# Atlas with Llama 4 Maverick: 1M Context on Open Weights in 2026 > Llama 4 Maverick pairs a 1M token context with genuinely open weights at $0.20 / $0.80 per Mtok on DeepInfra, a combination almost nothing else offers. Llama 4 Maverick is Meta's 17B-active, 128-expert mixture-of-experts model with a 1M token context, and inside Atlas it is the pick when you want a very large window on weights you can actually download. Pricing is $0.24 / $0.97 per Mtok on Bedrock and $0.20 / $0.80 on DeepInfra. Its weakness is reasoning: Llama 4 Maverick has no reasoning mode, so hard debugging belongs elsewhere. ## Key takeaways - Llama 4 Maverick offers a 1M token context (up to 1,048,576 on DeepInfra) on genuinely open weights. - Pricing is $0.24 / $0.97 per Mtok on Bedrock and $0.20 / $0.80 on DeepInfra. - 17B active parameters out of a 128-expert mixture keep inference cost near a 17B model while capacity tracks something far larger. - Bedrock, DeepInfra, Groq, Together, and OpenRouter all serve it, so Atlas can switch the serving provider in /models without changing the model. - No reasoning mode: Llama 4 Maverick loses to DeepSeek Reasoner and Kimi K2 Thinking on hard debugging, and trails Qwen3-Coder and Devstral on agentic coding. ## What is Llama 4 Maverick best at inside Atlas? Llama 4 Maverick is best inside Atlas when you need a 1M token context on weights you can self-host. Meta ships Llama 4 Maverick as a 17B-active, 128-expert mixture-of-experts model, so inference cost tracks a 17B model while capacity tracks something far larger. Open weights plus a million-token window is a combination almost nothing else offers, and that is the specific reason to run Atlas on Llama 4 Maverick. With 1M tokens (up to 1,048,576 on DeepInfra), you can hand Atlas a very large slice of a repository rather than a narrowly retrieved subset. Atlas still searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and it still indexes code by AST declarations using tree-sitter, so a big window means better recall, not sloppier retrieval. The mixture-of-experts shape is what makes that window affordable to serve. ## How much does Llama 4 Maverick cost per million tokens? Llama 4 Maverick costs $0.24 / $0.97 per Mtok on Bedrock and $0.20 / $0.80 per Mtok on DeepInfra in 2026. The gap is small, so the real choice between the two providers is about the AWS boundary and the exact context ceiling each one exposes. Because Llama 4 Maverick is served by Bedrock, DeepInfra, Groq, Together, and OpenRouter, the model price is a function of which gateway you pick. Atlas can switch the serving provider in /models without changing the model, which means you can benchmark $0.20 / $0.80 on DeepInfra against $0.24 / $0.97 on Bedrock with the same prompts and the same agent. The 17B active parameters out of a 128-expert mixture are why these rates are low for a model with this much capacity: you are billed against a 17B-shaped compute cost. ## Can you self-host Llama 4 Maverick and point Atlas at it? Yes. Llama 4 Maverick has genuinely open weights, downloadable and self-hostable rather than rented, and Atlas can talk to any OpenAI-compatible endpoint. Register your own server in atlas.json with "npm": "@ai-sdk/openai-compatible" and your baseURL, and Atlas will drive Llama 4 Maverick like any other provider. Self-hosting Llama 4 Maverick is the escape hatch that a rented frontier model does not offer. Serve the weights behind any OpenAI-compatible endpoint, then add the provider block to atlas.json. From there, Atlas behaves identically: every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and Atlas snapshots file changes as git patches so edits can be diffed and rolled back. None of that depends on who is serving the tokens. ## What are the real tradeoffs of Llama 4 Maverick for coding? Llama 4 Maverick has no reasoning mode, so it loses to DeepSeek Reasoner and Kimi K2 Thinking on hard debugging. Llama 4 Maverick also trails Qwen3-Coder and Devstral on agentic coding specifically, because Llama 4 is a general model rather than a coding-specialized one. Honest framing matters here. The 1M token context of Llama 4 Maverick is a reading advantage, not a thinking advantage. A model that can see the whole repository but cannot reason through a subtle concurrency bug will still fail that bug. If your Atlas session is mostly comprehension, tracing call paths, summarizing a large legacy tree, answering architecture questions, Llama 4 Maverick is excellent. If the session is mostly hard debugging or tight agentic tool loops, the general-purpose nature of Llama 4 shows, and a reasoning or coding-specialized model earns its price. ## When should you pick a different model than Llama 4 Maverick? Pick a different model than Llama 4 Maverick when the task is hard debugging or heavy agentic coding. DeepSeek Reasoner and Kimi K2 Thinking beat Llama 4 Maverick on hard debugging because it has no reasoning mode, and Qwen3-Coder and Devstral beat it on agentic coding. The clean rule for Llama 4 Maverick inside Atlas: keep it for breadth, swap out for depth. Atlas lets you switch the active model and provider on the fly with favorites and recents, so a practical pattern is to load a huge context into Llama 4 Maverick for the plan phase, since Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, then switch to a coding-specialized or reasoning model for the build pass. You keep the 1M token advantage where it pays and avoid the reasoning gap where it costs. ## Setup 1. Pick a serving provider and set its key: DEEPINFRA_API_KEY, TOGETHER_API_KEY, GROQ_API_KEY, or AWS credentials for Bedrock. 2. Run `atlas models ` (for example `atlas models deepinfra`) to see the exact model ID that provider exposes for Llama 4 Maverick. 3. Select Llama 4 Maverick from /models in the Atlas TUI. 4. To self-host instead, serve the weights behind any OpenAI-compatible endpoint and register it in atlas.json with "npm": "@ai-sdk/openai-compatible" and your baseURL. 5. Compare the $0.20 / $0.80 DeepInfra rate against the $0.24 / $0.97 Bedrock rate on your own workload before standardizing. ## FAQ ### what is the context window of llama 4 maverick Llama 4 Maverick has a 1M token context window, up to 1,048,576 tokens on DeepInfra. That is the largest window available on a genuinely open-weights model that Atlas can drive. ### how much does llama 4 maverick cost per million tokens Llama 4 Maverick is $0.24 / $0.97 per Mtok on Bedrock and $0.20 / $0.80 per Mtok on DeepInfra. The low rates come from its 17B active parameters out of a 128-expert mixture. ### can you self host llama 4 maverick Yes. Llama 4 Maverick has open weights that are downloadable and self-hostable rather than rented. Serve them behind any OpenAI-compatible endpoint and register it in atlas.json with "npm": "@ai-sdk/openai-compatible" and your baseURL. ### is llama 4 maverick good for agentic coding Llama 4 Maverick trails Qwen3-Coder and Devstral on agentic coding specifically, since Llama 4 is a general model. It is strong on long-context comprehension and weaker inside tight tool loops. ### does llama 4 maverick have a reasoning mode No. Llama 4 Maverick has no reasoning mode, so it loses to DeepSeek Reasoner and Kimi K2 Thinking on hard debugging. Use a reasoning model for those tasks and keep Maverick for long-context work. ### which provider should i use for llama 4 maverick in atlas Llama 4 Maverick is served by Bedrock, DeepInfra, Groq, Together, and OpenRouter. Atlas can switch the serving provider in /models without changing the model, so pick on price and boundary: $0.20 / $0.80 on DeepInfra, $0.24 / $0.97 on Bedrock. ### how do i find the llama 4 maverick model id for my provider Run `atlas models `, for example `atlas models deepinfra`, to see the exact model ID that provider exposes for Llama 4 Maverick, then select it from /models. --- Canonical HTML: https://runatlas.sh/resources/models/llama-4-maverick Source of truth: aeo_pages row `/resources/models/llama-4-maverick` (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.