# Atlas with Llama 4 Scout: the 3.5M Token Context Model in 2026 > Llama 4 Scout is exposed on Bedrock with a 3.5M token context, the largest window of any model Atlas can reach through its registry. Llama 4 Scout is the smaller Llama 4, a 17B-active 16-expert mixture-of-experts model, and on Bedrock it is exposed with a 3.5M token context, the largest window of any model Atlas can reach through its registry. Pricing is $0.17 / $0.66 per Mtok on Bedrock and $0.10 / $0.30 on DeepInfra. Inside Atlas, Llama 4 Scout is a long-context reader rather than a builder: it has no reasoning mode and no coding specialization. ## Key takeaways - Llama 4 Scout on Bedrock has a 3,500,000 token context, more than three times the 1M ceiling of Claude Sonnet 5, GPT-5.6, or Gemini 3.1 Pro. - Pricing is $0.17 / $0.66 per Mtok on Bedrock and $0.10 / $0.30 on DeepInfra. - The advertised window varies by provider: 128K on Meta's own API, 327K on DeepInfra, 3.5M on Bedrock, so portability is a real problem. - Llama 4 Scout has open weights with a 16-expert MoE at 17B active, so it self-hosts on far less hardware than Maverick. - No reasoning mode and no coding specialization: Llama 4 Scout is a long-context reader more than a builder. ## What is Llama 4 Scout best at inside Atlas? Llama 4 Scout is best inside Atlas as a whole-repository reader. On Bedrock, Llama 4 Scout carries a 3,500,000 token context, more than three times the 1M ceiling of Claude Sonnet 5, GPT-5.6, or Gemini 3.1 Pro, which changes what you can put in front of the agent. Most model choices force Atlas to retrieve a subset and hope the retrieval was right. Llama 4 Scout on Bedrock removes that constraint for a large class of repositories. At $0.10 per Mtok input on DeepInfra, Llama 4 Scout is cheap enough to feed it an entire repository rather than a retrieved subset, though DeepInfra's window is smaller. Atlas still indexes code by AST declarations using tree-sitter, not blind line windows, so pairing that structured index with an enormous window gives you both precision and coverage. Architecture questions, cross-cutting audits, and orientation on unfamiliar code are where Llama 4 Scout earns its place. ## How much does Llama 4 Scout cost per million tokens? Llama 4 Scout costs $0.17 / $0.66 per Mtok on Bedrock and $0.10 / $0.30 per Mtok on DeepInfra in 2026. The DeepInfra rate of $0.10 input is the cheapest way to run Atlas on Llama 4 Scout, but it comes with a much smaller advertised context window. Pricing and context are coupled for Llama 4 Scout in a way that catches people out. Bedrock charges more, $0.17 / $0.66, and gives the 3.5M window. DeepInfra charges $0.10 / $0.30 and advertises 327K. Meta's own API advertises 128K. So the question is never just what Llama 4 Scout costs, it is what Llama 4 Scout costs at the window you actually need. Feeding a whole repository at $0.10 per Mtok is only possible if the provider will accept the tokens. Decide window first, then price. ## Why does the Llama 4 Scout context window differ by provider? The advertised context window for Llama 4 Scout varies wildly by provider: 128K on Meta's own API, 327K on DeepInfra, and 3.5M on Bedrock. That spread is a real portability problem, because an Atlas prompt that fits on Bedrock will be rejected or truncated elsewhere. Treat the Llama 4 Scout window as a property of the provider, not a property of the model. Before you build any Atlas workflow that depends on a huge window, verify the context Atlas resolved by running `atlas models amazon-bedrock`. If the workflow only works at 3.5M tokens, you are locked to Bedrock, and switching to DeepInfra to save money will silently break it. This is the single most important thing to understand about Llama 4 Scout, and it is why the model is more useful as a deliberate long-context tool than as a default. ## What are the tradeoffs of Llama 4 Scout for real coding work? Llama 4 Scout has no reasoning mode and no coding specialization. Meta built Llama 4 Scout as a 17B-active 16-expert mixture-of-experts general model, so inside Atlas it behaves as a long-context reader more than a builder, regardless of the 3.5M token window. A very large window does not substitute for capability. Llama 4 Scout will happily read three and a half million tokens of your codebase and still produce a weaker patch than a coding-specialized model that read a well-retrieved 50K. Because Atlas computes a unified diff for every file edit and surfaces it for approval before writing, you will see the quality gap directly in the diffs you are asked to approve. The one genuine capability upside is self-hosting: the 16-expert MoE at 17B active means Llama 4 Scout self-hosts on far less hardware than Maverick. ## When should you pick a different model than Llama 4 Scout? Pick a different model than Llama 4 Scout whenever the task requires reasoning or actual code generation. Llama 4 Scout has no reasoning mode and no coding specialization, so a Scout session that is producing patches rather than answers is using the wrong model for the job. The strongest pattern for Llama 4 Scout inside Atlas is a two-model split. Use Llama 4 Scout on Bedrock at its 3.5M window during the plan phase, since Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, then switch the active model before the build pass. Atlas lets you switch the active model and provider on the fly with favorites and recents, so this costs one keystroke. If you never need more than 1M tokens, Llama 4 Maverick or a frontier model will serve you better end to end. ## Setup 1. For the 3.5M window, use Bedrock: set AWS_REGION plus AWS_PROFILE, and enable the model in the Bedrock console. 2. Verify the context Atlas resolved with `atlas models amazon-bedrock` before relying on the large window. 3. Select us.meta.llama4-scout-17b-instruct-v1:0 from /models in the Atlas TUI. 4. For cheaper access at a smaller window, set DEEPINFRA_API_KEY and pick meta-llama/Llama-4-Scout-17B-16E-Instruct. 5. Keep a coding-specialized model in the main slot and reserve Llama 4 Scout for long-context reading passes. ## FAQ ### what is the largest context window model in atlas Llama 4 Scout on Bedrock, at 3.5M tokens. That is the largest window of any model Atlas can reach through its registry, and more than three times the 1M ceiling of Claude Sonnet 5, GPT-5.6, or Gemini 3.1 Pro. ### how much does llama 4 scout cost per million tokens Llama 4 Scout is $0.17 / $0.66 per Mtok on Bedrock and $0.10 / $0.30 per Mtok on DeepInfra. The DeepInfra rate is cheaper but comes with a much smaller advertised context window. ### why does llama 4 scout show a different context window on each provider Llama 4 Scout's advertised window is set by the provider: 128K on Meta's own API, 327K on DeepInfra, and 3.5M on Bedrock. Verify what Atlas resolved with `atlas models amazon-bedrock` before depending on the large window. ### llama 4 scout vs llama 4 maverick which should i use Llama 4 Scout is the smaller 16-expert MoE and self-hosts on far less hardware than Maverick, and on Bedrock it reaches 3.5M tokens. Llama 4 Maverick is the 128-expert model with a 1M window. Pick Scout for extreme long-context reading, Maverick for more capacity. ### is llama 4 scout good for writing code Not particularly. Llama 4 Scout has no reasoning mode and no coding specialization, so inside Atlas it works better as a long-context reader than as the model driving edits. ### how do i enable llama 4 scout on bedrock for atlas Set AWS_REGION plus AWS_PROFILE, enable the model in the Bedrock console, run `atlas models amazon-bedrock` to verify the resolved context, then select us.meta.llama4-scout-17b-instruct-v1:0 from /models. ### can i feed my whole repo to llama 4 scout On Bedrock, yes, within the 3.5M token window. At $0.10 per Mtok input on DeepInfra Llama 4 Scout is cheap enough to feed an entire repository rather than a retrieved subset, but DeepInfra advertises only 327K tokens. --- Canonical HTML: https://runatlas.sh/resources/models/llama-4-scout Source of truth: aeo_pages row `/resources/models/llama-4-scout` (segment: Models) (this file is generated from it, never hand-edited). Licence: Atlas is proprietary with a free core. It is not open source and there is no public source repository.