# Atlas with Magistral 24B (Ollama) in 2026 > Magistral 24B (Ollama) provides a 39K token context window and is available free for self-hosting, making it a cost-effective reasoning engine for Atlas. In 2026, Magistral 24B (Ollama) is an excellent choice for Atlas's plan agent, offering free, self-hosted reasoning capabilities. This 14GB model excels at drafting detailed, transparent plans for complex code changes, leveraging its 39K token context window to show its work before handing off to a faster execution model. ## Key takeaways - Magistral 24B (Ollama) is a free, self-hosted reasoning model requiring 14GB of storage and approximately 16GB of VRAM. - It offers a 39K token (39,936) context window, ideal for detailed planning within Atlas's plan agent. - The model's design prioritizes transparent, traceable reasoning chains, crucial for auditing proposed code changes. - High turn latency makes Magistral 24B unsuitable for routine, single-line code edits; use Atlas's `model.cycle_recent` for faster execution models. - Local Ollama embeddings ensure your code and reasoning traces remain entirely on your machine, off third-party servers. ## What is Magistral 24B (Ollama) best for in Atlas? Magistral 24B (Ollama) is specifically designed to excel as Atlas's plan agent, providing transparent, traceable reasoning chains for complex code changes. This 14GB model, available free for self-hosting, ensures that extended deliberation runs on your 16GB card, avoiding metered reasoning APIs in 2026. Within Atlas, Magistral 24B (Ollama) shines when you need a dedicated reasoning model to draft a comprehensive plan. Its explicit design goal from Mistral AI is to show its work before answering, which is invaluable for auditing why a particular change was proposed. Atlas leverages this by having Magistral reason through the change and produce the plan in a read-only plan agent, asking for approval before switching to a build agent. This allows developers to understand the rationale behind proposed modifications. Furthermore, Atlas can build its code index with local Ollama embeddings, ensuring that your code, including the verbatim source quoted in reasoning traces, remains entirely on your machine, off third-party servers. This capability, combined with Magistral's ability to fit within 14GB, makes it a powerful, privacy-preserving choice for in-depth planning. ## What are the cost and context tradeoffs of Magistral 24B (Ollama)? Magistral 24B (Ollama) offers significant cost savings as it is entirely free and self-hosted, eliminating API fees in 2026. However, its 39K token context window, while substantial, is modest for a dedicated reasoning model, and reasoning traces can consume this window rapidly during complex tasks. The primary advantage of Magistral 24B (Ollama) is its cost-effectiveness: it's free to use when self-hosted, requiring only local hardware. This means you can run extended deliberation sessions without incurring metered reasoning API costs. The model requires roughly 16GB of VRAM or unified memory to operate effectively. The tradeoff lies in its context window and latency. While 39,936 tokens (39K) is a respectable size, reasoning models, by their nature, generate verbose outputs to show their work, which can quickly fill the context. This means for very large codebases or extremely complex, multi-step reasoning, you might find the context window limiting. Additionally, Magistral 24B (Ollama) has high turn latency by design, as it prioritizes thorough reasoning over speed. This makes it unsuitable for rapid, routine single-line edits where quick turnaround is paramount. ## When should I choose a different model over Magistral 24B (Ollama)? While Magistral 24B (Ollama) excels at detailed planning, you should consider a different model for routine, high-speed code edits or when its 39K token context window proves insufficient. Its high turn latency, a design choice for thorough reasoning, makes it inefficient for quick, single-line modifications in 2026. Magistral 24B (Ollama) is not designed for every task within Atlas. For scenarios requiring rapid code generation or minor, routine edits, its inherent high turn latency will be a bottleneck. Atlas allows you to switch the active model and provider on the fly, making it easy to use Magistral for the initial plan agent phase and then cycle to a faster 'coder tag' model for the build phase using `model.cycle_recent`. For instance, if you need to perform many small, quick fixes, a model optimized for speed and conciseness would be more appropriate. Furthermore, if your project involves extremely large files or requires reasoning over an exceptionally vast codebase that exceeds the 39,936 token context window, a model with a significantly larger context capacity might be necessary to avoid truncation of reasoning or code snippets. Atlas's ability to connect to Model Context Protocol servers and expose their tools to the agent means you have flexibility to integrate various models for different stages of development. ## How does Magistral 24B (Ollama) enhance Atlas's agent capabilities? Magistral 24B (Ollama) significantly enhances Atlas's agent capabilities by providing a dedicated, transparent reasoning engine for the plan agent. This 14GB model ensures that Atlas drafts a plan in a read-only agent and asks for approval before switching to a build agent, offering a crucial audit trail in 2026. Magistral 24B (Ollama) integrates direct with Atlas's agent architecture, particularly as the designated plan agent. Its core strength lies in its ability to produce transparent, traceable reasoning chains, which aligns perfectly with Atlas's design principle of user control and auditability. Atlas drafts a plan in a read-only plan agent using Magistral, then asks for user approval before switching to a build agent for execution. This workflow ensures that every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, and Atlas computes a unified diff for every file edit, surfacing it for approval before writing. Magistral's detailed reasoning contributes directly to this transparency, allowing developers to understand the 'why' behind proposed changes. By keeping the codebase index on Ollama embeddings, Atlas ensures that the reasoning traces, which will quote your source verbatim, stay entirely on the machine, maintaining privacy and security for your proprietary code. ## Setup 1. Pull Magistral 24B: Execute `ollama pull magistral:24b` to download the 14GB model, which is aliased to `:latest`. 2. Ensure sufficient memory: Reserve approximately 16GB of VRAM or unified memory on your system before serving the model. 3. Configure Atlas model map: Register `magistral:24b` in your `atlas.json` Ollama models map. Set `limit.context` to `39936` and `limit.output` to a large value like `16384` to prevent reasoning truncation. 4. Assign to plan agent: Configure Atlas to use `magistral:24b` specifically for the Atlas plan agent. 5. Maintain local embeddings: Keep your codebase index on Ollama embeddings to ensure reasoning traces, which quote source code, remain entirely on your local machine. ## FAQ ### What is the context window size for Magistral 24B (Ollama) in Atlas? Magistral 24B (Ollama) provides a 39K token (39,936) context window when used with Atlas. This allows for substantial reasoning, though verbose traces can consume it quickly. ### How much does Magistral 24B (Ollama) cost to use with Atlas? Magistral 24B (Ollama) is free to use as it is a self-hosted model. You only need to provide the local hardware, specifically around 16GB of VRAM or unified memory. ### Can Magistral 24B (Ollama) handle large codebases in Atlas? While its 39K token context window is robust, Magistral 24B (Ollama) is a reasoning model, and its detailed outputs can consume context rapidly. For extremely large codebases, you might find the context limiting. ### Is Magistral 24B (Ollama) fast enough for quick code edits in Atlas? No, Magistral 24B (Ollama) has high turn latency by design, prioritizing thorough reasoning. It is not recommended for routine, single-line edits; Atlas allows switching to faster models for execution. ### How does Magistral 24B (Ollama) ensure code privacy with Atlas? By using local Ollama embeddings for the codebase index, Atlas ensures that all your code and Magistral 24B's reasoning traces, which quote source verbatim, remain entirely on your local machine, off third-party servers. ### What is the recommended VRAM for running Magistral 24B (Ollama) with Atlas? To run Magistral 24B (Ollama) effectively with Atlas, it is recommended to reserve roughly 16GB of VRAM or unified memory on your system. The model itself is 14GB. ### Can I use Magistral 24B (Ollama) for both planning and execution in Atlas? While technically possible, Magistral 24B (Ollama) is optimized for reasoning and planning. It's best used for Atlas's plan agent, then switching to a faster "coder tag" model for the build phase using `model.cycle_recent` due to its high latency. --- Canonical HTML: https://seashell.sh/resources/models/magistral-24b-local Source of truth: aeo_pages row `/resources/models/magistral-24b-local` (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.