Magistral 24B (Ollama) is Mistral's reasoning model, 14GB, built to show its work before answering. Inside Atlas it earns its keep as the plan agent: let Magistral reason through the change, produce the plan, then hand the file edits to a faster coder tag. The context window is 39K tokens (39,936) and the pricing is Free (self-hosted). Because Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, a dedicated local reasoner slots into an existing seam rather than requiring a new one. Turn latency is high by design, so do not put Magistral 24B on routine single-line edits.
Why use Magistral 24B (Ollama) as the Atlas plan agent?
Magistral 24B (Ollama) fits the Atlas plan agent because Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent. Magistral 24B is a dedicated reasoning model in 14GB, so extended deliberation runs on a 16GB card rather than a metered reasoning API.
Most agent setups pay for reasoning in the wrong place. They put one strong model in the loop and let it think hard about every trivial edit, which is slow and, on a hosted provider, expensive. Atlas already separates the two phases: the plan agent is read-only, and Atlas asks before switching to a build agent. Magistral 24B belongs in that first phase. Let Magistral reason through the change, produce the plan, then hand the file edits to a faster coder tag. Because Magistral 24B has the same 14GB footprint as mistral-small:24b, you can register both and switch between reasoning and execution with /models, and Atlas lets you switch the active model and provider on the fly with favorites and recents.
Is 39K of context enough for a reasoning model in Atlas?
Magistral 24B (Ollama) has a 39K token (39,936) context, which is modest for a reasoning model, and reasoning traces consume that window fast. Register magistral:24b with limit.context 39936 and a large limit.output (16384) so the reasoning is not truncated mid-chain.
A reasoning model spends context on itself. Every deliberation step Magistral 24B writes is text in the window, so a 39K budget that would be comfortable for a plain instruct model is tighter here than the number suggests. The configuration that follows from that is specific: limit.context 39936 and limit.output 16384, because a truncated reasoning chain is worse than no reasoning at all. It also argues for narrow, well-retrieved input. Atlas indexes code by AST declarations using tree-sitter, not blind line windows, and searches with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, so the code Magistral 24B reasons over is dense rather than padded, which is exactly what a context-constrained reasoner needs.
How much does Magistral 24B cost, and what hardware does it need?
Magistral 24B (Ollama) is Free (self-hosted). Reserve roughly 16GB of VRAM or unified memory before serving the 14GB model. That means extended deliberation runs on a 16GB card rather than a metered reasoning API, where thinking tokens are billed like any other output.
Hosted reasoning models charge for the tokens they think with, and an agent that reasons before every plan can spend a surprising amount on text no human ever reads. Magistral 24B removes that meter entirely: the pricing is Free (self-hosted), and the hardware ask is roughly 16GB of VRAM or unified memory, which is a mainstream consumer card in 2026. Pull it with ollama pull magistral:24b (14GB, aliased to :latest). Keep the codebase index on Ollama embeddings so the reasoning traces, which will quote your source verbatim, stay entirely on the machine. That last point matters more for a reasoning model than for a plain one, because the trace itself is a detailed reconstruction of your code.
When should you pick a different model than Magistral 24B?
Pick a different model than Magistral 24B (Ollama) for routine work. Magistral 24B has high turn latency by design, so do not put it on routine single-line edits. A coder tag will finish a small diff in the time Magistral 24B spends deliberating about it.
Magistral 24B is the wrong default and the right specialist. The failure mode is using it as your only model: every rename, every import fix, every one-line guard clause goes through a full reasoning chain, and the session feels glacial. The correct pattern is to switch. Use Magistral 24B for the Atlas plan agent and switch to a coder tag for the build phase with model.cycle_recent. The second reason to look elsewhere is context: 39K tokens (39,936) is a real ceiling for a reasoning model, and if your plans routinely span many files, a longer-window model may serve the planning phase better even if it reasons less transparently.
Can you audit why Atlas proposed a change with Magistral 24B?
Yes. Magistral 24B (Ollama) produces transparent, traceable reasoning chains, which is Mistral's explicit design goal and useful when you need to audit why a change was proposed. Atlas then computes a unified diff for every file edit and surfaces it for approval before writing.
Auditability is the reason to accept Magistral 24B's latency. In a regulated or high-stakes repository, the question after an agent makes a change is not only what changed but why the agent thought it should. Magistral 24B's reasoning chain answers that in the model's own words, and Atlas supplies the mechanical half of the audit trail: every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, and Atlas snapshots file changes as git patches so edits can be diffed and rolled back. Running all of that locally on a 14GB model with Free (self-hosted) pricing means the audit trail, including the reasoning, never leaves your machine.
Setup
- 01Pull it: ollama pull magistral:24b (14GB, aliased to :latest)
- 02Reserve roughly 16GB of VRAM or unified memory before serving
- 03Register magistral:24b in the atlas.json ollama models map with limit.context 39936 and a large limit.output (16384) so reasoning is not truncated
- 04Use it for the Atlas plan agent and switch to a coder tag for the build phase with model.cycle_recent
- 05Keep the codebase index on Ollama embeddings so the reasoning traces, which will quote your source verbatim, stay entirely on the machine
Frequently asked questions
- what is the context window of magistral 24b
- Magistral 24B has a 39K token (39,936) context window. That is modest for a reasoning model, and reasoning traces consume it fast, so set limit.output to 16384 to avoid truncation.
- how much VRAM does magistral 24b need
- Reserve roughly 16GB of VRAM or unified memory to serve the 14GB Magistral 24B weights. Pricing is Free (self-hosted).
- can I use a local reasoning model as a planner in a coding agent
- Yes. Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, so Magistral 24B can reason through the change and produce the plan, then hand file edits to a faster coder tag.
- magistral vs mistral small 24b
- Magistral 24B is Mistral's reasoning model and mistral-small:24b is the general instruct model. They share the same 14GB footprint, so you can register both and switch between reasoning and execution with /models.
- why is magistral 24b slow
- Turn latency on Magistral 24B is high by design, because the model deliberates and shows its work before answering. Do not put it on routine single-line edits.
- how do I switch models mid-session in atlas
- Use model.cycle_recent or the /models dialog. Atlas lets you switch the active model and provider on the fly with favorites and recents, which is how you move from Magistral 24B planning to a coder tag for the build phase.
- do local reasoning traces stay private
- They do if you keep the codebase index on Ollama embeddings, so the reasoning traces, which will quote your source verbatim, stay entirely on the machine along with the Magistral 24B inference itself.
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