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Mistral AI's code-specialised API model for fill-in-the-middle completion, code correction and test generation, with a 256K-token context window.
Overview. Codestral 2508 is Mistral AI's closed-weight, API-only code model, purpose-built for fill-in-the-middle completion, code correction, and test generation. Released in August 2025, it ships with a 256K-token context window, making it a candidate for teams that need a coding-focused model to reason across large files or multi-file projects rather than short snippets. It is served exclusively through Mistral's hosted API — there are no public weights.
Key strengths. The model is tuned for the editor loop rather than for open-ended chat: it accepts text and code files as plain-text input and returns text, and it exposes tool calling and structured outputs for schema-constrained decoding. On Design Arena its Elo ranges from 1034 in game development to 1099 in 3D, with UI components at 1072. It is not a reasoning model — configurable reasoning effort is not offered here, and Mistral reserves that control for its separate hybrid and reasoning lines.
When to use it. Reach for Codestral 2508 for fill-in-the-middle autocomplete, targeted code correction, and automated test generation, especially where the task spans a large codebase and needs the full 256K context rather than a single file. Its interactive latency profile and tool-calling support also make it a reasonable fit for chat-assisted coding sessions that call external tools or expect structured, JSON-schema-validated responses. It is not positioned as a general-purpose reasoning or extended-thinking model, so keep it scoped to code-centric tasks.
Limitations. Mistral has not disclosed parameter count, architecture, or licence for this version across repeated verification passes, so those remain unknown to customers. It has no vision input — the 'file' input type covers code, text, and PDFs read as plain text, not image understanding — and its Design Arena code-quality signal is mid-tier and has been declining over recent cycles, so it should not be treated as a frontier coding model.
Sovereignty note. Codestral 2508 is built by Mistral AI, a French company, and is available only as a hosted API, with no public weights. Where requests run, and whether inputs are retained or used for training, depends on the endpoint serving it; see the endpoint facts on this page.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L1 | €0.30 / 0.90 | Online |
13 use cases classified by the curator. The note says why.
Code correction is an explicit target workload, and the 256K window allows the model to ingest stack traces, surrounding modules, and recent diffs in one pass.
Codestral-2508 is built for fill-in-the-middle completion, offering a 256K context window and prompt-cache support so it can hold large files and cross-file context without re-billing the prefix on every keystroke.
Long-context code generation and correction are the design centre of this model; 256K tokens covers full-file and multi-file review passes that would truncate on shorter-window code models.
Refactoring needs whole-file coherence; Codestral-2508's 256K context plus prompt-caching of the stable prefix is a direct architectural fit for large mechanical rewrites.
Test generation is a stated purpose of this code specialist, and its 256K window lets it read the surrounding module or repository slice before writing assertions.
Tool-calling support plus a 256K window make it viable as a coding agent, but this is a completion/correction-oriented model rather than a long-horizon planner, so pair it with an explicit orchestration layer.
A 256K window with prompt-caching suits grounding answers over large retrieved contexts, though a general-purpose long-context model would likely read prose documents better.
A capable code-specialist model with enough context to explain large files, though explanation is a secondary use case relative to its generation and correction focus.
The 256K context window comfortably fits long documents, but the model is code-tuned, so extractive QA over prose is competent rather than class-leading.
Native tool-calling support makes it usable for function-calling backends, with the caveat that its tuned strength is code rather than general conversational tool use.
Tool-calling and a 256K context give it room to carry state across steps, but no published reasoning or planning benchmarks differentiate it from general-purpose alternatives here.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistral-ai/codestral-2508",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at