above its price tier.
Quality score
66%
Pricing
$0.25/1M in
$2.00/1M out
Speed
Very fast
Best for high-volume production workloads — chatbots, summarization pipelines, and document q&a — where cost efficiency matters more than peak reasoning.
Context
400k tokens
Output cost of $2/1M tokens is higher than some competing budget models (Gemini Flash at ~$0.60/1M output). At scale, output-heavy tasks may erode cost advantages — monitor token ratios carefully. Supersedes GPT-4o, which may be deprecated on a rolling basis.
BudgetFastLong ContextHigh VolumeOpenAI
Best for
High-volume production workloads — chatbots, summarization pipelines, and document Q&A — where cost efficiency matters more than peak reasoning.
GPT-5.1-Codex-Mini is OpenAI's budget-tier coding-specialized model built on the GPT-5.1 architecture, optimized for code generation, completion, and debugging at low cost. It offers a 400K context window, making it practical for large codebases without the price tag of flagship models.
Verdict
The sharpest budget coding model available if you need speed, volume, and a long context window without breaking your API budget.
Quality score
63%
Pricing
$0.25/1M in
$2.00/1M out
Speed
Very fast
Best for high-volume code generation, autocomplete pipelines, and developer tooling where cost efficiency matters more than peak reasoning depth.
Context
400k tokens
At $2/1M output tokens, costs can accumulate in verbose code-generation tasks — monitor output token usage carefully in agentic loops. Not a general-purpose flagship replacement; best deployed alongside a stronger model for planning/reasoning layers.
CodingBudgetLong ContextFastCodex
Best for
High-volume code generation, autocomplete pipelines, and developer tooling where cost efficiency matters more than peak reasoning depth.
Ministral 3B is Mistral's ultra-compact edge model designed for low-latency, cost-sensitive deployments. It punches above its weight for a sub-4B parameter model, handling instruction following, summarization, and lightweight reasoning at near-negligible cost.
Verdict
The go-to model for bulk processing tasks where cost and speed trump quality.
Quality score
50%
Pricing
$0.15/1M in
$0.15/1M out
Speed
Very fast
Best for high-volume, latency-sensitive applications where cost per token matters more than top-tier quality.
Context
262k tokens
The '8B 2512' in the model name likely refers to a specific versioned release; despite the naming, this is based on Mistral's 3B architecture. Confirm parameter count and capabilities with Mistral's official documentation before production use.
budgetedgefastlong-contextcompact
Best for
High-volume, latency-sensitive applications where cost per token matters more than top-tier quality.
Ministral 3B is Mistral's compact edge-optimized model designed for high-throughput, low-latency tasks at an extremely competitive price point. Despite its small size, it supports a 262K context window, making it unusually capable for a sub-$0.20/1M token model.
Verdict
An ultra-cheap, fast model with a surprisingly large context window, but quality limitations make it a pipeline tool rather than a general assistant.
Quality score
48%
Pricing
$0.20/1M in
$0.20/1M out
Speed
Very fast
Best for high-volume, cost-sensitive workflows like document triage, classification, summarization, and lightweight coding assistance where budget is the primary constraint.
Context
262k tokens
Model name suggests a December 2025 revision ('2512'). Pricing is symmetric at $0.20/1M for both input and output, which simplifies cost modeling. Confirm availability on your target API platform as Mistral model availability varies by provider.
budgetedgesmall modellong contexthigh throughput
Best for
High-volume, cost-sensitive workflows like document triage, classification, summarization, and lightweight coding assistance where budget is the primary constraint.
Grok Code Fast 1 is xAI's budget-tier coding-focused model optimized for speed and cost efficiency, built on xAI's infrastructure with a 256K context window. It targets developers who need rapid code generation and completion at near-commodity pricing.
Verdict
A scrappy, low-cost coding model worth benchmarking for high-volume pipelines, but output pricing limits its ceiling.
Quality score
45%
Pricing
$0.20/1M in
$1.50/1M out
Speed
Very fast
Best for high-volume, low-latency coding tasks where cost per token matters more than peak quality.
Context
256k tokens
Pricing is asymmetric: input at ~$0.20/1M is excellent, but $1.50/1M output undercuts its budget appeal for generation-heavy use. Availability through xAI's API; check for rate limits and regional availability as xAI's infrastructure is still scaling.
budgetcodingfastxAIcode-focused
Best for
High-volume, low-latency coding tasks where cost per token matters more than peak quality.
Claude 3 Haiku is Anthropic's fastest and most affordable Claude 3 model, designed for high-throughput tasks where speed and cost efficiency matter more than peak intelligence. It delivers surprisingly capable responses for a budget tier model, with a generous 200K context window.
Verdict
A capable budget workhorse, but Claude 3.5 Haiku has made it mostly obsolete for new deployments.
Quality score
53%
Pricing
$0.25/1M in
$1.25/1M out
Speed
Very fast
Best for high-volume production pipelines, customer support bots, and real-time text processing where cost and latency are critical constraints.
Context
200k tokens
Claude 3 Haiku is part of the original Claude 3 family (March 2024). Anthropic has since released Claude 3.5 Haiku, which is generally recommended over this model for new use cases. Still widely available via Anthropic API and AWS Bedrock.
BudgetFastHigh VolumeLong ContextProduction
Best for
High-volume production pipelines, customer support bots, and real-time text processing where cost and latency are critical constraints.
Claude 3.5 Haiku is Anthropic's fastest and most affordable model in the Claude 3.5 family, designed for high-throughput tasks requiring quick responses without sacrificing Claude's core instruction-following quality. It handles a massive 200K context window while maintaining speed suitable for production pipelines.
Verdict
The fastest way to get Claude's quality in production — just don't confuse 'fast' with 'cheap'.
Quality score
64%
Pricing
$0.80/1M in
$4.00/1M out
Speed
Very fast
Best for high-volume, latency-sensitive applications like chatbots, classification, data extraction, and agentic tool use where speed and cost matter more than peak reasoning depth.
Context
200k tokens
Output cost of $4/1M is notably higher than competing fast/mini models. Input cost at ~$0.80/1M is competitive. Best value emerges in input-heavy pipelines like document classification or RAG retrieval where output tokens are minimal.
High-volume, latency-sensitive applications like chatbots, classification, data extraction, and agentic tool use where speed and cost matter more than peak reasoning depth.
Claude Haiku 4.5 is Anthropic's latest lightweight model in the Claude 4 family, optimized for speed and cost-efficiency while retaining strong instruction-following and reasoning capabilities. It supersedes Claude 4 Haiku with improved performance across coding, summarization, and conversational tasks.
Verdict
The best balance of speed, context length, and cost in Anthropic's lineup for production-scale deployments.
Quality score
68%
Pricing
$1.00/1M in
$5.00/1M out
Speed
Very fast
Best for high-volume production pipelines and real-time applications that need claude-quality output without flagship-model costs.
Context
200k tokens
Priced at $1/1M input and $5/1M output tokens, placing it above true budget models like Gemini Flash but below mid-tier flagships. Confirm availability of extended thinking or tool-use features via Anthropic's API documentation, as Haiku-tier models sometimes receive these capabilities later than Sonnet/Opus.
Llama Guard 4 12B is Meta's specialized safety classification model designed to detect and filter harmful content in LLM inputs and outputs. It's purpose-built for content moderation pipelines, not general-purpose text generation.
Verdict
The go-to cheap, fast content moderation layer for production LLM pipelines.
Quality score
15%
Pricing
$0.18/1M in
$0.18/1M out
Speed
Very fast
Best for automated content safety screening and policy enforcement in llm-powered applications
Context
164k tokens
Llama Guard 4 supports the MLCommons hazard taxonomy and is designed to be used as a shield model in multi-model architectures. Not suitable as a standalone AI assistant. Available via Meta's open model ecosystem and third-party API providers.
Ministral 3B is Mistral's ultra-compact 3-billion parameter edge model designed for lightweight inference, on-device deployment, and cost-sensitive applications. It delivers surprisingly capable text understanding and generation at a fraction of the cost of larger models.
Verdict
The cheapest viable option for simple NLP tasks, but don't expect small-flagship performance.
Quality score
41%
Pricing
$0.10/1M in
$0.10/1M out
Speed
Very fast
Best for high-volume, low-latency tasks where cost and speed matter more than frontier-level reasoning.
Context
131k tokens
Priced at a flat $0.10/1M for both input and output, making cost estimation predictable. The '2512' suffix indicates a December 2025 release version. Best suited for batch processing, classification, or extraction pipelines where volume is high and task complexity is low.
3BEdgeUltra-budgetMistralLightweight
Best for
High-volume, low-latency tasks where cost and speed matter more than frontier-level reasoning.
Mistral's ultra-budget multimodal model — exceptionally cheap with vision support, built for high-volume lightweight tasks where cost is the primary constraint.
Verdict
Ultra-cheap multimodal model for massive-volume, low-complexity pipelines.
Quality score
57%
Pricing
$0.35/1M in
$0.56/1M out
Speed
Very fast
Best for ultra-high-volume classification, summarisation, and lightweight vision tasks
Context
128k tokens
At $0.10/1M input, the cost question disappears. The only question is whether the task complexity exceeds what Mistral Small can handle.
BudgetMultimodalUltra cheapMistral
Best for
Ultra-high-volume classification, summarisation, and lightweight vision tasks
Llama Guard 3 8B is a specialized safety classifier built on Meta's Llama 3 architecture, designed to detect and categorize harmful or policy-violating content in both user inputs and model outputs. It is purpose-built for content moderation pipelines, not general-purpose text generation.
Verdict
A hyper-specialized, ultra-cheap safety classifier — indispensable in the right pipeline, useless outside of it.
Quality score
14%
Pricing
$0.48/1M in
$0.03/1M out
Speed
Very fast
Best for automated content safety screening and moderation for ai application pipelines at minimal cost.
Context
131k tokens
This model is designed exclusively for content moderation and safety classification tasks. It follows the MLCommons AI Safety benchmark taxonomy. It should be deployed as a guardrail layer alongside generative models, not as a replacement for them. Not suitable for end-user-facing conversational applications.
SafetyContent ModerationClassifierBudgetMeta
Best for
Automated content safety screening and moderation for AI application pipelines at minimal cost.
Llama 3.2 1B Instruct is Meta's smallest production language model, designed for lightweight text tasks with an extremely low cost footprint. It excels at simple instruction-following, text classification, and on-device or edge deployment scenarios.
Verdict
The go-to model when cost per token matters more than output quality.
Quality score
25%
Pricing
$0.03/1M in
$0.20/1M out
Speed
Very fast
Best for ultra-low-cost text classification, simple q&a, and high-volume automation pipelines where cost per token is critical.
Context
60k tokens
Output cost of ~$0.20/1M tokens is notably higher relative to input cost — factor this in for verbose generation tasks. Best suited for inference pipelines where outputs are short and structured. Available via multiple inference providers due to open-weight licensing.
Mistral Small 3 is a compact, budget-oriented language model from Mistral AI that punches above its weight class for everyday NLP tasks. It supersedes Mistral Large 2 in efficiency while targeting cost-sensitive deployments that don't require frontier-level reasoning.
Verdict
A lean, fast, affordable workhorse for text tasks — ideal for scale, not for depth.
Quality score
55%
Pricing
$0.05/1M in
$0.08/1M out
Speed
Very fast
Best for high-volume, cost-sensitive applications like customer support automation, content drafting, and lightweight code assistance.
Context
33k tokens
Pricing is exceptionally competitive at $0.05/$0.08 per 1M tokens. Available via Mistral's La Plateforme API and various third-party providers. GDPR-friendly EU-based hosting is a notable advantage for European enterprise customers. No image input or output support.
BudgetFastMultilingualLightweightHigh-volume
Best for
High-volume, cost-sensitive applications like customer support automation, content drafting, and lightweight code assistance.
A budget-tier image-capable variant of Gemini 2.5 Flash, optimized for cost-effective multimodal tasks involving image understanding. Despite the whimsical internal name, it delivers Gemini 2.5 Flash's vision capabilities at a low price point.
Verdict
A scrappy budget image model that's fast and cheap on ingestion but constrained by a tiny context window.
Quality score
42%
Pricing
$0.30/1M in
$2.50/1M out
Speed
Very fast
Best for budget-conscious teams needing fast image analysis and visual question answering without flagship pricing.
Context
33k tokens
The 32,768 token context window is unusually small even for a budget model — verify this limit hasn't changed before deploying in production. The 'Nano Banana' name appears to be an internal or experimental identifier; confirm model availability and stability via Google AI Studio or Vertex AI before relying on it in critical workflows.
budgetimage-analysismultimodalflashgoogle
Best for
Budget-conscious teams needing fast image analysis and visual question answering without flagship pricing.
Gemini 2.5 Pro is Google's flagship reasoning-capable model with a massive 1M token context window, designed for complex analysis, coding, and multimodal tasks. It balances frontier-level intelligence with competitive mid-tier pricing.
Verdict
The best Google model for serious, complex work — especially when you need to fit an entire codebase or document corpus into a single prompt.
Quality score
87%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Balanced
Best for deep reasoning over very long documents, complex codebases, or multimodal inputs where context size is a constraint with other models.
Context
1.0M tokens
Pricing shown is for prompts under 200K tokens; inputs over 200K tokens are billed at $2.50/1M input and $15/1M output. Gemini 2.5 Pro includes built-in 'thinking' (reasoning) mode which can increase latency and cost further.
FlagshipLong ContextMultimodalReasoningGoogle
Best for
Deep reasoning over very long documents, complex codebases, or multimodal inputs where context size is a constraint with other models.
Llama 3.1 8B Instruct is Meta's smallest production-ready open-weight model, optimized for fast, low-cost inference on everyday language tasks. It delivers surprisingly capable instruction-following for its size, making it a go-to for high-volume, cost-sensitive deployments.
Verdict
The right tool for cheap, fast, high-volume tasks — not for anything that requires serious thinking.
Quality score
43%
Pricing
$0.02/1M in
$0.05/1M out
Speed
Very fast
Best for high-throughput applications where cost and speed matter more than frontier-level quality, such as chatbots, content classification, and text summarization.
Context
16k tokens
Being open-weight, this model can be run locally or self-hosted via providers like Together AI, Fireworks, or Groq, often at even lower costs. The 16K context window is a meaningful limitation compared to other models in this price tier.
Open WeightBudgetFastSelf-HostableMeta
Best for
High-throughput applications where cost and speed matter more than frontier-level quality, such as chatbots, content classification, and text summarization.
Gemma 2 9B is Google's open-weight 9-billion parameter model designed for efficient on-device and API deployment. It punches above its weight class for instruction-following and general language tasks at an exceptionally low cost.
Verdict
A capable open-weight budget model hamstrung by a frustratingly small context window.
Quality score
45%
Pricing
$0.03/1M in
$0.09/1M out
Speed
Very fast
Best for lightweight text tasks, classification, and summarization where cost matters more than frontier-level quality.
Context
8k tokens
Pricing reflects API access through third-party providers; Google also offers Gemma 2 9B weights for free download and self-hosting. The 8,192 token limit is a hard architectural constraint of this version.
Open WeightBudgetSmall ModelGoogleOn-Device
Best for
Lightweight text tasks, classification, and summarization where cost matters more than frontier-level quality.
Llama 3 8B Instruct is Meta's compact open-weight instruction-following model, optimized for efficiency and accessibility at extremely low cost. It handles everyday text tasks like summarization, Q&A, and light coding at a fraction of the price of frontier models.
Verdict
A dirt-cheap, fast open model for simple tasks — just don't expect frontier-level quality.
Quality score
39%
Pricing
$0.03/1M in
$0.04/1M out
Speed
Very fast
Best for high-volume, cost-sensitive applications where speed and price matter more than peak accuracy.
Context
8k tokens
As an open-weight model, Llama 3 8B can be self-hosted via platforms like Ollama, Replicate, or Together AI. The 8,192 token context window is a significant practical limitation. Pricing listed reflects hosted API inference; self-hosted costs vary.
Open-weightBudgetFastSelf-hostableCompact
Best for
High-volume, cost-sensitive applications where speed and price matter more than peak accuracy.
GPT-3.5 Turbo is OpenAI's legacy fast and affordable chat model, optimized for dialogue and straightforward text tasks at low cost. It was the backbone of early ChatGPT and remains a go-to for high-volume, cost-sensitive deployments.
Verdict
A once-dominant budget model now outclassed by cheaper, smarter alternatives like GPT-4o mini.
Quality score
35%
Pricing
$0.50/1M in
$1.50/1M out
Speed
Very fast
Best for high-volume, low-complexity tasks like chatbots, classification, summarization, and simple q&a where cost matters more than cutting-edge quality.
Context
16k tokens
GPT-3.5 Turbo is still available via OpenAI API and supports fine-tuning, which keeps it relevant for teams with existing trained models. However, OpenAI has deprioritized its development in favor of the GPT-4o family. Not multimodal — text only.
BudgetLegacyFastHigh-volumeChatbot
Best for
High-volume, low-complexity tasks like chatbots, classification, summarization, and simple Q&A where cost matters more than cutting-edge quality.
Mistral 7B Instruct v0.1 is a 7-billion-parameter instruction-tuned model from Mistral AI, one of the earliest open-weight models to challenge larger proprietary models on efficiency. It handles general text tasks at extremely low cost but is constrained by a very small context window of under 3K tokens.
Verdict
A historically significant but now outdated budget model crippled by an unusably small context window.
Quality score
26%
Pricing
$0.11/1M in
$0.19/1M out
Speed
Very fast
Best for ultra-low-cost simple text tasks like classification, short summarization, or lightweight chatbot responses where context length is not a concern.
Context
3k tokens
This is v0.1, the original release — not to be confused with v0.2 or v0.3 which substantially improve context length and quality. The listed context window of ~2,824 tokens is unusually small even among budget models. Marked as superseding Mistral Large 2 in the spec, which appears to be a data error — this model does not supersede Mistral Large 2 in capability or positioning.
budgetopen-weightsmall modellegacyfast
Best for
Ultra-low-cost simple text tasks like classification, short summarization, or lightweight chatbot responses where context length is not a concern.
GPT-4.1 is OpenAI's refined successor to GPT-4o, offering sharper instruction-following, stronger coding performance, and a massive 1M token context window at a mid-tier price point. It targets developers and power users who need reliable, precise outputs without paying flagship reasoning model prices.
Verdict
The sharpest everyday workhorse in OpenAI's lineup, best when you need precise instructions met over long documents or complex codebases.
Quality score
76%
Pricing
$2.00/1M in
$8.00/1M out
Speed
Balanced
Best for developers and researchers needing accurate instruction-following and long-document analysis at a cost-efficient rate.
Context
1.0M tokens
Priced at $2/1M input and $8/1M output tokens — cheaper than GPT-4o at launch. The 1M context window is real but performance near the ceiling is less tested than Gemini's equivalent. No built-in image generation or voice modality.
Long ContextInstruction-FollowingCodingBalanced PriceGPT-4 Series
Best for
Developers and researchers needing accurate instruction-following and long-document analysis at a cost-efficient rate.
An older versioned snapshot of GPT-3.5 Turbo (v0613), OpenAI's once-dominant mid-tier language model optimized for fast chat completions and instruction following. This specific checkpoint is frozen in time, predating later capability improvements introduced in subsequent GPT-3.5 Turbo updates.
Verdict
A once-useful workhorse now completely overshadowed by cheaper, more capable successors.
Quality score
31%
Pricing
$1.00/1M in
$2.00/1M out
Speed
Very fast
Best for high-volume, cost-sensitive text tasks like classification, summarization, and simple q&a where bleeding-edge quality is not required.
Context
4k tokens
This is a pinned legacy snapshot (v0613) and may eventually be deprecated by OpenAI. The 4,095-token context window is its most significant practical limitation. OpenAI's own GPT-4o mini offers drastically more context and better quality at a comparable price — strongly consider migrating.
LegacyBudgetFastShort ContextOpenAI
Best for
High-volume, cost-sensitive text tasks like classification, summarization, and simple Q&A where bleeding-edge quality is not required.
GPT-3.5 Turbo Instruct is a legacy completion-style model from OpenAI, designed for instruction-following tasks using the older text completion API rather than the chat API. It excels at structured text generation, fill-in-the-middle tasks, and traditional NLP workflows that predate the chat paradigm.
Verdict
A legacy model only worth using if your pipeline depends on the text completion API.
Quality score
30%
Pricing
$1.50/1M in
$2.00/1M out
Speed
Very fast
Best for legacy completion api workflows, structured text generation, and simple instruction-following tasks where the chat format is not required.
Context
4k tokens
Uses the legacy /v1/completions endpoint, not /v1/chat/completions. The 4,095-token context window is a hard constraint that makes it unsuitable for most modern tasks. OpenAI has not deprecated it, but it receives no capability updates.
LegacyCompletion APILow LatencyNarrow TasksOld Gen
Best for
Legacy completion API workflows, structured text generation, and simple instruction-following tasks where the chat format is not required.
Claude Sonnet 4.5 is Anthropic's mid-tier workhorse model, balancing strong reasoning and writing quality with reasonable latency at $3/$15 per million tokens. It slots above Haiku in capability while remaining more cost-accessible than Opus-tier models.
Verdict
A dependable mid-tier Claude model with a best-in-class context window, but output pricing limits its appeal for scale.
Quality score
77%
Pricing
$3.00/1M in
$15.00/1M out
Speed
Balanced
Best for production applications that need claude's nuanced writing and reasoning without the latency or cost of opus-class models.
Context
1M tokens
Supersedes Claude 4 Haiku, positioning it as a step-up option rather than a true budget model. The 1M token context window is the headline feature. Output cost of $15/1M tokens is on the higher end for this tier — compare to Gemini 3.1 Pro at roughly $10/1M output before committing to high-volume use.
GPT-5 Image Mini is OpenAI's mid-tier multimodal model optimized for image understanding and generation tasks at a balanced price point. It supersedes GPT-4o with improved visual reasoning capabilities while maintaining a large 400K context window.
Verdict
A capable multimodal workhorse for image-heavy workflows that don't justify full GPT-5 flagship pricing.
Quality score
72%
Pricing
$2.50/1M in
$2.00/1M out
Speed
Fast
Best for teams needing strong image analysis and generation integrated with text workflows at a reasonable cost.
Context
400k tokens
Output cost of $2/1M tokens is unusual — lower than input cost, which favors use cases with long inputs but short outputs like image captioning or document summarization. Verify image generation token pricing separately, as image outputs are often billed differently by OpenAI.
MultimodalImage GenerationLong ContextBalanced PriceGPT-5 Family
Best for
Teams needing strong image analysis and generation integrated with text workflows at a reasonable cost.
Gemma 4 26B A4B is a sparse mixture-of-experts open model from Google, activating only ~4B parameters per forward pass despite having 26B total parameters. It offers a 262K context window at budget pricing, making it one of the more capable open-weight models for its cost tier.
Verdict
A lean, fast, and surprisingly capable budget model best suited for high-volume text tasks where cost efficiency trumps peak quality.
Quality score
59%
Pricing
$0.13/1M in
$0.40/1M out
Speed
Fast
Best for cost-sensitive applications needing long-context processing with reasonable quality, such as document summarization pipelines or lightweight coding assistants.
Context
262k tokens
As an open-weight model, Gemma 4 26B can also be self-hosted, making API pricing largely irrelevant at scale. The 'A4B' suffix denotes the active parameter count in its MoE configuration. Listed as superseding Gemini 3 Flash Preview, though Gemini 2.0 Flash remains a stronger hosted alternative.
Open-weightBudgetMoELong ContextGoogle
Best for
Cost-sensitive applications needing long-context processing with reasonable quality, such as document summarization pipelines or lightweight coding assistants.
Gemma 4 31B is Google's open-weight instruction-tuned model offering a strong balance of capability and cost efficiency at just $0.14/$0.40 per million tokens. It features a 262K context window and is designed for developers who need capable on-premise or API-hosted inference without flagship pricing.
Verdict
A well-priced, long-context open-weight model that's ideal for high-volume developer workloads but won't match frontier models on complex reasoning.
Quality score
66%
Pricing
$0.14/1M in
$0.40/1M out
Speed
Fast
Best for cost-conscious developers needing a capable open-weight model for coding assistance, summarization, and document analysis at scale.
Context
262k tokens
As an open-weight model, Gemma 4 31B can be self-hosted via Ollama or Hugging Face in addition to Google's API. Pricing shown is for hosted inference. No image input capability confirmed at launch.
Open WeightBudgetLong ContextCodingSelf-Hostable
Best for
Cost-conscious developers needing a capable open-weight model for coding assistance, summarization, and document analysis at scale.
Mistral Small 4 is a compact, cost-efficient language model from Mistral AI that punches well above its price class, succeeding Mistral Large 2 in capability while costing a fraction of the price. It features a 256K context window and is optimized for high-throughput, latency-sensitive applications.
Verdict
The best bang-for-buck text model in its class — Mistral Large 2 quality at a fraction of the cost.
Quality score
68%
Pricing
$0.15/1M in
$0.60/1M out
Speed
Fast
Best for teams needing reliable, fast text generation and coding assistance at near-commodity pricing without sacrificing too much quality.
Context
262k tokens
Pricing at $0.15/$0.60 per million tokens makes this one of the most affordable capable models on the market. Available via Mistral's La Plateforme API and compatible with OpenAI-style endpoints. No image input support confirmed at launch.
BudgetFastLong ContextMultilingualCoding
Best for
Teams needing reliable, fast text generation and coding assistance at near-commodity pricing without sacrificing too much quality.
Devstral 2 2512 is Mistral's second-generation code-specialized model, built specifically for software development tasks with a 256K context window. It targets developers needing a cost-efficient coding assistant without sacrificing meaningful capability.
Verdict
A purpose-built coding workhorse that punches well above its price tag for development teams running high-volume or agentic pipelines.
Quality score
55%
Pricing
$0.40/1M in
$2.00/1M out
Speed
Fast
Best for budget-conscious developers who need a capable coding model for agentic workflows, code generation, and repository-scale context at a fraction of flagship pricing.
Context
262k tokens
The December 2025 (2512) release date suggests this is a recent iteration. Pricing at $0.40 input / $2.00 output is notably competitive for a code-specialist model with 256K context. Verify availability and rate limits via Mistral API or partner providers.
Code-specialistBudgetLong contextAgenticMistral
Best for
Budget-conscious developers who need a capable coding model for agentic workflows, code generation, and repository-scale context at a fraction of flagship pricing.
Codestral 2508 is Mistral's latest dedicated code model, succeeding Codestral 25.01 with improved code generation, completion, and reasoning across 80+ programming languages. It offers a massive 256K context window at a budget-friendly price point aimed squarely at developer tooling and IDE integrations.
Verdict
The most cost-effective specialized code model for production developer tooling with serious context capacity.
Quality score
53%
Pricing
$0.30/1M in
$0.90/1M out
Speed
Fast
Best for high-volume code generation, completion, and refactoring tasks where cost efficiency and long-context handling matter most.
Context
256k tokens
Available via Mistral's La Plateforme API. Also accessible through Continue.dev, Cursor, and other IDE integrations that support the Codestral endpoint. FIM (fill-in-the-middle) mode is specifically supported for autocomplete use cases. Output price rounds to ~$0.90/1M tokens.
Mistral Nemo is a compact 12B-parameter open-weight model developed in collaboration with NVIDIA, designed to deliver strong multilingual and instruction-following performance at an extremely low cost. It fits into a 128K context window and is optimized for deployment efficiency without sacrificing too much reasoning depth.
Verdict
A dirt-cheap multilingual model perfect for bulk text tasks, but don't expect frontier-level reasoning.
Quality score
55%
Pricing
$0.02/1M in
$0.03/1M out
Speed
Fast
Best for teams needing a cheap, fast, multilingual workhorse for classification, summarization, or light coding tasks at scale.
Context
131k tokens
Mistral Nemo is open-weight (Apache 2.0 license), so self-hosting is an option for teams that want to eliminate API costs entirely. Pricing via API is through Mistral's La Plateforme. The model uses a Tekken tokenizer which is more efficient than older Mistral tokenizers, especially for non-English text.
budgetmultilingualopen-weight12Befficient
Best for
Teams needing a cheap, fast, multilingual workhorse for classification, summarization, or light coding tasks at scale.
A 20-billion parameter open-weights safety-focused model from OpenAI, designed primarily for content moderation, policy enforcement, and safeguard classification tasks. It is purpose-built to detect harmful, policy-violating, or unsafe content rather than serve as a general-purpose assistant.
Verdict
A purpose-built safety classifier that's excellent at its narrow job and essentially useless outside it.
Quality score
27%
Pricing
$0.07/1M in
$0.30/1M out
Speed
Fast
Best for automated content moderation pipelines and safety classification at scale.
Context
131k tokens
This is an open-weights safety/moderation-specific model, not a general assistant. Pricing reflects its budget-tier positioning. Availability may be limited or subject to change as it appears to be a research/infrastructure model rather than a consumer product. Verify OpenAI's terms around usage and redistribution for the OSS weights.
Devstral Small 1.1 is Mistral's code-specialized small model, purpose-built for software engineering tasks including code generation, debugging, and repository-level reasoning. It succeeds Devstral Small 1.0 with improved instruction following and agentic coding capabilities at a fraction of flagship model costs.
Verdict
The best dollar-for-dollar coding model for agentic pipelines that doesn't need to do anything else.
Quality score
54%
Pricing
$0.10/1M in
$0.30/1M out
Speed
Fast
Best for developers who need a cheap, fast coding assistant for agentic workflows, code review, and multi-file repo tasks without paying flagship prices.
Context
131k tokens
Available via Mistral API and can be self-hosted via open weights. Pricing is among the lowest available for a code-specialized model. Designed to work within coding agent frameworks like SWE-agent and OpenHands.
Mistral Small 3.2 24B is a compact 24-billion parameter model from Mistral that punches well above its weight class, superseding Mistral Large 2 at a fraction of the cost. It handles coding, instruction-following, and multilingual tasks with strong efficiency for its size.
Verdict
The best budget coding model available today, offering frontier-adjacent performance at commodity pricing.
Quality score
68%
Pricing
$0.07/1M in
$0.20/1M out
Speed
Fast
Best for high-volume production workloads where cost matters but quality can't be sacrificed entirely — especially code generation and structured output tasks.
Context
128k tokens
Mistral Small 3.2 is available as an open-weight model, making it deployable on-premises or via self-hosted infrastructure — a key differentiator over GPT-4o Mini and Claude Haiku for privacy-sensitive use cases.
BudgetCodingEfficientOpen-weightMultilingual
Best for
High-volume production workloads where cost matters but quality can't be sacrificed entirely — especially code generation and structured output tasks.
Llama 3.2 11B Vision Instruct is Meta's open-weight multimodal model capable of understanding both text and images at an extremely low price point. It handles image captioning, visual question answering, and document analysis alongside standard text tasks.
Verdict
The go-to vision model when budget is the top constraint and good-enough accuracy is acceptable.
Quality score
57%
Pricing
$0.24/1M in
$0.24/1M out
Speed
Fast
Best for budget-conscious developers who need basic vision capabilities without paying premium multimodal prices.
Context
131k tokens
Available via multiple inference providers including Together AI, Fireworks, and OpenRouter. As an open-weight model, it can also be self-hosted for even lower marginal costs at scale. Part of Meta's Llama 3.2 family which also includes a 90B vision variant for heavier workloads.
Open-weightVisionBudgetMultimodalMeta
Best for
Budget-conscious developers who need basic vision capabilities without paying premium multimodal prices.
Grok 3 Mini is xAI's lightweight, budget-tier reasoning model built on the Grok 3 architecture, designed to deliver strong logical and analytical performance at a fraction of the cost of flagship models. It targets cost-sensitive workloads where reasoning quality still matters.
Verdict
A sharp budget reasoning model that earns its place when logic matters more than creativity or multimodal support.
Quality score
57%
Pricing
$0.30/1M in
$0.50/1M out
Speed
Fast
Best for developers and researchers who need solid reasoning and logic tasks at near-throwaway pricing without committing to a full flagship model.
Context
131k tokens
Pricing is highly competitive at $0.30 input / $0.50 output per million tokens. Context window is 131K tokens. No vision/image input support. xAI's API platform is newer and may have availability or rate-limit considerations compared to established providers.
BudgetReasoningLightweightLow CostxAI
Best for
Developers and researchers who need solid reasoning and logic tasks at near-throwaway pricing without committing to a full flagship model.
Grok 3 Mini Beta is xAI's lightweight reasoning-capable model designed for cost-efficient tasks that benefit from structured thinking without the full compute of Grok 3. It offers a 128K context window at sub-dollar pricing per million tokens.
Verdict
A surprisingly capable budget reasoner held back only by its beta instability.
Quality score
58%
Pricing
$0.30/1M in
$0.50/1M out
Speed
Fast
Best for budget-conscious users who need light reasoning and logical tasks without paying flagship prices.
Context
131k tokens
Model is in Beta — API behavior, rate limits, and availability may change without notice. No multimodal support confirmed. Reasoning mode may increase effective latency on complex prompts despite fast base speed.
BudgetReasoningMiniBetaxAI
Best for
Budget-conscious users who need light reasoning and logical tasks without paying flagship prices.
Open-source frontier model from DeepSeek that matches GPT-4o class performance at a fraction of the cost — the most disruptive budget option for coding and general tasks.
Verdict
GPT-4o-class coding quality at under $0.30/1M — the best value in the directory.
Quality score
71%
Pricing
$0.27/1M in
$1.10/1M out
Speed
Fast
Best for coding, reasoning, and general tasks at extreme cost efficiency
Context
128k tokens
DeepSeek V3 shocked the market on release. At this price point with this capability level, it forces a reconsideration of when premium models are actually worth it.
Open sourceBudgetCodingDeepSeek
Best for
Coding, reasoning, and general tasks at extreme cost efficiency
Meta's Llama 3.1 70B Instruct is a open-weight large language model with 70 billion parameters, fine-tuned for instruction following across coding, reasoning, and general-purpose tasks. It offers a strong balance of capability and cost at $0.40/1M tokens for both input and output.
Verdict
The go-to budget open-weight model for teams who need solid LLM capability without frontier model pricing.
Quality score
65%
Pricing
$0.40/1M in
$0.40/1M out
Speed
Fast
Best for teams needing capable open-weight llm performance at budget pricing for coding assistance, summarization, or rag pipelines.
Context
131k tokens
Pricing shown is via third-party API providers (e.g., OpenRouter, Together AI) — costs may vary. Meta releases Llama 3.1 weights publicly, enabling self-hosting at even lower cost. Not available directly from Meta as a hosted API.
Mistral Medium 3 is a mid-tier model from Mistral AI that punches above its weight class, officially superseding Mistral Large 2 while costing a fraction of the price. It targets teams needing capable multilingual and coding performance without flagship-level spend.
Verdict
The most capable budget model Mistral has shipped — a smart default for high-volume teams that need real performance without flagship pricing.
Quality score
67%
Pricing
$0.40/1M in
$2.00/1M out
Speed
Fast
Best for cost-conscious teams running high-volume coding, summarization, or multilingual tasks at enterprise scale.
Context
131k tokens
Priced at $0.40 input / $2.00 output per 1M tokens. Officially supersedes Mistral Large 2, making it an easy drop-in upgrade for existing Mistral users. Available via Mistral's API and La Plateforme.
BudgetMultilingualCodingHigh VolumeMid-Tier
Best for
Cost-conscious teams running high-volume coding, summarization, or multilingual tasks at enterprise scale.
Mistral Medium 3.1 is a multimodal mid-tier model from Mistral that supersedes Mistral Large 2, offering vision capabilities alongside strong text performance at a significantly reduced price point. It targets the sweet spot between budget models and expensive flagships, with a 128K context window and competitive multilingual support.
Verdict
The best Mistral model for budget-conscious builders who still need multimodal capability and solid multilingual output.
Quality score
70%
Pricing
$0.40/1M in
$2.00/1M out
Speed
Fast
Best for cost-sensitive teams needing solid coding, instruction-following, and basic vision tasks without paying flagship prices.
Context
131k tokens
Officially supersedes Mistral Large 2, representing a generational shift in Mistral's lineup toward multimodal capability at lower cost tiers. Available via Mistral API and select cloud providers. No function calling limitations noted at this tier.
BudgetMultimodalMultilingualMid-tierVision
Best for
Cost-sensitive teams needing solid coding, instruction-following, and basic vision tasks without paying flagship prices.
GPT Audio Mini is OpenAI's cost-efficient audio-capable model that handles real-time speech input and output alongside text, built on the GPT-4o Mini architecture. It's designed for voice-driven applications where low latency and affordable pricing matter more than peak intelligence.
Verdict
The most practical choice for cost-conscious voice application developers who need native audio I/O without compromising too much on intelligence.
Quality score
44%
Pricing
$0.60/1M in
$2.40/1M out
Speed
Fast
Best for building voice assistants, audio bots, and speech-enabled applications that need real-time audio processing at scale without breaking the budget.
Context
128k tokens
Audio tokens are priced differently from text tokens in OpenAI's API — audio input/output carries a significant premium over text tokens, so real-world costs for voice-heavy workloads will be substantially higher than the listed text token price suggests. Check OpenAI's audio token pricing separately.
AudioVoice AIReal-timeBudgetMultimodal
Best for
Building voice assistants, audio bots, and speech-enabled applications that need real-time audio processing at scale without breaking the budget.
Mistral Small Creative is a fine-tuned variant of Mistral Small optimized for creative writing tasks, offering a budget-friendly option for generative content at under $0.10/1M input tokens. It targets storytelling, copywriting, and imaginative text generation at a fraction of the cost of flagship models.
Verdict
A lean, cheap creative writing workhorse — ideal for volume content generation but not for quality-critical storytelling.
Quality score
36%
Pricing
$0.10/1M in
$0.30/1M out
Speed
Fast
Best for budget-conscious creative writing tasks like short stories, marketing copy, and brainstorming where cost matters more than peak quality.
Context
33k tokens
Context window of 32,768 tokens is notably smaller than competing budget models. Pricing is approximate ($0.10 input / $0.30 output per 1M tokens). Availability is through Mistral's API (La Plateforme) and may also be accessible via third-party providers. Confirm fine-tune scope before deploying for non-creative tasks.
Creative WritingBudgetFastShort-formMistral
Best for
Budget-conscious creative writing tasks like short stories, marketing copy, and brainstorming where cost matters more than peak quality.
Voxtral Small 24B is Mistral's audio-capable language model, designed for speech transcription, voice understanding, and spoken language tasks at a budget-friendly price point. It supersedes Mistral Small 3.1 with native audio input support built on a 24B parameter base.
Verdict
A purpose-built budget audio model that excels at voice tasks but stumbles on context length and general-purpose depth.
Quality score
47%
Pricing
$0.10/1M in
$0.30/1M out
Speed
Fast
Best for transcribing, analyzing, and responding to audio input cost-effectively without needing a separate speech-to-text pipeline.
Context
32k tokens
Voxtral Small is audio-in capable but does not support image input. The 32K context window is notably short for a 2025 model. Pricing is via Mistral's API; availability through third-party providers may vary. Check whether your use case requires audio input — the text-only version of Mistral Small 3.1 may be more appropriate for pure text workloads.
Audio AIBudgetMultilingualSpeechMistral
Best for
Transcribing, analyzing, and responding to audio input cost-effectively without needing a separate speech-to-text pipeline.
Mistral Saba is a compact, budget-oriented language model from Mistral designed for efficient text tasks with a focus on Arabic and South Asian languages alongside English. It targets cost-sensitive deployments where multilingual support is more important than raw reasoning depth.
Verdict
A bargain multilingual model built for Arabic and South Asian languages, but too constrained for demanding workloads.
Quality score
45%
Pricing
$0.20/1M in
$0.60/1M out
Speed
Fast
Best for low-cost multilingual applications requiring arabic, hindi, or urdu language support
Context
33k tokens
Pricing reflects Mistral API rates and may vary by reseller. The model's name 'Saba' references Arabic linguistic heritage, signaling its intended multilingual focus. No vision or tool-use capabilities documented at launch.
BudgetMultilingualArabicCompactEfficient
Best for
Low-cost multilingual applications requiring Arabic, Hindi, or Urdu language support
Mixtral 8x7B Instruct is Mistral's sparse mixture-of-experts model that routes tokens through 2 of 8 expert networks, achieving strong performance while activating only ~13B parameters per forward pass. It excels at instruction-following, multilingual tasks, and code generation at a competitive price point.
Verdict
A historically significant open-weight model that's been surpassed by newer alternatives but still earns its place in self-hosted and multilingual pipelines.
Quality score
53%
Pricing
$0.54/1M in
$0.54/1M out
Speed
Fast
Best for developers and teams needing a capable open-weight model for coding, multilingual tasks, and general instruction-following without flagship model pricing.
Context
33k tokens
Pricing is symmetric at $0.54/1M for both input and output. As an open-weight model, costs can drop significantly if self-hosted. The 32K context window is a hard ceiling — plan accordingly for document-heavy workflows.
Developers and teams needing a capable open-weight model for coding, multilingual tasks, and general instruction-following without flagship model pricing.
Gemini 2.5 Pro Preview 05-06 is Google's latest frontier reasoning model featuring a massive 1M token context window and strong multimodal capabilities. It targets developers and researchers needing deep analytical power with competitive pricing relative to its capability tier.
Verdict
The go-to model when you need a frontier brain and a million-token memory, at a price that won't immediately break your budget.
Quality score
86%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Deliberate
Best for complex multi-document analysis, long-context reasoning, and advanced coding tasks where a massive context window is essential.
Context
1.0M tokens
This is a preview model (05-06 date suffix indicates a versioned snapshot); Google may deprecate or change it without long notice. Confirm production readiness before building critical pipelines on this endpoint. The 1M context window applies to text and multimodal inputs combined.
Long ContextReasoningMultimodalFrontierPreview
Best for
Complex multi-document analysis, long-context reasoning, and advanced coding tasks where a massive context window is essential.
Gemini 2.5 Pro Preview 06-05 is Google's most capable reasoning-focused model, featuring a massive 1M token context window and strong performance across code, math, and complex analysis tasks. It represents Google's top-tier offering in the Gemini 2.5 generation, optimized for depth over speed.
Verdict
Google's most capable model — a top-tier reasoning and coding powerhouse with an unmatched context window, held back only by its preview status and output cost.
Quality score
83%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Deliberate
Best for complex multi-step reasoning, large codebase analysis, and tasks requiring deep synthesis across very long documents.
Context
1.0M tokens
This is a preview model (06-05 date suffix indicates a versioned snapshot); Google may deprecate or modify it before a stable GA release. Pricing tiers differ based on prompt length — prompts over 200K tokens are charged at $2.50/1M input and $15/1M output, significantly increasing cost for very long-context use cases.
FlagshipLong ContextReasoningCodingPreview
Best for
Complex multi-step reasoning, large codebase analysis, and tasks requiring deep synthesis across very long documents.
Gemma 2 27B is Google's largest open-weight model in the Gemma 2 family, designed for high-quality text generation, reasoning, and instruction-following at a mid-range price point. It punches above its weight class for an open model, rivaling some proprietary mid-tier offerings.
Verdict
A strong open-weight performer for short-context coding and reasoning, hobbled by an outdated 8K context limit.
Quality score
55%
Pricing
$0.65/1M in
$0.65/1M out
Speed
Fast
Best for teams that need strong open-weight model performance for coding and reasoning tasks without paying flagship prices.
Context
8k tokens
Symmetric input/output pricing at $0.65/1M tokens is straightforward but positions it oddly — it's pricier than GPT-4o Mini while lacking its multimodal features. Available via multiple inference providers including Google Vertex AI and third-party APIs.
Open WeightMid-RangeText OnlyCodingInstruction Following
Best for
Teams that need strong open-weight model performance for coding and reasoning tasks without paying flagship prices.
GPT-3.5 Turbo 16k is OpenAI's extended-context variant of their older flagship chat model, offering double the context window of the base 3.5 Turbo at a higher price point. It handles general-purpose text tasks but has been largely superseded by newer, more capable models.
Verdict
An outdated model that's been lapped by cheaper, more capable competitors on every meaningful dimension.
Quality score
37%
Pricing
$3.00/1M in
$4.00/1M out
Speed
Fast
Best for legacy integrations or applications that need slightly longer documents processed without upgrading to a modern model.
Context
16k tokens
OpenAI has been gradually deprecating older GPT-3.5 variants. Availability may be limited or sunset in the future. At $3/$4 per million tokens, this is dramatically overpriced relative to its capability in 2024-2025.
LegacyExtended ContextGeneral PurposeAffordable
Best for
Legacy integrations or applications that need slightly longer documents processed without upgrading to a modern model.
GPT-5 is OpenAI's flagship multimodal model, superseding GPT-4o with significantly improved reasoning, instruction-following, and knowledge breadth. It handles text, images, and complex multi-step tasks with state-of-the-art performance across most benchmarks.
Verdict
OpenAI's best general-purpose model — a strong flagship pick that punches above its price on input costs while delivering top-tier reasoning and multimodal capability.
Quality score
87%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Balanced
Best for high-stakes professional tasks requiring deep reasoning, precise instruction-following, and reliable multimodal understanding.
Context
400k tokens
Pricing is asymmetric: cheap on input ($1.25/1M) but expensive on output ($10/1M), so it favors read-heavy or summarization tasks over verbose generation. The 400K context window is one of the largest available at this price tier. Supersedes GPT-4o, which remains available at lower cost for lighter workloads.
FlagshipMultimodalLong ContextOpenAIReasoning
Best for
High-stakes professional tasks requiring deep reasoning, precise instruction-following, and reliable multimodal understanding.
GPT-5 Codex is OpenAI's specialized coding-focused evolution of GPT-5, designed for software development tasks with a massive 400K context window for handling large codebases. It bridges the gap between raw language capability and developer-specific tooling, succeeding GPT-4o as OpenAI's primary coding workhorse.
Verdict
A serious coding model with repository-scale context that earns its place in any developer's toolkit.
Quality score
68%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Balanced
Best for professional developers who need to reason across large codebases, generate production-ready code, and debug complex multi-file projects.
Context
400k tokens
The $10/1M output cost means heavy code generation workloads can get expensive fast — budget carefully for bulk generation use cases. Context window of 400K is among the largest in its price tier. Supersedes GPT-4o, so existing GPT-4o coding workflows should consider migrating for improved performance.
GPT-5.1 is OpenAI's mid-tier flagship model, succeeding GPT-4o with improved reasoning, instruction-following, and a 400K context window at a competitive price point. It sits between GPT-4o and full GPT-5 in capability and cost.
Verdict
A solid, practical upgrade over GPT-4o that hits the sweet spot between capability and cost — but not the best in any single category.
Quality score
76%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Balanced
Best for teams needing reliable, high-quality outputs across coding, writing, and analysis without paying premium gpt-5 prices.
Context
400k tokens
Pricing structure heavily favors input-heavy use cases like RAG and retrieval. The $10/1M output cost makes it expensive for long-form generation at scale. Context window of 400K is competitive but not best-in-class against Gemini 3.1 Pro's 1M+ window.
GPT-5.1-Codex is OpenAI's coding-specialized flagship model, purpose-built for software development tasks with a massive 400K context window. It supersedes GPT-4o with deeper code comprehension, multi-file reasoning, and tighter integration with developer workflows.
Verdict
The go-to model for large-codebase engineering tasks, but expensive output costs limit its appeal for high-throughput pipelines.
Quality score
70%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Balanced
Best for professional software engineers who need a high-capacity model for large codebase analysis, complex refactoring, and multi-file code generation.
Context
400k tokens
Asymmetric pricing ($1.25 input / $10 output) rewards read-heavy workflows like code review and repo analysis over generation-heavy tasks. The 400K context window is among the largest in the balanced price tier. No image input/output support confirmed at launch.
CodingLarge ContextDeveloperOpenAIFlagship
Best for
Professional software engineers who need a high-capacity model for large codebase analysis, complex refactoring, and multi-file code generation.
GPT-5.1-Codex-Max is OpenAI's specialized coding-focused flagship model, built on the GPT-5 architecture with deep optimization for software development, code generation, and technical problem-solving. It supersedes GPT-4o with significantly improved code comprehension and a 400K context window suited for large codebases.
Verdict
The strongest choice for serious software engineering work, provided you can absorb the output-side pricing.
Quality score
70%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Balanced
Best for professional developers and engineering teams working with complex, multi-file codebases who need accurate code generation, debugging, and architectural reasoning.
Context
400k tokens
Output cost of $10/1M tokens is the key budget consideration — input is competitively priced but output costs mirror GPT-4 Turbo-tier pricing. Best paired with a cheaper model for lightweight or repetitive coding subtasks. Context window of 400K is well-suited to monorepo analysis but verify token limits on your deployment tier.
CodingLarge ContextOpenAITechnicalFlagship
Best for
Professional developers and engineering teams working with complex, multi-file codebases who need accurate code generation, debugging, and architectural reasoning.
GPT-5.3-Codex is OpenAI's specialized coding-focused model in the GPT-5 lineage, built for deep software engineering tasks including code generation, debugging, and repository-level reasoning. It succeeds GPT-5.2 with improved instruction-following for complex multi-file codebases and a significantly expanded 400K context window.
Verdict
The go-to model for large-codebase reasoning, but its output pricing makes it a considered rather than casual choice.
Quality score
65%
Pricing
$1.75/1M in
$14.00/1M out
Speed
Balanced
Best for professional developers tackling large-scale coding tasks, refactoring legacy codebases, or working across multi-file projects where deep context retention is critical.
Context
400k tokens
Priced asymmetrically with low input cost ($1.75/1M) and high output cost ($14/1M), which rewards concise prompting but penalizes verbose code generation. The 400K context window is one of the largest available at this price tier. Supersedes GPT-5.2 with improved multi-file coherence; users on GPT-5.2 should migrate. No multimodal input support confirmed at launch.
Professional developers tackling large-scale coding tasks, refactoring legacy codebases, or working across multi-file projects where deep context retention is critical.
OpenAI's latest agentic flagship for coding, research, computer-use workflows, and long multi-step knowledge work.
Verdict
Best OpenAI flagship for agentic coding, research, and computer-use work.
Quality score
92%
Pricing
$30.00/1M in
$180.00/1M out
Speed
Balanced
Best for agentic coding, computer-use workflows, and complex research tasks
Context
1M tokens
Ranked from public benchmark and pricing data verified April 26, 2026: SWE-Bench Pro 58.6%, Terminal-Bench 2.0 82.7%, $5/$30 per 1M tokens, 1M API context.
AgenticCodingComputer useLong contextPremium
Best for
Agentic coding, computer-use workflows, and complex research tasks
Mistral Large 3 2512 is Mistral's flagship dense model updated in December 2025, offering strong multilingual reasoning and coding capabilities at a significantly reduced price point compared to its predecessor. It targets enterprise workloads that need high-quality outputs without paying top-tier frontier model prices.
Verdict
The best price-per-quality ratio in the non-mini flagship tier, especially for multilingual and long-context enterprise tasks.
Quality score
69%
Pricing
$0.50/1M in
$1.50/1M out
Speed
Balanced
Best for multilingual enterprise tasks, code generation, and long-document analysis where cost efficiency matters more than absolute state-of-the-art performance.
Context
262k tokens
Pricing of $0.50 input / $1.50 output per 1M tokens places it firmly in the budget-flagship category. Available via Mistral API (La Plateforme) and major cloud providers. December 2025 update ('2512') improves instruction following over the earlier 2407 release.
Multilingual enterprise tasks, code generation, and long-document analysis where cost efficiency matters more than absolute state-of-the-art performance.
GPT-5 Image is OpenAI's multimodal flagship optimized for deep visual understanding and generation tasks, built on the GPT-5 architecture with a 400K context window. It supersedes GPT-4o with significantly improved image reasoning, analysis, and generation capabilities.
Verdict
OpenAI's most capable eye for visuals, but you'll pay a premium over equally capable rivals.
Quality score
79%
Pricing
$10.00/1M in
$10.00/1M out
Speed
Balanced
Best for complex workflows combining visual analysis, image generation, and long-document understanding in a single model call.
Context
400k tokens
Flat $10/1M input and output pricing is unusual — most flagship models charge more for output tokens. Verify whether image token costs (typically higher per effective token) are included under this pricing or billed separately, as OpenAI historically charges additional fees for image inputs.
MultimodalImage AILong ContextOpenAIPremium
Best for
Complex workflows combining visual analysis, image generation, and long-document understanding in a single model call.
Claude Sonnet 4 is Anthropic's mid-tier flagship model balancing strong reasoning, coding, and writing capabilities at a competitive price point. It sits between Haiku and Opus in Anthropic's lineup, offering substantive intelligence without the cost of top-tier models.
Verdict
The sweet spot in Anthropic's lineup for serious coding and writing work — strong enough to replace Opus 4 in most real-world tasks.
Quality score
80%
Pricing
$3.00/1M in
$15.00/1M out
Speed
Balanced
Best for complex coding tasks, nuanced writing, and multi-step research where you need near-flagship quality without paying flagship prices.
Context
200k tokens
Pricing at $3 input / $15 output positions this as a 'balanced' tier model, but output costs are notably higher than comparable models like GPT-4o ($10 output). Extended context (200K) is available by default. Check Anthropic's API for rate limits and availability by tier.
Mid-tierCodingLong ContextAnthropicBalanced
Best for
Complex coding tasks, nuanced writing, and multi-step research where you need near-flagship quality without paying flagship prices.
Devstral Medium is Mistral's code-focused model optimized for software development tasks, offering strong code generation and debugging capabilities at a budget-friendly price point. It targets developers who need reliable coding assistance without paying flagship model rates.
Verdict
A genuinely specialized, budget-friendly coding model that earns its place in any developer's API toolkit.
Quality score
60%
Pricing
$0.40/1M in
$2.00/1M out
Speed
Balanced
Best for developers seeking capable code generation, debugging, and code review at a fraction of the cost of gpt-4-class models.
Context
131k tokens
Pricing is notably aggressive at ~$0.40 input / $2.00 output per 1M tokens. Available via Mistral's La Plateforme API. Part of the Devstral family, which is distinct from Mistral's general-purpose Mistral Medium line.
GPT-5 Chat is OpenAI's flagship conversational model, succeeding GPT-4o with improved reasoning, instruction-following, and multimodal capabilities. It targets professional and enterprise use cases where output quality matters more than cost.
Verdict
A polished, capable flagship that earns its place but faces stiff competition at its price point.
Quality score
75%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Balanced
Best for complex professional tasks requiring nuanced reasoning, strong writing quality, and reliable instruction-following across long conversations.
Context
128k tokens
Pricing is asymmetric — input is relatively affordable at $1.25/1M but output at $10/1M can accumulate quickly in agentic or verbose-output workflows. Cached input pricing may apply through the OpenAI API. Not to be confused with GPT-5 reasoning variants (o-series) which use chain-of-thought and have separate pricing.
FlagshipMultimodalOpenAIProfessionalGPT-5
Best for
Complex professional tasks requiring nuanced reasoning, strong writing quality, and reliable instruction-following across long conversations.
GPT-5.1 Chat is OpenAI's mid-tier conversational model, positioned as a capable successor to GPT-4o with improved instruction-following, reasoning, and knowledge depth at a balanced price point.
Verdict
A reliable mid-tier upgrade over GPT-4o for instruction-heavy tasks, but the context window and output pricing limit its value against Sonnet-class competitors.
Quality score
67%
Pricing
$1.25/1M in
$10.00/1M out
Speed
Balanced
Best for teams and developers who need gpt-4o-level quality with incremental improvements in accuracy and instruction adherence without paying flagship model prices.
Context
128k tokens
Output cost of $10/1M tokens is asymmetric compared to the $1.25 input price — high-volume generation tasks will become expensive quickly. No vision or image generation confirmed based on available specs. Supersedes GPT-4o in the OpenAI lineup but does not replace o-series reasoning models.
Teams and developers who need GPT-4o-level quality with incremental improvements in accuracy and instruction adherence without paying flagship model prices.
GPT-5.3 Chat is OpenAI's mid-cycle refinement of the GPT-5 series, offering improved instruction-following and reasoning over GPT-5.2 at a balanced price point. It targets professionals needing strong general-purpose performance without paying flagship model premiums.
Verdict
A solid GPT-5 series refinement with strong reasoning, but its output pricing makes it hard to recommend over Claude Sonnet 4.6 unless you're OpenAI-first.
Quality score
71%
Pricing
$1.75/1M in
$14.00/1M out
Speed
Balanced
Best for professionals and developers who need reliable, high-quality text generation and reasoning at a cost that scales reasonably with usage.
Context
128k tokens
Output cost of $14/1M tokens is the primary budget consideration — workloads with high output-to-input ratios will accumulate costs quickly. No image generation capability. Supersedes GPT-5.2, which should be deprecated or deprioritized.
Pixtral Large 2411 is Mistral's flagship multimodal model, adding native image understanding to the Mistral Large 2 foundation. It processes both text and images with strong reasoning across documents, charts, and visual content.
Verdict
A capable and fairly priced multimodal flagship, best suited for Mistral ecosystem users and European compliance requirements.
Quality score
74%
Pricing
$2.00/1M in
$6.00/1M out
Speed
Balanced
Best for teams needing a capable european-hosted multimodal model for document analysis, visual qa, and code generation with image context.
Context
131k tokens
Available via Mistral API (la Plateforme) and supports self-hosted deployment. The '2411' suffix indicates a November 2024 release. Supersedes Mistral Large 2 as the primary flagship. Image input pricing follows the same $2/1M token rate.
Balanced enterprise model with consistent reasoning, good speed, and a dependable middle-ground — especially for European teams with data residency requirements.
Verdict
Best balanced generalist for EU teams with data residency needs.
Quality score
67%
Pricing
$2.00/1M in
$6.00/1M out
Speed
Balanced
Best for balanced team usage with eu data residency requirements
Context
128k tokens
The EU hosting angle is the real differentiator here — for teams outside Europe, other models perform better.
EU hostingBalancedTeam default
Best for
Balanced team usage with EU data residency requirements
GPT Audio is OpenAI's speech-capable model variant optimized for real-time audio input and output, enabling natural voice conversations and audio processing. It extends GPT-4o's multimodal capabilities with native audio understanding and generation without requiring separate transcription pipelines.
Verdict
The go-to choice for native voice AI applications, but overkill and potentially costly for anything without real audio requirements.
Quality score
43%
Pricing
$2.50/1M in
$10.00/1M out
Speed
Balanced
Best for building voice assistants, real-time spoken dialogue systems, and applications that need to process or generate natural speech end-to-end.
Context
128k tokens
Audio tokens are counted differently from text tokens — a few seconds of audio can consume hundreds of tokens, so monitor usage carefully. Real-time audio streaming requires WebSocket or Realtime API endpoints, not the standard Chat Completions API. Availability may be limited by tier or region.
Voice AIAudioMultimodalReal-timeSpeech
Best for
Building voice assistants, real-time spoken dialogue systems, and applications that need to process or generate natural speech end-to-end.
Grok 3 is xAI's flagship large language model, trained on a massive dataset including real-time X (Twitter) data and designed for advanced reasoning, coding, and research tasks. It competes directly with GPT-4o and Claude Sonnet 4 at a similar price point.
Verdict
A strong STEM-focused flagship with unique real-time X data access, but priced high for what it delivers versus Claude Sonnet 4 and GPT-4o.
Quality score
68%
Pricing
$3.00/1M in
$15.00/1M out
Speed
Balanced
Best for users who need strong reasoning and coding capabilities with access to real-time x/twitter data for current events and social context.
Context
131k tokens
Available via xAI API and integrated into X Premium subscriptions. Real-time X data access is a differentiating feature not available on competing models. Pricing is competitive but output costs are on the higher end for balanced-tier models.
FlagshipSTEMReal-time dataReasoningxAI
Best for
Users who need strong reasoning and coding capabilities with access to real-time X/Twitter data for current events and social context.
Grok 3 Beta is xAI's flagship large language model, trained on a massive dataset with claimed real-time access to X (Twitter) data and strong reasoning capabilities. It competes directly with frontier models like Claude Sonnet 4 and GPT-4o across coding, analysis, and general tasks.
Verdict
A powerful but unproven flagship that earns its place for STEM and real-time social data use cases, but the beta tag means it's not yet ready to dethrone Anthropic or OpenAI at this price.
Quality score
71%
Pricing
$3.00/1M in
$15.00/1M out
Speed
Balanced
Best for users who want a frontier-capable model with real-time social context from x and strong stem reasoning at a mid-range price point.
Context
131k tokens
Model is currently in beta, meaning capabilities and pricing may change. Real-time X data integration depends on xAI's API access policies, which may be subject to change. No image generation support confirmed.
FrontierSTEMReal-timexAIBeta
Best for
Users who want a frontier-capable model with real-time social context from X and strong STEM reasoning at a mid-range price point.
Claude 3.5 Sonnet is Anthropic's mid-cycle flagship model, balancing strong reasoning, coding, and instruction-following with a 200K context window. It sits between Haiku and Opus in Anthropic's lineup, offering near-flagship quality at a lower cost than top-tier models.
Verdict
One of the best models for coding and complex instruction-following, but its premium pricing demands premium use cases.
Quality score
81%
Pricing
$6.00/1M in
$30.00/1M out
Speed
Balanced
Best for complex coding tasks, multi-step reasoning, and long-document analysis where gpt-4o-class quality is needed without paying for the absolute top tier.
Context
200k tokens
Pricing at $6 input / $30 output per million tokens is significantly higher than GPT-4o ($2.50/$10). Best accessed via Anthropic API or Amazon Bedrock. Claude 3.5 Sonnet (October 2024 version) supersedes the June 2024 release with improved performance.
Google: Nano Banana Pro (Gemini 3 Pro Image Preview)
Gemini 3 Pro Image Preview is Google's image-focused multimodal model designed for advanced visual understanding and generation tasks. It sits in the balanced price tier, targeting professional workflows that require strong image comprehension alongside text reasoning.
Verdict
A capable image-first multimodal model held back by a small context window and preview-stage instability.
Quality score
64%
Pricing
$2.00/1M in
$12.00/1M out
Speed
Balanced
Best for teams needing robust image analysis, visual question answering, and multimodal workflows at a mid-range price point.
Context
66k tokens
This is a preview model — API behavior, pricing, and availability may change before general release. The 65K context window is unusually constrained for a Gemini Pro-tier model; double-check if your use case requires longer contexts before committing.
VisionMultimodalGooglePreviewImage Analysis
Best for
Teams needing robust image analysis, visual question answering, and multimodal workflows at a mid-range price point.
Mixtral 8x22B Instruct is Mistral's flagship sparse mixture-of-experts model, routing tokens through 2 of 8 expert networks (39B active parameters out of 141B total) for efficient high-quality inference. It excels at multilingual tasks, code generation, and instruction-following with strong European language support.
Verdict
A capable MoE workhorse with strong multilingual chops, but its short context window and rising competition have eroded its value proposition.
Quality score
59%
Pricing
$2.00/1M in
$6.00/1M out
Speed
Balanced
Best for teams needing strong multilingual capabilities and solid coding performance at a mid-tier price point without relying on openai or anthropic infrastructure.
Context
66k tokens
Available via Mistral API and as open weights (Apache 2.0 license) for self-hosting. The open-weight option is a key differentiator for privacy-sensitive or on-premise deployments. API pricing at $2/$6 per million tokens is mid-range but faces pressure from newer, cheaper alternatives.
MoEMultilingualOpen-weightMid-tierInstruct
Best for
Teams needing strong multilingual capabilities and solid coding performance at a mid-tier price point without relying on OpenAI or Anthropic infrastructure.
Meta's Llama 3 70B Instruct is a 70-billion parameter open-weight language model fine-tuned for instruction following, representing Meta's most capable publicly available model at the time of release. It excels at general reasoning, coding assistance, and structured text tasks with strong multilingual support.
Verdict
A capable but now-outdated open-weight model undercut by its tiny context window and newer successors.
Quality score
53%
Pricing
$0.51/1M in
$0.74/1M out
Speed
Balanced
Best for developers and researchers who need a capable open-weight model for coding, analysis, and instruction-following tasks at a mid-range price point.
Context
8k tokens
This is the original Llama 3 70B, not the 3.1 or 3.3 variants. Llama 3.1 70B offers a 128K context window at comparable pricing and is strongly preferred. Consider this model only if you have a specific reason to pin to the original Llama 3 checkpoint.
Open-weightInstruction-tunedMid-rangeMetaLlama 3
Best for
Developers and researchers who need a capable open-weight model for coding, analysis, and instruction-following tasks at a mid-range price point.
GPT-4 Turbo is OpenAI's high-capability flagship model featuring a 128K context window, trained on data up to April 2024. It delivers strong reasoning, coding, and instruction-following across complex tasks.
Verdict
A capable but aging flagship that has been outpaced by cheaper, faster successors in OpenAI's own lineup.
Quality score
75%
Pricing
$10.00/1M in
$30.00/1M out
Speed
Balanced
Best for complex multi-step tasks requiring deep reasoning, long document analysis, or sophisticated code generation where cost is secondary to quality.
Context
128k tokens
GPT-4 Turbo is available via the OpenAI API. It has largely been succeeded by GPT-4o, which is faster, supports vision natively, and is cheaper. Organizations should evaluate whether migrating to GPT-4o or o3 makes more sense before building new workflows on this model.
GPT-4 Turbo (v1106) is an older snapshot of OpenAI's flagship GPT-4 Turbo model released in November 2023, offering a 128K context window with strong general-purpose reasoning and instruction-following capabilities. It predates later GPT-4 Turbo updates and GPT-4o, making it a legacy choice for workflows locked to this specific version.
Verdict
A reliable but outdated GPT-4 snapshot that only makes sense when version pinning is a hard requirement.
Quality score
66%
Pricing
$10.00/1M in
$30.00/1M out
Speed
Balanced
Best for teams requiring a pinned, stable version of gpt-4 turbo for reproducible outputs in long-document analysis or complex instruction pipelines.
Context
128k tokens
This is a pinned model snapshot (v1106) and will not receive capability updates. OpenAI may deprecate older snapshots over time. Knowledge cutoff is April 2023. Not recommended for new deployments given the superior cost-performance of GPT-4o and GPT-4.1.
Legacy128K ContextPinned SnapshotGPT-4Premium
Best for
Teams requiring a pinned, stable version of GPT-4 Turbo for reproducible outputs in long-document analysis or complex instruction pipelines.
GPT-4 Turbo Preview is an early access version of GPT-4 Turbo, OpenAI's then-flagship model featuring a 128K context window and knowledge improvements over the original GPT-4. It was designed to deliver GPT-4-class reasoning at reduced cost compared to the original GPT-4.
Verdict
A once-capable flagship now overshadowed by faster, cheaper, and smarter successors.
Quality score
67%
Pricing
$10.00/1M in
$30.00/1M out
Speed
Balanced
Best for complex multi-step reasoning, long-document analysis, and professional writing tasks requiring strong instruction-following.
Context
128k tokens
This is a 'preview' variant that OpenAI has largely deprecated in favor of gpt-4-turbo and gpt-4o. The endpoint may be retired or redirected by OpenAI without notice. Check the OpenAI model deprecation schedule before building production applications on this model.
GPT-4Long ContextLegacyPremiumOpenAI
Best for
Complex multi-step reasoning, long-document analysis, and professional writing tasks requiring strong instruction-following.
OpenAI's o3 Mini is a compact reasoning model optimized for STEM tasks, offering chain-of-thought capabilities at a fraction of the cost of o3. It excels at math, coding, and logical problem-solving while maintaining a large 200K context window.
Verdict
The most cost-efficient way to access serious chain-of-thought reasoning for STEM and coding work.
Quality score
68%
Pricing
$1.10/1M in
$4.40/1M out
Speed
Deliberate
Best for cost-effective deep reasoning on math, code, and structured logic problems where o3's full price isn't justified.
Context
200k tokens
Supports three reasoning effort settings via the API (low, medium, high), which significantly affect latency and token usage. No vision/image input support. Available via OpenAI API and ChatGPT Plus.
o4 Mini is OpenAI's compact reasoning model that applies chain-of-thought thinking to complex problems at a fraction of the cost of o4. It delivers strong mathematical, coding, and logical reasoning capabilities while remaining accessible to developers on tighter budgets.
Verdict
The most cost-efficient reasoning model for serious STEM and coding workloads.
Quality score
70%
Pricing
$1.10/1M in
$4.40/1M out
Speed
Deliberate
Best for developers and analysts who need serious reasoning power for stem tasks without paying full o4 or o3 prices.
Context
200k tokens
Priced at $1.1/$4.4 per 1M tokens (input/output), o4 Mini is significantly cheaper than o3 ($10/$40) and o4. Output tokens are 4x the input price, so verbose reasoning traces can add up — use max_completion_tokens limits in production pipelines.
ReasoningSTEMBudget-FriendlyLong ContextCoding
Best for
Developers and analysts who need serious reasoning power for STEM tasks without paying full o4 or o3 prices.
o4 Mini High is OpenAI's compact reasoning model running at its maximum reasoning effort setting, trading speed for deeper multi-step logical analysis. It applies extended chain-of-thought processing to complex problems while remaining significantly cheaper than full o3 or o4 class flagships.
Verdict
Maximum-effort reasoning at mid-tier pricing — excellent for hard problems, overkill for everything else.
Quality score
70%
Pricing
$1.10/1M in
$4.40/1M out
Speed
Deliberate
Best for developers and researchers who need strong reasoning accuracy on hard stem, math, or logic problems without paying full o3 pricing.
Context
200k tokens
The 'High' suffix denotes maximum reasoning effort, distinct from o4 Mini (balanced) and o4 Mini Low. Higher effort means higher token consumption in internal reasoning traces, which can push effective cost above the stated $1.1/$4.4 per million for very complex queries. No image generation capability.
reasoningSTEMcost-efficientlong-contextcoding
Best for
Developers and researchers who need strong reasoning accuracy on hard STEM, math, or logic problems without paying full o3 pricing.
o4 Mini Deep Research is OpenAI's cost-efficient reasoning model specialized for autonomous multi-step research tasks, capable of browsing the web, synthesizing sources, and producing detailed research reports. It brings deep research capabilities to a mid-tier price point by trading some of o4's raw power for significantly lower inference costs.
Verdict
The pragmatic choice for automated deep research at scale — capable enough, priced right, but don't expect o4-level depth.
Quality score
61%
Pricing
$2.00/1M in
$8.00/1M out
Speed
Deliberate
Best for automated research pipelines that require web browsing, source synthesis, and structured report generation at scale without flagship-model costs.
Context
200k tokens
Deep Research mode requires agentic tool access (web browsing); pricing reflects token usage but research tasks can consume significant tokens across multi-step retrieval loops. Availability may depend on API tier or organizational access level. Not a drop-in replacement for the standard o4 Mini in general-purpose workflows.
Deep ResearchReasoningWeb BrowsingCost-EfficientLong Context
Best for
Automated research pipelines that require web browsing, source synthesis, and structured report generation at scale without flagship-model costs.
Claude 3.7 Sonnet with extended thinking enabled — Anthropic's hybrid reasoning model that explicitly deliberates before responding, surfacing its chain-of-thought for complex multi-step problems. It sits between standard Sonnet and full reasoning-only models, balancing depth with practical usability.
Verdict
The most transparent reasoning model on the market — ideal when you need to see and trust the thought process, not just the answer.
Quality score
73%
Pricing
$3.00/1M in
$15.00/1M out
Speed
Deliberate
Best for tackling complex coding challenges, mathematical proofs, and multi-step logical problems where visible reasoning and higher accuracy matter more than speed.
Context
200k tokens
Thinking tokens (the internal reasoning trace) count toward output token billing, which can significantly increase costs on complex queries. The thinking budget can often be configured via the API. Best used selectively for tasks that genuinely benefit from deliberation rather than as a default model.
ReasoningExtended ThinkingCodingAgenticAnthropic
Best for
Tackling complex coding challenges, mathematical proofs, and multi-step logical problems where visible reasoning and higher accuracy matter more than speed.
Claude Opus 4.5 is Anthropic's flagship reasoning and writing model, offering deep analytical capability and nuanced instruction-following across a 200K context window. It sits at the top of the Claude 4 lineup, prioritizing quality over speed.
Verdict
Anthropic's most capable model delivers best-in-class reasoning and writing quality, but the steep output cost demands genuinely complex use cases to justify it.
Quality score
82%
Pricing
$5.00/1M in
$25.00/1M out
Speed
Deliberate
Best for complex multi-step reasoning, long-document analysis, and high-stakes writing tasks where output quality is non-negotiable.
Context
200k tokens
Pricing is $5 input / $25 output per 1M tokens — identical output cost to GPT-5.4 tier models. Note the 'Supersedes Claude 4 Haiku' label appears to be a data anomaly; Opus 4.5 is the top-tier model, not a Haiku replacement. Confirm model availability on the Anthropic API dashboard as Opus-tier models sometimes have access restrictions.
GPT-5 Pro is OpenAI's most capable flagship model, designed for complex reasoning, advanced coding, and high-stakes professional tasks. It supersedes GPT-4o with substantially improved intelligence at a premium price point reflecting its top-tier positioning.
Verdict
The most capable model OpenAI offers, but the steep output cost means it's only justifiable for genuinely high-stakes, complex tasks.
Quality score
84%
Pricing
$15.00/1M in
$120.00/1M out
Speed
Deliberate
Best for demanding professional workflows requiring deep reasoning, nuanced writing, and sophisticated multi-step problem solving where cost is secondary to quality.
Context
400k tokens
Output cost of $120/1M tokens is exceptionally high and will compound quickly in agentic or multi-turn workflows. Budget carefully. Context window of 400K is generous but falls short of Gemini 3.1 Pro's 1M+ offering for ultra-long document tasks.
FlagshipPremiumDeep ReasoningLong ContextOpenAI
Best for
Demanding professional workflows requiring deep reasoning, nuanced writing, and sophisticated multi-step problem solving where cost is secondary to quality.
Claude Opus 4.1 is Anthropic's top-tier flagship model, designed for the most demanding tasks requiring deep reasoning, nuanced writing, and complex multi-step analysis. It sits at the apex of the Claude 4 family, prioritizing capability over cost and speed.
Verdict
Anthropic's most capable model for demanding professional work, but its steep output cost demands justification.
Quality score
83%
Pricing
$15.00/1M in
$75.00/1M out
Speed
Deliberate
Best for high-stakes professional work where output quality justifies premium pricing — legal analysis, advanced research synthesis, and complex agentic workflows.
Context
200k tokens
Output pricing at $75/1M tokens is among the highest in the market — nearly 3x GPT-4.1's output cost. Batch API discounts may be available through Anthropic. Context window is 200K but very long prompts at Opus pricing can become extremely expensive quickly. Note: supersedes field lists Claude 4 Haiku, which is likely a data error — Opus 4.1 more logically succeeds Claude Opus 4.
FlagshipPremiumReasoningLong ContextAgentic
Best for
High-stakes professional work where output quality justifies premium pricing — legal analysis, advanced research synthesis, and complex agentic workflows.
GPT-4 is OpenAI's original flagship large language model, released in March 2023, offering strong reasoning and instruction-following across text tasks. It represents the foundational GPT-4 release before later variants like GPT-4 Turbo or GPT-4o improved speed, cost, and context length.
Verdict
A once-groundbreaking model now badly outclassed by cheaper, faster, and more capable successors — only use it if you have no choice.
Quality score
51%
Pricing
$30.00/1M in
$60.00/1M out
Speed
Balanced
Best for teams or workflows locked into the original gpt-4 api that require reliable, high-quality text reasoning without needing long context or multimodal input.
Context
8k tokens
At $30/$60 per million tokens, this is one of the most expensive text-only models available. The 8,191-token context window is a hard ceiling that makes it unsuitable for most document-processing tasks. OpenAI continues to offer it for API backward compatibility but actively recommends migrating to GPT-4o or GPT-4 Turbo. New projects should not default to this model.
Legacy flagshipText-onlyHigh costOpenAIGPT-4
Best for
Teams or workflows locked into the original GPT-4 API that require reliable, high-quality text reasoning without needing long context or multimodal input.
GPT-4 v0314 is a frozen snapshot of the original GPT-4 release from March 2023, preserved for reproducibility and regression testing. It offers the same core reasoning capabilities as early GPT-4 but lacks all subsequent improvements, fine-tuning updates, and safety refinements.
Verdict
An expensive museum piece: only justified if you need this exact model snapshot for legacy reproducibility.
Quality score
40%
Pricing
$30.00/1M in
$60.00/1M out
Speed
Balanced
Best for reproducible research or legacy workflows that require consistent, version-locked gpt-4 outputs.
Context
8k tokens
This is a frozen March 2023 snapshot of GPT-4, not a current model. OpenAI may deprecate legacy snapshots with limited notice. The 8,191-token context window is a hard constraint. Cost is identical to much more capable current models, making this a poor choice for new projects.
LegacyGPT-4Version-lockedResearchDeprecated
Best for
Reproducible research or legacy workflows that require consistent, version-locked GPT-4 outputs.
o3 Mini High is OpenAI's compact reasoning model running at maximum reasoning effort, delivering deep chain-of-thought problem-solving in a cost-efficient package. It specializes in STEM tasks — math, coding, and logic — where extended deliberation yields significantly better results than standard chat models.
Verdict
The best bang-for-buck reasoning model for STEM and coding tasks that can tolerate slow response times.
Quality score
66%
Pricing
$1.10/1M in
$4.40/1M out
Speed
Deliberate
Best for solving hard math, competitive programming, and multi-step logical reasoning problems where accuracy matters more than speed.
Context
200k tokens
The 'High' suffix refers to the reasoning_effort parameter set to 'high', which increases token usage and latency significantly versus o3 Mini at medium or low effort. Priced at $1.1/$4.4 per million tokens, it is far cheaper than o1 ($15/$60) and full o3, making it attractive for batch workloads.
OpenAI's o3 is a frontier reasoning model that uses extended chain-of-thought to solve complex problems in math, science, coding, and logic. It represents a significant step up from o1 in reasoning depth and accuracy.
Verdict
The go-to model when you need the right answer, not the fast answer.
Quality score
73%
Pricing
$2.00/1M in
$8.00/1M out
Speed
Deliberate
Best for tackling hard technical problems — from competition-level math to multi-step code debugging — where accuracy matters more than speed.
Context
200k tokens
Pricing at $2/$8 per 1M input/output tokens is moderate for a reasoning model, but long internal reasoning traces can significantly inflate output token counts. Not available via all API tiers — check OpenAI access levels.
ReasoningMathCodingFrontierChain-of-thought
Best for
Tackling hard technical problems — from competition-level math to multi-step code debugging — where accuracy matters more than speed.
Open-source reasoning model that matches o1-class performance on math, science, and complex coding at a fraction of the cost — the best open alternative to proprietary reasoning models.
Verdict
Open-source o1-class reasoning at a fraction of the cost.
Quality score
68%
Pricing
$0.55/1M in
$2.19/1M out
Speed
Deliberate
Best for math, science, complex reasoning, and multi-step problem solving at budget cost
Context
128k tokens
R1 is a genuine milestone for open-source AI. The reasoning quality is real — the tradeoff is latency, not capability.
ReasoningOpen sourceBudgetDeepSeek
Best for
Math, science, complex reasoning, and multi-step problem solving at budget cost
Claude Opus 4 is Anthropic's most capable flagship model, designed for complex reasoning, nuanced writing, and sophisticated multi-step tasks. It sits at the top of the Claude 4 family, prioritizing depth and quality over speed.
Verdict
Anthropic's best model for when quality matters more than speed or cost.
Quality score
84%
Pricing
$30.00/1M in
$150.00/1M out
Speed
Deliberate
Best for demanding professional tasks requiring deep reasoning, nuanced judgment, and high-quality long-form output.
Context
200k tokens
At $15 input / $75 output per 1M tokens, Opus 4 is one of the most expensive models available. Anthropic recommends using Claude Sonnet 4 for most production use cases and reserving Opus 4 for tasks explicitly requiring maximum capability.
FlagshipPremiumReasoningLong ContextAgentic
Best for
Demanding professional tasks requiring deep reasoning, nuanced judgment, and high-quality long-form output.
OpenAI's o3 Deep Research is a reasoning-heavy model purpose-built for multi-step research tasks, capable of autonomously browsing the web, synthesizing sources, and producing detailed analytical reports. It combines o3's chain-of-thought reasoning with agentic tool use to tackle complex, open-ended research questions.
Verdict
The gold standard for autonomous AI research — if you can afford to run it.
Quality score
67%
Pricing
$10.00/1M in
$40.00/1M out
Speed
Deliberate
Best for conducting exhaustive, multi-source research that would take a human analyst hours to compile manually.
Context
200k tokens
Deep Research mode involves agentic tool calls and web browsing, which can multiply effective token costs significantly. Pricing is per token but real-world research sessions often consume large amounts of both. Available via ChatGPT Plus/Pro and API; API access may require higher usage tiers.
Deep ResearchAgenticReasoningPremiumWeb Browsing
Best for
Conducting exhaustive, multi-source research that would take a human analyst hours to compile manually.
OpenAI's o1 is a reasoning-focused model that uses chain-of-thought processing to tackle complex, multi-step problems in math, science, and coding. It deliberately 'thinks before answering,' trading speed for significantly improved accuracy on hard problems.
Verdict
The original deep-thinker that excels at hard reasoning problems, now overshadowed by newer o-series models but still formidable for complex STEM work.
Quality score
69%
Pricing
$15.00/1M in
$60.00/1M out
Speed
Deliberate
Best for solving complex reasoning tasks where accuracy matters more than response time, such as competitive programming, advanced mathematics, and rigorous scientific analysis.
Context
200k tokens
At $15 input / $60 output per 1M tokens, a single complex back-and-forth session can cost dollars. o1-mini is available at a fraction of the price for lighter reasoning tasks. OpenAI has since released o3 and o3-mini, which largely supersede o1 for most reasoning use cases.
ReasoningMathSciencePremiumChain-of-Thought
Best for
Solving complex reasoning tasks where accuracy matters more than response time, such as competitive programming, advanced mathematics, and rigorous scientific analysis.
OpenAI's o3 Pro is the highest-tier reasoning model in the o3 family, designed for maximum accuracy on the most demanding intellectual tasks. It applies extended compute and deeper chain-of-thought reasoning to outperform standard o3 on math, science, coding, and complex analysis.
Verdict
The most powerful reasoning model OpenAI offers — but its extreme pricing means you should reach for it only when accuracy genuinely cannot be compromised.
Quality score
77%
Pricing
$20.00/1M in
$80.00/1M out
Speed
Deliberate
Best for elite-level reasoning tasks where accuracy is paramount and cost is not a constraint — graduate-level math, competitive programming, and rigorous scientific analysis.
Context
200k tokens
o3 Pro is only available via the OpenAI API and ChatGPT Pro subscription tier. Response times can range from tens of seconds to several minutes depending on problem complexity. Output pricing at $80/M tokens is 4x the cost of standard o3.
reasoningSTEMpremiumdeep thinkingflagship
Best for
Elite-level reasoning tasks where accuracy is paramount and cost is not a constraint — graduate-level math, competitive programming, and rigorous scientific analysis.
o1-pro is OpenAI's highest-tier reasoning model, running o1 with extended compute time for deeper, more reliable problem-solving on complex tasks. It is designed for users who need maximum accuracy and thoroughness over speed.
Verdict
The most powerful reasoning model available, but its extreme cost means it's only justified for the hardest problems where no other model will do.
Quality score
75%
Pricing
$150.00/1M in
$600.00/1M out
Speed
Deliberate
Best for solving the hardest math, science, and engineering problems where accuracy is non-negotiable and cost is secondary.
Context
200k tokens
o1-pro is available only via the OpenAI API and ChatGPT Pro subscription ($200/month). It does not support streaming and has longer latency than any other OpenAI model. Not suitable for high-volume workloads.
Max ReasoningUltra-PremiumResearch-GradeMath & ScienceHigh Accuracy
Best for
Solving the hardest math, science, and engineering problems where accuracy is non-negotiable and cost is secondary.
Start with Claude Opus 4.7 for the best daily-driver default. Use Mistral Small 3.1 if cost is the priority. Use Meta: Llama 3.1 8B Instruct if you need the strongest coding performance.
Which AI model is cheapest?
Mistral Small 3.1 is the best cheap default balancing cost, usefulness, and context window. Meta: Llama 3.1 8B Instruct is the cheapest coding specialist.
Which AI model is best for coding?
Meta: Llama 3.1 8B Instruct is the strongest budget coding option in the directory. Claude Opus 4.7 is the practical all-around default that also excels at coding tasks.
How should I compare models?
Start with your main use case, then compare price, speed, and context window. The best model changes quickly when one of those priorities matters more than the others.
The right tool for cheap, fast, high-volume tasks — not for anything that requires serious thinking.
Best for
High-throughput applications where cost and speed matter more than frontier-level quality, such as chatbots, content classification, and text summarization.
The right tool for cheap, fast, high-volume tasks — not for anything that requires serious thinking.
Meta
High-throughput applications where cost and speed matter more than frontier-level quality, such as chatbots, content classification, and text summarization.
$0.02/1M
$0.05/1M
16k tokens
Very fast
Ultra-cheap multimodal model for massive-volume, low-complexity pipelines.
When to use
Ultra-high-volume classification, summarisation, and lightweight vision tasks
When not to use
You need reliable multi-step reasoning or coding quality — it won't hold up.
A capable budget workhorse, but Claude 3.5 Haiku has made it mostly obsolete for new deployments.
When to use
High-volume production pipelines, customer support bots, and real-time text processing where cost and latency are critical constraints.
When not to use
You need deep reasoning, complex coding tasks, or high-quality creative writing — Claude 3 Sonnet, GPT-4o Mini, or even Claude 3.5 Haiku will serve you better.
The right tool for cheap, fast, high-volume tasks — not for anything that requires serious thinking.
When to use
High-throughput applications where cost and speed matter more than frontier-level quality, such as chatbots, content classification, and text summarization.
When not to use
You need deep reasoning, long document analysis, complex code generation, or outputs where quality directly impacts user trust.
