Best open-weight long-context option for self-hosted pipelines.
Affordable self-hosted long-context workflows and analysis pipelines
You want a hosted solution — Gemini 3.1 Flash gives more context for roughly the same cost.
Compare every model's knowledge cutoff, max output, and context window.
Worth considering for internal search, analysis, and review workflows where data sovereignty matters.
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
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
What people actually use Llama 4 Scout for.
Processing large internal document archives in self-hosted analysis pipelines
Long-context retrieval across large codebases with open weights and full data control
Budget-conscious long-context tasks where cloud API costs are prohibitive
The nearest models people weigh against it, and what actually separates them.
vs Llama 4 Maverick — Against Llama 4 Maverick (Meta), Llama 4 Scout runs about 23% cheaper per token and takes 2x the context. Take Llama 4 Scout unless you specifically need what Llama 4 Maverick does better.
vs GPT-6 Astra — Against GPT-6 Astra (OpenAI), Llama 4 Scout runs about 97% cheaper per token, gives up 2.1x on context and answers faster. Take Llama 4 Scout unless you specifically need what GPT-6 Astra does better.
vs GPT-5.5 — Against GPT-5.5 (OpenAI), Llama 4 Scout runs about 95% cheaper per token, gives up 2x on context and answers faster. Take Llama 4 Scout unless you specifically need what GPT-5.5 does better.
Price History
↑525% since May 30
90 data points · tracked daily since May 30, 2026
Affordable self-hosted long-context workflows and analysis pipelines. Start free — no card required.
Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
Similar models worth checking before you commit.
Flexible open-weight model for teams that want control, portability, and solid general-purpose performance.
OpenAI's September 3, 2026 frontier release — the first GPT-6 model and OpenAI's answer to Claude Fable 5.1 two days earlier. State of the art on computer use (OSWorld 2.0 72.6% in ~47% less time than GPT-5.6 Sol), agentic coding (Terminal-Bench 4.0 57.9%), and frontier math (FrontierMath Tier 4 97.6%). $10/$50 per 1M tokens, 1.05M context, 128K output, knowledge cutoff April 30, 2026.
OpenAI's latest agentic flagship for coding, research, computer-use workflows, and long multi-step knowledge work.
Pricing moves, ranking shifts, and capability updates.
Scout moved into the stronger value tier after comparing affordable long-context options.
View modelLlama 4 Scout costs $0.5 per million input tokens and $1.2 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $7.40 at list price, before any batch or caching discounts.
Llama 4 Scout has a 128k tokens context window, with up to 8k tokens of output per response. That is the total of prompt plus response the model can hold in one request.
Llama 4 Scout's training data runs through August 2024, and the model was released on April 5, 2025. For anything after that date it needs web search or documents in the prompt.
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.
You want a hosted solution — Gemini 3.1 Flash gives more context for roughly the same cost.
GPT-5.6 Terra (OpenAI) at $2.00/1M/1M input against Llama 4 Scout's $0.50/1M/1M. Best OpenAI value — near-flagship capability at 60% off. Compare it first if Llama 4 Scout's pricing is the thing stopping you.
Llama 4 Maverick — fast against Llama 4 Scout's fast, with 256k tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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