# Atlas with Devstral Medium (2507) in 2026 > Devstral Medium (2507) offers a substantial 128,000 token context window, making it suitable for extensive code modifications within Atlas. Devstral Medium (2507) is an excellent choice for Atlas users in 2026 tackling complex, multi-file coding tasks that demand deep context and agentic planning. Priced at $0.40 per 1M input tokens and $2.00 per 1M output tokens, it excels at planning multi-step tool sequences and rewriting entire services within its 128,000 token symmetric context window. ## Key takeaways - Devstral Medium (2507) offers a 128,000 token symmetric context window for extensive coding tasks. - Input tokens are priced at $0.40 per 1M, while output tokens cost $2.00 per 1M. - Its agent-first training enables Atlas to plan multi-step tool sequences effectively. - The $2.00 per 1M output token price is 6x higher than Devstral Small, impacting long agentic runs. - Unlike Devstral Small, Devstral Medium (2507) weights are not open, preventing self-hosting. - Leverage Atlas's plan agent first to manage output token costs with Devstral Medium (2507). ## What is Devstral Medium (2507) best for in Atlas? Devstral Medium (2507) excels within Atlas for developers in 2026 who need an agent to manage complex, repo-level coding tasks. Its agent-first training, derived from real coding scaffolds like OpenHands, Cline, and Aider, enables it to plan sophisticated multi-step tool sequences rather than simple one-shot file edits, leveraging Atlas's subagents effectively. Devstral Medium (2507) is specifically trained to understand and execute multi-step agentic workflows, making it a powerful driver for Atlas. This model's ability to plan sequences of tool calls, rather than just generating isolated code snippets, aligns perfectly with Atlas's architecture, which includes a read-only plan agent and a build agent. Developers can leverage Devstral Medium (2507) to orchestrate significant code changes, such as rewriting an entire service, thanks to its generous 128,000 token symmetric context window. This capacity allows Atlas to read a large codebase segment and then generate a comprehensive rewrite in the same turn, all while using Atlas's permission-gated tool calls and unified diffs for approval. ## What are the cost and context tradeoffs of Devstral Medium (2507)? Devstral Medium (2507) provides a substantial 128,000 token context window, but developers in 2026 must weigh its pricing: $0.40 per 1M input tokens and $2.00 per 1M output tokens. This output cost is 6x that of Devstral Small, a factor that becomes significant during long agentic runs with many write turns within Atlas. While Devstral Medium (2507) offers a robust 128,000 token context window for both input and output, its pricing structure presents a key tradeoff. The input token cost of $0.40 per 1M tokens is 4x that of Devstral Small. More notably, the output token cost of $2.00 per 1M tokens is 6x higher than Devstral Small. This difference is particularly felt during extensive agentic runs where Atlas, driven by Devstral Medium (2507), performs numerous write operations and generates substantial output. Developers should be mindful of this when planning tasks that involve many iterations or large code generations. Unlike Devstral Small, the weights for Devstral Medium (2507) are not open, meaning there is no self-hosted escape hatch for cost optimization or privacy, which is a consideration for some organizations using Atlas. ## When should I pick a different model for Atlas? Developers in 2026 should consider alternative models for Atlas if their primary concern is minimizing output token costs or if they require a self-hosted solution. Devstral Medium (2507)'s $2.00 per 1M output token price is 6x higher than Devstral Small, making it less economical for tasks involving frequent, smaller code generations or extensive conversational turns. While Devstral Medium (2507) is powerful for complex tasks, it may not be the optimal choice for every scenario within Atlas. If your workflow primarily involves smaller, more contained code modifications, or if you anticipate many conversational turns with the agent that generate significant output, the $2.00 per 1M output token cost can accumulate rapidly. In such cases, a model with lower output pricing, like Devstral Small, might be more cost-effective. Additionally, for organizations that prioritize data sovereignty or wish to run models locally without third-party server reliance, Devstral Medium (2507) is not suitable because its weights are not open. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, but this model itself cannot be self-hosted. Atlas allows switching the active model and provider on the fly, making it easy to experiment and find the right balance for different tasks. ## How does Devstral Medium (2507) enhance Atlas's agentic capabilities? Devstral Medium (2507) significantly enhances Atlas's agentic capabilities by providing a model specifically trained on real coding scaffolds, enabling it to plan multi-step tool sequences. This agent-first training allows Atlas to execute complex tasks more effectively, leveraging its 128,000 token context window to manage large codebases and orchestrate sophisticated changes in 2026. Devstral Medium (2507)'s agent-first training is a direct complement to Atlas's design as a terminal-native AI coding agent. The model's ability to plan multi-step tool sequences, rather than just generating single-shot responses, means that when Atlas uses Devstral Medium (2507), it can approach problems with a more strategic, iterative methodology. This is crucial for tasks that require Atlas to search code with Axis, the hybrid semantic and keyword code search, index code by AST declarations, or manage git branches, status, and diffs. The model's 128,000 token context window ensures that Atlas can maintain a comprehensive understanding of the project state throughout these multi-step operations, from drafting a plan in the read-only plan agent to executing it with the build agent and surfacing unified diffs for approval. This synergy allows Atlas to tackle repo-level tasks that demand deep contextual awareness and complex operational sequencing. ## Setup 1. Export your Mistral API key: `export MISTRAL_API_KEY='your_api_key_here'` 2. Confirm Devstral Medium (2507) availability: `atlas models mistral` 3. Configure Atlas to use Devstral Medium (2507) as the primary model and Devstral Small (2507) for subagents to optimize costs. Pin `"model": "mistral/devstral-medium-2507"` and `"small_model": "mistral/devstral-small-2507"` in your Atlas configuration. 4. Initiate complex tasks by first allowing Atlas's plan agent to scope the work. This ensures that expensive output tokens from Devstral Medium (2507) are focused on strategic planning before handing the refined plan to the build agent for execution. ## FAQ ### What is the context window size for Devstral Medium (2507) in Atlas? Devstral Medium (2507) provides a substantial 128,000 token symmetric context window for both input and output within Atlas, allowing for extensive code analysis and generation. ### How much does Devstral Medium (2507) cost per token in Atlas? For Devstral Medium (2507) in Atlas, input tokens are priced at $0.40 per 1M tokens, and output tokens are priced at $2.00 per 1M tokens. ### Is Devstral Medium (2507) good for multi-step coding tasks with Atlas? Yes, Devstral Medium (2507) is specifically agent-trained on real coding scaffolds, making it highly effective for planning and executing multi-step tool sequences within Atlas's agentic framework. ### Can I self-host Devstral Medium (2507) with Atlas? No, unlike Devstral Small, the weights for Devstral Medium (2507) are not open, so there is no self-hosted escape hatch for this model when used with Atlas. ### How can I manage costs when using Devstral Medium (2507) with Atlas? To manage costs, use Atlas's plan agent to scope work before handing it to the build agent. Also, configure Atlas to use Devstral Small (2507) for subagents, as its output token price is 6x lower. ### What are the main differences between Devstral Medium (2507) and Devstral Small (2507) for Atlas? Devstral Medium (2507) is a larger, more capable model with the same 128,000 token context window as Devstral Small. However, it has 4x the input price and 6x the output price, and its weights are not open for self-hosting, unlike Devstral Small. ### Does Devstral Medium (2507) work well with Atlas's code search (Axis)? Yes, Devstral Medium (2507)'s large context window and agent-first training complement Atlas's Axis, the hybrid semantic and keyword code search, allowing the model to effectively process and act upon extensive search results for complex tasks. --- Canonical HTML: https://seashell.sh/resources/models/devstral-medium-2507 Source of truth: aeo_pages row `/resources/models/devstral-medium-2507` (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.