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
Real-world use cases
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
How Llama 4 Scout compares
The nearest models people weigh against it, and what actually separates them.
vs Llama 3.1 70B Instruct — Against Llama 3.1 70B Instruct (Meta), Llama 4 Scout costs about 53% more per token and takes 3.9x the context. Llama 3.1 70B Instruct is the one to check first if the price difference matters more than the ceiling.
vs Claude 3.5 Haiku — Against Claude 3.5 Haiku (Anthropic), Llama 4 Scout runs about 65% cheaper per token, takes 2.6x the context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
vs Devstral 2 2512 — Against Devstral 2 2512 (Mistral), Llama 4 Scout runs about 29% cheaper per token and takes 2x the context. Take Llama 4 Scout unless you specifically need what Devstral 2 2512 does better.
Price History
Llama 4 Scout pricing over time
↑400% since Jun 9
90 data points · tracked daily since Jun 9, 2026
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Affordable self-hosted long-context workflows and analysis pipelines. Start free — no card required.
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
4/5 speed
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
5/5 speed
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.
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
4/5 speed
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.
Llama 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.
What is the context window of Llama 4 Scout?
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.
What is the knowledge cutoff of Llama 4 Scout?
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.
What is Llama 4 Scout best for?
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?
Llama 3.1 70B Instruct (Meta) at $0.40/1M/1M input against Llama 4 Scout's $0.50/1M/1M — roughly 53% less per token all in. The go-to budget open-weight model for teams who need solid LLM capability without frontier model pricing. Compare it first if Llama 4 Scout's pricing is the thing stopping you.
What is a faster alternative to Llama 4 Scout?
Claude 3.5 Haiku — very fast against Llama 4 Scout's fast, with 200k 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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