Mistral: Mistral 7B Instruct v0.1 – API Quickstart

Sample code and API for Mistral 7B Instruct v0.1

OpenRouter normalizes requests and responses across providers for you.

Get your API key

Create an API key from your OpenRouter dashboard and set it as an environment variable:

Make your first request

Use mistralai/mistral-7b-instruct-v0.1 with the OpenRouter API:

OpenRouter provides an OpenAI-compatible completion API to 400+ models & providers that you can call directly, or using the OpenAI SDK. Additionally, some third-party SDKs are available.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

Enable streaming

Add "stream": true to your request body to receive responses as server-sent events:

Endpoint

POSThttps://openrouter.ai/api/v1/chat/completions

AuthorizationBearer $OPENROUTER_API_KEY

Content-Typeapplication/json

HTTP-Refereroptional — your site URL, for rankings

X-Titleoptional — your site name, for rankings

Modelmistralai/mistral-7b-instruct-v0.1

Parameters

Name	Type	Default	Description
`max_tokens`	integer	—	This sets the upper limit for the number of tokens the model can generate in response.
`temperature`	float	`1`	This setting influences the variety in the model's responses.
`top_p`	float	`1`	This setting limits the model's choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P.
`top_k`	integer	`0`	This limits the model's choice of tokens at each step, making it choose from a smaller set.
`seed`	integer	—	If specified, the inferencing will sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
`repetition_penalty`	float	`1`	Helps to reduce the repetition of tokens from the input.
`frequency_penalty`	float	`0`	This setting aims to control the repetition of tokens based on how often they appear in the input.
`presence_penalty`	float	`0`	Adjusts how often the model repeats specific tokens already used in the input.
`min_p`	float	`0`	Represents the minimum probability for a token to be considered, relative to the probability of the most likely token.
`stop`	array	—	Stop generation immediately if the model encounter any token specified in the stop array.
`logit_bias`	map	—	Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100.