--- title: "Mistral AI API" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/mistral-1 source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:31:04.073Z license: CC-BY-4.0 attribution: "Mistral AI API — Claudary (https://claudary.paisolsolutions.com/skills/mistral-1)" --- # Mistral AI API import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Mistral AI API https://docs.mistral.ai/api/ ## API Key ```python # env variable os.environ['MISTRAL_API_KEY'] ``` ## Sample Usage ```python from litellm import completion import os os.environ['MISTRAL_API_KEY'] = "" response = completion( model="mistral/mistral-tiny", messages=[ {"role": "user", "content": "hello from litellm"} ], ) print(response) ``` ## Sample Usage - Streaming ```python from litellm import completion import os os.environ['MISTRAL_API_KEY'] = "" response = completion( model="mistral/mistral-tiny", messages=[ {"role": "user", "content": "hello from litellm"} ], stream=True ) for chunk in response: print(chunk) ``` ## Usage with LiteLLM Proxy ### 1. Set Mistral Models on config.yaml ```yaml model_list: - model_name: mistral-small-latest litellm_params: model: mistral/mistral-small-latest api_key: "os.environ/MISTRAL_API_KEY" # ensure you have `MISTRAL_API_KEY` in your .env ``` ### 2. Start Proxy ``` litellm --config config.yaml ``` ### 3. Test it ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Content-Type: application/json' \\ --data ' { "model": "mistral-small-latest", "messages": [ { "role": "user", "content": "what llm are you" } ] } ' ``` ```python import openai client = openai.OpenAI( api_key="anything", base_url="http://0.0.0.0:4000" ) response = client.chat.completions.create(model="mistral-small-latest", messages = [ { "role": "user", "content": "this is a test request, write a short poem" } ]) print(response) ``` ```python from langchain.chat_models import ChatOpenAI from langchain.prompts.chat import ( ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate, ) from langchain.schema import HumanMessage, SystemMessage chat = ChatOpenAI( openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy model = "mistral-small-latest", temperature=0.1 ) messages = [ SystemMessage( content="You are a helpful assistant that im using to make a test request to." ), HumanMessage( content="test from litellm. tell me why it's amazing in 1 sentence" ), ] response = chat(messages) print(response) ``` ## Supported Models :::info All models listed here https://docs.mistral.ai/platform/endpoints are supported. We actively maintain the list of models, pricing, token window, etc. [here](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). ::: | Model Name | Function Call | Reasoning Support | |----------------|--------------------------------------------------------------|-------------------| | Mistral Small | `completion(model="mistral/mistral-small-latest", messages)` | No | | Mistral Medium | `completion(model="mistral/mistral-medium-latest", messages)`| No | | Mistral Large 2 | `completion(model="mistral/mistral-large-2407", messages)` | No | | Mistral Large Latest | `completion(model="mistral/mistral-large-latest", messages)` | No | | **Magistral Small** | `completion(model="mistral/magistral-small-2506", messages)` | Yes | | **Magistral Medium** | `completion(model="mistral/magistral-medium-2506", messages)`| Yes | | Mistral 7B | `completion(model="mistral/open-mistral-7b", messages)` | No | | Mixtral 8x7B | `completion(model="mistral/open-mixtral-8x7b", messages)` | No | | Mixtral 8x22B | `completion(model="mistral/open-mixtral-8x22b", messages)` | No | | Codestral | `completion(model="mistral/codestral-latest", messages)` | No | | Mistral NeMo | `completion(model="mistral/open-mistral-nemo", messages)` | No | | Mistral NeMo 2407 | `completion(model="mistral/open-mistral-nemo-2407", messages)` | No | | Codestral Mamba | `completion(model="mistral/open-codestral-mamba", messages)` | No | | Codestral Mamba | `completion(model="mistral/codestral-mamba-latest"", messages)` | No | ## Function Calling ```python from litellm import completion # set env os.environ["MISTRAL_API_KEY"] = "your-api-key" tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, }, } ] messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] response = completion( model="mistral/mistral-large-latest", messages=messages, tools=tools, tool_choice="auto", ) # Add any assertions, here to check response args print(response) assert isinstance(response.choices[0].message.tool_calls[0].function.name, str) assert isinstance( response.choices[0].message.tool_calls[0].function.arguments, str ) ``` ## Reasoning Mistral does not directly support reasoning, instead it recommends a specific [system prompt](https://docs.mistral.ai/capabilities/reasoning/) to use with their magistral models. By setting the `reasoning_effort` parameter, LiteLLM will prepend the system prompt to the request. If an existing system message is provided, LiteLLM will send both as a list of system messages (you can verify this by enabling `litellm._turn_on_debug()`). ### Supported Models | Model Name | Function Call | |----------------|--------------------------------------------------------------| | Magistral Small | `completion(model="mistral/magistral-small-2506", messages)` | | Magistral Medium | `completion(model="mistral/magistral-medium-2506", messages)`| ### Using Reasoning Effort The `reasoning_effort` parameter controls how much effort the model puts into reasoning. When used with magistral models. ```python from litellm import completion import os os.environ['MISTRAL_API_KEY'] = "your-api-key" response = completion( model="mistral/magistral-medium-2506", messages=[ {"role": "user", "content": "What is 15 multiplied by 7?"} ], reasoning_effort="medium" # Options: "low", "medium", "high" ) print(response) ``` ### Example with System Message If you already have a system message, LiteLLM will prepend the reasoning instructions: ```python response = completion( model="mistral/magistral-medium-2506", messages=[ {"role": "system", "content": "You are a helpful math tutor."}, {"role": "user", "content": "Explain how to solve quadratic equations."} ], reasoning_effort="high" ) # The system message becomes: # "When solving problems, think step-by-step in tags before providing your final answer... # # You are a helpful math tutor." ``` ### Usage with LiteLLM Proxy You can also use reasoning capabilities through the LiteLLM proxy: ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Content-Type: application/json' \\ --data '{ "model": "magistral-medium-2506", "messages": [ { "role": "user", "content": "What is the square root of 144? Show your reasoning." } ], "reasoning_effort": "medium" }' ``` ```python import openai client = openai.OpenAI( api_key="anything", base_url="http://0.0.0.0:4000" ) response = client.chat.completions.create( model="magistral-medium-2506", messages=[ { "role": "user", "content": "Calculate the area of a circle with radius 5. Show your work." } ], reasoning_effort="high" ) print(response) ``` ### Important Notes - **Model Compatibility**: Reasoning parameters only work with magistral models - **Backward Compatibility**: Non-magistral models will ignore reasoning parameters and work normally ## Audio Transcription Use Mistral's Voxtral models for audio transcription via `litellm.transcription()`. ### SDK Usage ```python from litellm import transcription import os os.environ["MISTRAL_API_KEY"] = "" audio_file = open("path/to/audio.wav", "rb") response = transcription( model="mistral/voxtral-mini-latest", file=audio_file, ) print(response.text) ``` ### With Optional Parameters ```python response = transcription( model="mistral/voxtral-mini-latest", file=audio_file, language="en", temperature=0.0, response_format="json", ) ``` ### Mistral-Specific Parameters Mistral supports additional parameters beyond the OpenAI-compatible ones: | Parameter | Type | Description | |-----------|------|-------------| | `diarize` | `bool` | Enable speaker diarization | ```python response = transcription( model="mistral/voxtral-mini-latest", file=audio_file, diarize=True, ) ``` ### Usage with LiteLLM Proxy ```yaml model_list: - model_name: voxtral litellm_params: model: mistral/voxtral-mini-latest api_key: os.environ/MISTRAL_API_KEY model_info: mode: audio_transcription ``` ```bash litellm --config /path/to/config.yaml ``` ```bash curl --location 'http://0.0.0.0:4000/v1/audio/transcriptions' \\ --header 'Authorization: Bearer sk-1234' \\ --form 'file=@"audio.wav"' \\ --form 'model="voxtral"' ``` ## Sample Usage - Embedding ```python from litellm import embedding import os os.environ['MISTRAL_API_KEY'] = "" response = embedding( model="mistral/mistral-embed", input=["good morning from litellm"], ) print(response) ``` ## Supported Models All models listed here https://docs.mistral.ai/platform/endpoints are supported | Model Name | Function Call | |--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Mistral Embeddings | `embedding(model="mistral/mistral-embed", input)` | --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/mistral-1) · https://claudary.paisolsolutions.com