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Meta: Llama 3.1 70B Instruct
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.
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
$0.34/1M in
$0.34/1M 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.
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.
Llama 4 Maverick is best for flexible self-hosted deployments and mixed general workloads. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and fast speed.
When should I avoid Llama 4 Maverick?
You want the strongest hosted answer quality — closed frontier models win on benchmarks.
What is a cheaper alternative to Llama 4 Maverick?
Meta: Llama 3.1 8B Instruct is the lower-cost option to compare first when you want a similar workflow fit with less token spend.
What is a faster alternative to Llama 4 Maverick?
Anthropic: Claude 3.5 Haiku is the better pick when response time matters more than maximum depth or premium quality.
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