LIVE
Google Introduces "Gemini 4 Carbon" as Next Step in Its Model Lineup28/10/26 · Google|DeepMind unveils Genie 4, a new generation of world model27/10/26 · Google DeepMind|Anthropic files IPO prospectus, with risk disclosures taking center stage26/10/26 · Anthropic|Robot policy trained only in simulation matches top real-world systems26/10/26|OpenAI unveils GPT-6.1 Terra, a model built for computer use26/10/26 · OpenAI|Google's AI gives evasive answers about who won the 2020 US election28/10/26 · Google|NVIDIA introduces Nemotron 3.5 Transcribe, a promptable multilingual transcription model28/10/26 · NVIDIA|OpenAI and ServiceNow deepen partnership on enterprise workflows28/10/26 · OpenAI|Failed startups' data finds a second life training AI models28/10/26|Anthropic expands AI for Science program to 20 foundations and research institutions28/10/26 · Anthropic|Anthropic shares early results on using Claude to accelerate drug development27/10/26 · Anthropic|GEPA-Curator: Filtering Failures to Better Train LLM Agents27/10/26|Google Introduces "Gemini 4 Carbon" as Next Step in Its Model Lineup28/10/26 · Google|DeepMind unveils Genie 4, a new generation of world model27/10/26 · Google DeepMind|Anthropic files IPO prospectus, with risk disclosures taking center stage26/10/26 · Anthropic|Robot policy trained only in simulation matches top real-world systems26/10/26|OpenAI unveils GPT-6.1 Terra, a model built for computer use26/10/26 · OpenAI|Google's AI gives evasive answers about who won the 2020 US election28/10/26 · Google|NVIDIA introduces Nemotron 3.5 Transcribe, a promptable multilingual transcription model28/10/26 · NVIDIA|OpenAI and ServiceNow deepen partnership on enterprise workflows28/10/26 · OpenAI|Failed startups' data finds a second life training AI models28/10/26|Anthropic expands AI for Science program to 20 foundations and research institutions28/10/26 · Anthropic|Anthropic shares early results on using Claude to accelerate drug development27/10/26 · Anthropic|GEPA-Curator: Filtering Failures to Better Train LLM Agents27/10/26|
Toolofficial publisher

code_interpreter (Mistral)

Tool : A precise action the AI can call · requires mistral-api

Python sandbox for Mistral agents.

The European option for code execution.

Overall score
6.7 /10
Security: one caveat, see the card
Utility
7.0
Reliability
6.0
Security
One caveat
Ease
1 click · Comfortable

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

When an agent needs to perform calculations, analyze data, or produce charts, it cannot do so from its language model alone. Mistral's code_interpreter tool solves this by allowing the agent to execute Python code in an isolated environment, directly on the server side.

In practice, the agent receives a request, generates Python code, sends it to the tool which runs it in a secure sandbox, then retrieves the result or the produced chart. All execution takes place within Mistral's internal infrastructure, without any code running on the user's machine.

This tool is aimed at developers building agents via the Mistral API who need to automate data analysis, visualization, or computation tasks. It is also accessible directly through the Conversations interface, without needing to create an agent beforehand.

However, code_interpreter is not available in the classic Chat Completions API. It is also not suitable for users who want to run code in their own local environment, as execution is entirely managed on Mistral's side.

What it offers

  • ·Python code execution in an isolated server-side sandbox (paid).
  • ·Chart and visualization generation from provided data (paid).
  • ·Available as a built-in tool, ready to use without additional configuration (paid).
  • ·Usable directly via the Conversations interface without prior agent creation (paid).
  • ·Compatible with the Conversations API and the Agents API (paid).
  • ·Can be combined with other built-in tools such as web search or image generation within the same agent (paid).

In practice

Price
Paid · from 28 €
Commercial use
Commercial use: yes
Available on
api
Host
mistral-api
FR interface
No

Security and data

Rating
Security: one caveat, see the card
Capabilities
readswritesexecutesnetwork
Your data
Data with the vendor
Maintenance
Active

Links and repository checked on 25 October 2026 · added on 1 May 2025

How to install code_interpreter (Mistral)

1. Create an account and obtain an API key on the Mistral platform. 2. Install the Mistral Python client in your development environment. 3. When creating an agent via client.beta.agents.create(), add code_interpreter to the tools parameter. 4. You can also call the tool directly via the Conversations API without creating an agent. 5. Never include your API key in shared or public code; store it in an environment variable.
tools=[{"type":"code_interpreter"}]

Official documentation ↗

In the same category

Whole category Code →