Mistral Code Agent
Mistral AI
At a glance
- Price per 1M tokens (input)
- $0.40
- Price per 1M tokens (output)
- $2.00
- Context
- 256,000 tokens
- Free access
- yes (see below)
Refreshed daily; data verified September 1, 2026. Published September 1, 2026.
The Mistral Code Agent is designed for developers and data‑science teams that need an AI that can interact with external software through tool calling, a capability that lets the model invoke functions or APIs as part of its reasoning. It excels at automating code generation, debugging assistance, data pipeline orchestration, and interactive query answering where live system access is required. Mistral AI’s approach embeds the tool‑calling logic directly into the model’s inference loop, allowing tighter integration and more reliable execution compared with models that treat external calls as post‑processing steps. The model also supports safe fallback to natural language responses when a suitable tool is unavailable, making it adaptable for mixed workloads.
Specifications & pricing
| Input (per 1M tokens) | $0.40 |
|---|---|
| Output (per 1M tokens) | $2.00 |
| Context window | 256,000 tokens |
| Max output | 256,000 tokens |
| Capabilities | tool calling |
LiteLLM community dataset (MIT), verified September 1, 2026. Official Mistral AI pricing.
What Mistral Code Agent would cost on your workload — run it through the cost calculator →
Where to try Mistral Code Agent for free
- Mistral AI offers a free chat — Le Chat (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What kinds of tasks can the Code Agent handle better than a standard language model?+
The model shines when a task requires calling external services, such as generating code snippets that are immediately tested, retrieving up‑to‑date data from a database, or orchestrating multi‑step workflows that involve system commands, because it can embed those calls directly into its reasoning process.
How do we integrate the model with our existing toolchain?+
Integration is done by defining function specifications that describe the inputs and outputs of your services; the model receives these definitions at runtime and can request execution of the appropriate function, returning the result as part of its response, which fits naturally into API‑first architectures.
In what ways does this model differ from other Mistral models?+
Unlike the baseline conversational models, the Code Agent includes a built‑in interpreter for tool calling, enabling it to trigger external code paths rather than only generating text, and it is tuned with examples that emphasize reliable function selection and error handling.
What are the main limitations we should be aware of?+
The model depends on accurate function definitions and reliable external services; if a called tool fails or returns unexpected data, the model may need additional prompting to recover, and it does not replace thorough testing of generated code before deployment.
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