Best open-weight long-context option for self-hosted pipelines.
54
Coding
60
Writing
78
Research
35
Images
86
Value
88
Long Context
Use this when
Affordable self-hosted long-context workflows and analysis pipelines
Skip this if
Strengths
512K context window at the lowest cost point in the directory
Good for internal analysis pipelines and document processing
Open weights give you full control over deployment
Weaknesses
Less polished than hosted frontier models on nuanced tasks
Gemini 3.1 Flash now offers 1M context at only $0.50/1M — bigger and hosted
Monthly cost estimate
See what Llama 4 Scout actually costs at your usage level
Input tokens / month1M
10k50M
Output tokens / month500k
10k25M
Input cost
$0.080
Output cost
$0.150
Total / month
$0.230
Based on Llama 4 Scout API pricing: $0.08/1M input · $0.3/1M output. Real costs vary by provider discounts and caching. Check the provider for exact current rates.
Price History
Llama 4 Scout pricing over time
↓84% since Mar 24
41 data points · tracked daily since Mar 24, 2026
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Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
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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
Change history
Pricing moves, ranking shifts, and capability updates.
PricingMar 27, 2026
Llama 4 Scout — output price cut
Llama 4 Scout output pricing changed from $1.20/1M to $0.30/1M (↓ cheaper, 75% cut).
Llama 4 Scout is best for affordable self-hosted long-context workflows and analysis pipelines. 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 Scout?
You want a hosted solution — Gemini 3.1 Flash gives more context for roughly the same cost.
What is a cheaper alternative to Llama 4 Scout?
Mistral: Mistral Nemo 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 Scout?
Anthropic: Claude 3.5 Haiku is the better pick when response time matters more than maximum depth or premium quality.
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You want a hosted solution — Gemini 3.1 Flash gives more context for roughly the same cost.
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.
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.
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.