Coding

Large language models (LLMs) generate text effectively, and they also perform well at code generation: from code completion to agentic tool use for semi-automated software development.

We provide two major model families for coding:

Codestral: trained for code generation and fill-in-the-middle (FIM) completion.
Devstral: trained for agentic tool use in software development.

We also provide Codestral Embed for semantic search and embedding code databases, repositories, and retrieval-powered coding assistants. Learn more in the code embeddings guide.

Before you start

Endpoints and models

We provide two main endpoints:

FIM: https://api.mistral.ai/v1/fim/completions
- for code completion and code generation; supporting codestral-latest.
Instruction following: https://api.mistral.ai/v1/chat/completions
- for coding and agentic tool use; supporting codestral-latest, devstral-small-latest and devstral-medium-latest.

FIM and instruction following

Use coding specialized models

Below you can find how to use our coding dedicated models, from code completion to instruction following and agentic models.

With this feature, users can define the starting point of the code using a prompt, and the ending point of the code using an optional suffix and an optional stop. The FIM model will then generate the code that fits in between, making it ideal for tasks that require a specific piece of code to be generated.

tip

We also provide the min_tokens and max_tokens sampling parameters, which are particularly useful for code generation as it allows you to set the minimum and maximum number of tokens that should be produced. This is especially useful when FIM models decide to produce no tokens at all, or are overly verbose, allowing developers to enforce completions within a specific range if they are needed.

Codestral

Codestral is a generative model designed and optimized for code generation tasks, including fill-in-the-middle and code completion. Codestral was trained on 80+ programming languages, so it performs well on both common and less common languages.

note

We offer two domains for Codestral endpoints, both providing FIM and instruct routes:

Domain	Use case
codestral.mistral.ai	For individual users and IDE plugins. Requires a separate Codestral API key.
api.mistral.ai	For production and business use. Uses your existing API key.

Which endpoint should you use?

If you're querying Codestral as part of an IDE plugin, use codestral.mistral.ai.
If you're building a plugin that expects users to bring their own API keys, also target codestral.mistral.ai.
For all other use cases, api.mistral.ai is better suited.

This guide uses api.mistral.ai for demonstration.

Below we present three approaches to using Codestral for code generation.

Originally, these models are designed to complete code in-between 2 points: a prefix (here called prompt) and a suffix, generating the code in-between.

import os
from mistralai.client import Mistral

api_key = os.environ["MISTRAL_API_KEY"]
client = Mistral(api_key=api_key)

model = "codestral-latest"
prompt = "def fibonacci(n: int):"
suffix = "n = int(input('Enter a number: '))\nprint(fibonacci(n))"

response = client.fim.complete(
    model=model,
    prompt=prompt,
    suffix=suffix,
    temperature=0,
    # min_tokens=1, # Uncomment to enforce completions to at least 1 token
)

import os
from mistralai.client import Mistral

api_key = os.environ["MISTRAL_API_KEY"]
client = Mistral(api_key=api_key)

model = "codestral-latest"
prompt = "def fibonacci(n: int):"
suffix = "n = int(input('Enter a number: '))\nprint(fibonacci(n))"

response = client.fim.complete(
    model=model,
    prompt=prompt,
    suffix=suffix,
    temperature=0,
    # min_tokens=1, # Uncomment to enforce completions to at least 1 token
)

The prefix + completion + suffix will correspond to the full code.

# prefix
def fibonacci(n: int):
  # completion
  if n == 0:
    return 0
  elif n == 1:
    return 1
  else:
    return fibonacci(n-1) + fibonacci(n-2)
# suffix
n = int(input('Enter a number: '))
print(fibonacci(n))

# prefix
def fibonacci(n: int):
  # completion
  if n == 0:
    return 0
  elif n == 1:
    return 1
  else:
    return fibonacci(n-1) + fibonacci(n-2)
# suffix
n = int(input('Enter a number: '))
print(fibonacci(n))

Integrations

IDE integrations

If you want to use our models in your favorite IDE, you can find integrations below.

Mistral Vibe

Mistral Vibe is a command-line coding assistant powered by Mistral models. It provides a conversational interface to your codebase, letting you explore, modify, and interact with your projects using natural language.

Learn more in the Mistral Vibe CLI guide or visit the GitHub repository.

Integration with continue.dev

Continue.dev supports both Codestral base for code generation and Codestral Instruct for chat.

How to set up Codestral with Continue

Here is a step-by-step guide on how to set up Codestral with Continue using the Mistral AI API:

Install the Continue VS Code or JetBrains extension following the instructions here. Please make sure you install Continue version >v0.8.33.
Automatic set up:

Click on the Continue extension iron on the left menu. Select Mistral API as a provider, select Codestral as a model.
Click "Get API Key" to get Codestral API key.
Click "Add model", which will automatically populate the config.json.

(alternative) Manually edit config.json

Click on the gear icon in the bottom right corner of the Continue window to open ~/.continue/config.json (MacOS) / %userprofile%\.continue\config.json (Windows)
Log in and request a Codestral API key in Studio›Codestral ↗
To use Codestral as your model for both autocomplete and chat, replace [API_KEY] with your Mistral API key below and add it to your config.json file:

{
  "models": [
    {
      "title": "Codestral",
      "provider": "mistral",
      "model": "codestral-latest",
      "apiKey": "[API_KEY]"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Codestral",
    "provider": "mistral",
    "model": "codestral-latest",
    "apiKey": "[API_KEY]"
  }
}

{
  "models": [
    {
      "title": "Codestral",
      "provider": "mistral",
      "model": "codestral-latest",
      "apiKey": "[API_KEY]"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Codestral",
    "provider": "mistral",
    "model": "codestral-latest",
    "apiKey": "[API_KEY]"
  }
}

If you run into any issues or have any questions, please join our Discord and post in #help channel here

Integration with Tabnine

Tabnine supports Codestral Instruct for chat.

How to set up Codestral with Tabnine

What is Tabnine Chat?

Tabnine Chat is a code-centric chat application that runs in the IDE and allows developers to interact with Tabnine’s AI models in a flexible, free-form way, using natural language. Tabnine Chat also supports dedicated quick actions that use predefined prompts optimized for specific use cases.

Getting started

To start using Tabnine Chat, first launch it in your IDE (VSCode, JetBrains, or Eclipse). Then, learn how to interact with Tabnine Chat, for example, how to ask questions or give instructions. Once you receive your response, you can read, review, and apply it within your code.

Selecting Codestral as Tabnine Chat App model

In the Tabnine Chat App, use the model selector to choose Codestral.

Integration with LangChain

LangChain provides support for Codestral Instruct. Here is how you can use it in LangChain:

# make sure to install `langchain` and `langchain-mistralai` in your Python environment

import os
from langchain_mistralai import ChatMistralAI
from langchain_core.prompts import ChatPromptTemplate 

api_key = os.environ["MISTRAL_API_KEY"]
mistral_model = "codestral-latest"
llm = ChatMistralAI(model=mistral_model, temperature=0, api_key=api_key)
llm.invoke([("user", "Write a function for fibonacci")])

# make sure to install `langchain` and `langchain-mistralai` in your Python environment

import os
from langchain_mistralai import ChatMistralAI
from langchain_core.prompts import ChatPromptTemplate 

api_key = os.environ["MISTRAL_API_KEY"]
mistral_model = "codestral-latest"
llm = ChatMistralAI(model=mistral_model, temperature=0, api_key=api_key)
llm.invoke([("user", "Write a function for fibonacci")])

For a more complex use case of self-corrective code generation using the instruct Codestral tool use, check out this notebook and this video:

Integration with LlamaIndex

LlamaIndex provides support for Codestral Instruct and Fill In Middle (FIM) endpoints. Here is how you can use it in LlamaIndex:

# make sure to install `llama-index` and `llama-index-llms-mistralai` in your Python enviornment

import os
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI

api_key =  os.environ["MISTRAL_API_KEY"]
mistral_model = "codestral-latest"
messages = [
    ChatMessage(role="user", content="Write a function for fibonacci"),
]
MistralAI(api_key=api_key, model=mistral_model).chat(messages)

# make sure to install `llama-index` and `llama-index-llms-mistralai` in your Python enviornment

import os
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI

api_key =  os.environ["MISTRAL_API_KEY"]
mistral_model = "codestral-latest"
messages = [
    ChatMessage(role="user", content="Write a function for fibonacci"),
]
MistralAI(api_key=api_key, model=mistral_model).chat(messages)

Check out more details on using Instruct and Fill In Middle(FIM) with LlamaIndex in this notebook.

Integration with Jupyter AI

Jupyter AI seamlessly integrates Codestral into JupyterLab, offering users a streamlined and enhanced AI-assisted coding experience within the Jupyter ecosystem. This integration boosts productivity and optimizes users' overall interaction with Jupyter.

To get started using Codestral and Jupyter AI in JupyterLab, first install needed packages in your Python environment:

pip install jupyterlab langchain-mistralai jupyter-ai pandas matplotlib

pip install jupyterlab langchain-mistralai jupyter-ai pandas matplotlib

Then launch Jupyter Lab:

jupyter lab

jupyter lab

Afterwards, you can select Codestral as your model of choice, input your Mistral API key, and start coding with Codestral!

Integration with JupyterLite

JupyterLite is a project that aims to bring the JupyterLab environment to the web browser, allowing users to run Jupyter directly in their browser without the need for a local installation.

You can try Codestral with JupyterLite in your browser:

Integration with Tabby

Tabby is an open-source AI coding assistant. You can use Codestral for both code completion and chat via Tabby.

To use Codestral in Tabby, configure your model configuration in ~/.tabby/config.toml as follows.

[model.completion.http]
kind = "mistral/completion"
api_endpoint = "https://api.mistral.ai"
api_key = "secret-api-key"

[model.completion.http]
kind = "mistral/completion"
api_endpoint = "https://api.mistral.ai"
api_key = "secret-api-key"

You can check out Tabby's documentation to learn more.

Integration with E2B

E2B provides open-source secure sandboxes for AI-generated code execution. With E2B, it is easy for developers to add code interpreting capabilities to AI apps using Codestral.

In the following examples, the AI agent performs a data analysis task on an uploaded CSV file, executes the AI-generated code by Codestral in the sandboxed environment by E2B, and returns a chart, saving it as a PNG file.

Python implementation (cookbook):

JS implementation (cookbook):