# Atlas with Magistral Small in 2026 > Magistral Small offers a 128,000 token context window for extensive reasoning within Atlas's plan agent. Magistral Small is Mistral AI's first open reasoning model, designed to enhance Atlas's plan agent by providing explicit chains of thought. This transforms the plan agent into a step-by-step debugger, costing $0.50 / 1M input tokens and $1.50 / 1M output tokens for its powerful reasoning capabilities. ## Key takeaways - Magistral Small excels at explicit reasoning traces, costing $0.50 / 1M input tokens. - It features a 128,000 token context window, supporting extensive reasoning and large diffs. - Reasoning tokens are billed as output at $1.50 / 1M, potentially increasing costs for complex bugs. - The model's 24B open weights enable local deployment via Ollama for offline work. - Magistral Small has a slower first token than Mistral Small 3.2, making it less ideal for speed-critical tasks. ## What is Magistral Small best for in Atlas? Magistral Small excels at providing detailed reasoning traces within Atlas, making the plan agent a powerful debugger for complex tasks in 2026. This model's explicit chain of thought capability is available for $0.50 per 1M input tokens, significantly enhancing problem-solving workflows. Magistral Small, Mistral AI's first open reasoning model, is specifically designed to emit an explicit chain of thought before answering. Within Atlas, this capability transforms the read-only plan agent from a simple summarizer into an actual step-by-step debugger. Developers using Atlas can leverage Magistral Small to gain deep insights into the agent's decision-making process, understanding the 'why' behind each proposed action. This is particularly valuable for tackling hard bugs or intricate coding challenges where a clear, traceable path to a solution is essential. The model's substantial 128,000 token context window ensures that long reasoning chains, along with the final patch, can fit comfortably within a single response, facilitating comprehensive problem resolution. ## What are the cost and context tradeoffs of Magistral Small? Magistral Small offers a generous 128,000 token context window for both input and output, but its pricing structure means reasoning tokens are billed as output. This model costs $0.50 per 1M input tokens and $1.50 per 1M output tokens, a quarter the input price of Magistral Medium. The primary tradeoffs for Magistral Small revolve around its pricing and how it handles reasoning. While the model provides an impressive 128,000 token context window, allowing for extensive input and output, its explicit chain of thought is billed as output tokens. This means that for a hard bug requiring a lengthy reasoning process, the cost can accumulate significantly, potentially exceeding the token count of the final diff itself. Developers must weigh the value of detailed debugging against the potential for higher output token costs. However, the model's input price of $0.50 per 1M input tokens is a quarter of Magistral Medium's input price, making it an economical choice for initiating complex reasoning tasks, provided the output token usage is managed. The large context window is a significant advantage, ensuring that Atlas can process substantial codebases and generate comprehensive solutions or detailed reasoning without hitting token limits prematurely. ## When should I choose a different model over Magistral Small? While Magistral Small excels at reasoning, it has a slower first token compared to Mistral Small 3.2, making it a poor choice for tasks prioritizing speed. If your Atlas workflow demands rapid initial responses or serves as a `small_model` for quick, iterative actions, consider alternatives in 2026. Magistral Small is not the ideal choice when speed of initial response is paramount. Its slower first token compared to Mistral Small 3.2 means that for workflows requiring quick, snappy interactions or when used as a `small_model` for rapid, less complex tasks, it may introduce noticeable delays. Atlas allows you to switch the active model and provider on the fly, so for scenarios where you need immediate feedback or are performing actions that do not require extensive, explicit reasoning, a faster model like Mistral Small 3.2 would be more appropriate. For instance, if Atlas is performing simple code lookups using Axis, the hybrid semantic and keyword code search, or generating very short, direct responses, the overhead of Magistral Small's reasoning capabilities and its slower first token might not be justified. ## How does Magistral Small support local development with Atlas? Magistral Small's 24B open weights allow developers to pull the same reasoning model locally via Ollama, enabling offline work in 2026. This capability ensures that sensitive code can remain off third-party servers, leveraging Atlas's ability to build its code index with local Ollama embeddings. One significant advantage of Magistral Small is its availability as 24B open weights. This means that the same powerful reasoning model can be pulled and run locally via Ollama. For developers concerned about data privacy or working in environments with restricted internet access, this is a crucial feature. Atlas can build its code index with local Ollama embeddings, ensuring that code never leaves your local machine when operating in an offline mode. By deploying Magistral Small locally, you can maintain the full reasoning capabilities of the model for your Atlas workflows, including detailed plan agent debugging, while keeping your code off third-party servers. This flexibility allows for a direct transition between cloud-based and local execution, depending on project requirements and security policies. ## Setup 1. Export your Mistral API key: `export MISTRAL_API_KEY='your_key_here'`. 2. Run `atlas models mistral` in your terminal and confirm that `magistral-small` is present in the list of available models. 3. Pin Magistral Small as your preferred model by adding or updating the model entry in your `atlas.json` configuration file: `"model": "mistral/magistral-small"`. 4. To optimize for cost and speed, run Atlas's plan agent on Magistral Small for reasoning, then switch to a faster model like Devstral for the build pass using `/models` in the TUI. ## FAQ ### What is the context window size for Magistral Small? Magistral Small offers a substantial 128,000 token context window, allowing for extensive input and output in Atlas workflows. ### How much does Magistral Small cost per token? Magistral Small is priced at $0.50 per 1M input tokens and $1.50 per 1M output tokens, making its reasoning traces accessible. ### Can I use Magistral Small offline with Atlas? Yes, Magistral Small's 24B open weights allow it to be pulled locally via Ollama, enabling offline work and keeping code off third-party servers when Atlas builds its code index with local Ollama embeddings. ### Why is Magistral Small beneficial for Atlas's plan agent? Magistral Small emits an explicit chain of thought, which transforms Atlas's plan agent from a summarizer into an actual step-by-step debugger, providing clear insights into the reasoning process. ### What is a key tradeoff when using Magistral Small? A key tradeoff is that reasoning tokens are billed as output, meaning a hard bug requiring extensive reasoning can cost more than the token count of the final diff suggests due to the $1.50 / 1M output token price. ### Is Magistral Small suitable for tasks requiring fast responses? No, Magistral Small has a slower first token compared to Mistral Small 3.2, making it a less optimal choice for tasks that demand rapid initial responses or when used as a `small_model`. ### How does Magistral Small compare to Magistral Medium in pricing? Magistral Small's input price of $0.50 per 1M input tokens is a quarter of the input price of Magistral Medium, offering a more cost-effective option for initiating reasoning tasks. --- Canonical HTML: https://seashell.sh/resources/models/magistral-small Source of truth: aeo_pages row `/resources/models/magistral-small` (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.