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AI model by Mistral AI

Codestral 25.08 for business

Mistral's low-latency coding model for code completion and generation, with fill-in-the-middle support, tool calling and a 128K window.

  • Reviewed on September 24, 2026
  • Mistral AI

Capability tiersRelative, not benchmarks

Reasoning Low
Coding High
Vision Very low
Speed Very high
Price level Lowest
Context size Medium
Tool calling High
Structured output High

Key facts about Codestral 25.08

Model ID at review
codestral-2508
Provider
Mistral AI
Main category
Coding
Open weights
No, available as a hosted service
Last reviewed
September 24, 2026

Fit

Where it fits and where it does not

Good at

  • Fast code completion inside editors.

  • Fill-in-the-middle, completing code between existing lines.

  • Generating functions and tests.

  • Tool calling and structured outputs.

  • Low cost per request.

Not the right choice for

  • Large, multi-file agent changes, where bigger models do better.

  • Image input.

  • Self-hosting, since it is not listed as open weight.

  • General business chat.

Use cases

Business use cases we would use it for

Editor completion

Powering code completion in internal developer tools.

Test drafting

Drafting unit tests that developers review.

Query helpers

Suggesting queries and scripts in internal tools, with checks.

Our notes

When we would choose it

Codestral 25.08 is Mistral's current Codestral model, released in July 2025. It is built for low-latency code completion and generation, including fill-in-the-middle, where the model completes code between existing lines rather than only at the end. It supports tool calling and structured outputs, and the official card lists a 128K token context window.

Speed matters for code completion. A suggestion that arrives while the developer is still typing is useful. One that arrives two seconds later is ignored. That is why smaller, faster coding models still have a place next to large models such as Claude Opus or GPT-6 Sol.

For larger tasks, such as changing a feature across many files, we would use a stronger model in an agent setup and keep Codestral for completion. In our own work, every AI-generated change is reviewed by an engineer. See AI-accelerated development.

Codestral is listed under Mistral's commercial license rather than as open weights, so it is used through the API. If you need a coding model you can host yourself, look at Qwen3-Coder or Mistral Small 4.

When building completion into an internal tool, we would measure acceptance rate: how often developers keep the suggestion. It is the simplest and most honest measure of whether a coding model helps, and it is easy to track over time. See AI copilots.

Before you commit

Things to check before you commit

  • Context

    The official card lists 128K tokens. Some third-party sites say more.

  • License

    It is listed under Mistral's commercial license, not as open weight.

  • Code privacy

    Check data terms before sending private code.

  • Your languages

    Test with your own languages and frameworks.

Alternatives

Models to compare it with

Qwen3-Coder

Qwen's open-weight coding models under Apache 2.0, from the efficient Qwen3-Coder-Next to the large 480B model, with tool use and long context.

Mistral Medium 3.5

A newer open-weight Mistral model for agent and coding work, with image input, tool calling, structured outputs and a 256K window.

Claude Opus 5.5

Anthropic's recommended starting point for most serious work: long-running agentic coding and knowledge work, with a 1M token context window.

Keep exploring

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