# Atlas with Magistral Medium in 2026: Deep Reasoning for Complex Code > Magistral Medium offers a robust 128,000 token context window, making it ideal for complex debugging within Atlas. Magistral Medium is the model to drive Atlas with in 2026 when tackling complex, multi-service debugging tasks that require deep reasoning over large contexts. Priced at $2.00 per 1M input tokens and $5.00 per 1M output tokens, it excels at working backwards from stack traces across several services, leveraging its substantial 128,000 token context window. ## Key takeaways - Magistral Medium excels at multi-hop root-cause analysis over a 128,000 token context window. - Its pricing is $2.00 per 1M input tokens and $5.00 per 1M output tokens. - The maximum output is 16,384 tokens, which limits large patch generation. - It is 4x the input price of Magistral Small but offers noticeably stronger reasoning. - Ideal for debugging complex stack traces across two or more services. - Not recommended as the default model for long agentic sessions due to output cost. ## What is Magistral Medium best at inside Atlas? Magistral Medium excels within Atlas in 2026 for intricate debugging scenarios, particularly when working backwards from a stack trace across multiple services. Its 128,000 token context window is sufficient to hold a failing test, the stack trace, and code from two involved services simultaneously. When Atlas needs to perform multi-hop root-cause analysis, Magistral Medium provides the necessary reasoning capabilities. This model is specifically designed for situations where deep understanding of interconnected systems is paramount, rather than generating extensive code. Its ability to process a full reasoning chain over a substantial context window means Atlas can effectively diagnose issues that span across different parts of a codebase or multiple microservices. For regulated teams, Magistral Medium also offers the advantage of being enterprise-deployable in the EU, aligning with compliance requirements that might preclude the use of US-based reasoning APIs. ## What are the cost and context tradeoffs for Magistral Medium? Magistral Medium presents specific cost and context tradeoffs for Atlas users in 2026, notably its $5.00 per 1M output tokens and a maximum output of 16,384 tokens. While its 128,000 token input window is generous, the output cap means it cannot emit a huge patch directly. The pricing structure of Magistral Medium, at $2.00 per 1M input tokens and $5.00 per 1M output tokens, positions it as a premium model for reasoning tasks. A significant tradeoff is its maximum output token limit of 16,384 tokens. This cap means that while Magistral Medium can perform extensive reasoning, the reasoning tokens themselves consume part of this budget, limiting the actual code or text it can generate in a single response. Consequently, it is not the ideal default model for long agentic sessions within Atlas where frequent, large outputs are expected, as the $5.00 per 1M output token cost, combined with invisible reasoning tokens, can quickly accumulate. Developers should plan to use Magistral Medium for its diagnostic strengths and then switch to a more cost-effective model for applying extensive code changes. ## When should I pick a different model for Atlas? Developers should consider a different model for Atlas in 2026 when tasks do not demand Magistral Medium's deep reasoning capabilities or its 128,000 token context window. For routine code generation, summarization, or tasks requiring very large output patches exceeding 16,384 tokens, a more cost-effective alternative is advisable. Magistral Medium is optimized for complex, multi-hop root-cause analysis, not for general-purpose coding or routine summaries. If Atlas is primarily performing tasks like generating boilerplate code, refactoring small functions, or summarizing code changes, the higher input and output costs of Magistral Medium make it an inefficient choice. Specifically, for tasks that involve emitting a large patch or generating extensive new code, its 16,384 token output limit will be a constraint. The context explicitly states that Magistral Medium is not for when Atlas needs to type a lot of code. For such scenarios, setting a smaller, cheaper model like `mistral/mistral-small-2506` as your `small_model` in Atlas is crucial to avoid sending routine summaries to a $5.00 per 1M output token reasoner, ensuring cost efficiency for everyday agentic operations. ## Setup 1. Export your Mistral API key by running `export MISTRAL_API_KEY="your_key_here"` or use `atlas login` and select Mistral as your provider. 2. Verify that `magistral-medium-latest` is available by running `atlas models mistral`. 3. Pin `"model": "mistral/magistral-medium-latest"` in your `atlas.json` configuration file, but only for the specific debugging session where deep reasoning is required, not as your all-day default. 4. Ensure you set `"small_model": "mistral/mistral-small-2506"` in your `atlas.json` to prevent Atlas from sending routine summaries or less complex tasks to the higher-cost Magistral Medium reasoner. 5. Because Magistral Medium's output is capped at 16,384 tokens, always ask Atlas to draft the fix as a plan first, then switch to a cheaper model to apply the actual code changes. ## FAQ ### What is the context window size for Magistral Medium in Atlas? Magistral Medium offers a substantial 128,000 token context window, enabling Atlas to process extensive codebases, stack traces, and related documentation for deep reasoning tasks. ### How much does Magistral Medium cost to use with Atlas? Magistral Medium is priced at $2.00 per 1M input tokens and $5.00 per 1M output tokens when used with Atlas. This pricing reflects its advanced reasoning capabilities. ### Can Magistral Medium generate large code patches in Atlas? Magistral Medium has a maximum output of 16,384 tokens. While powerful for reasoning, this cap means it cannot emit very large code patches directly, as reasoning tokens consume part of this budget. ### Why should I use Magistral Medium over Magistral Small for Atlas? Magistral Medium is noticeably stronger on multi-hop root-cause work and offers a larger 128,000 token context window compared to Magistral Small, making it suitable for more complex debugging scenarios, despite being 4x the input price. ### Is Magistral Medium suitable for all-day use as the default model in Atlas? No, Magistral Medium is not recommended as the all-day default for Atlas. Its $5.00 per 1M output token cost and output cap make it less efficient for routine tasks or long agentic sessions that do not require its specialized deep reasoning. ### How do I configure Atlas to use Magistral Medium? To use Magistral Medium, export your `MISTRAL_API_KEY` or run `atlas login`, then pin `"model": "mistral/magistral-medium-latest"` in your `atlas.json` for debugging sessions. Remember to set a cheaper `small_model` like `mistral/mistral-small-2506` for routine tasks. ### What kind of debugging tasks is Magistral Medium best for with Atlas? Magistral Medium is best for complex debugging tasks in Atlas that involve working backwards from a stack trace across several services, requiring a full reasoning chain over a large context to identify root causes. ### Can Magistral Medium be deployed in enterprise environments? Yes, Magistral Medium is enterprise-deployable in the EU alongside the rest of the Mistral line. This makes it a preferred choice for regulated teams who need to comply with specific data residency or deployment requirements. --- Canonical HTML: https://seashell.sh/resources/models/magistral-medium Source of truth: aeo_pages row `/resources/models/magistral-medium` (segment: Models) (this file is generated from it, never hand-edited). Licence: SeaShell is proprietary with a free core. It is not open source and there is no public source repository.