Mistral AIChat

Mistral 7B Instruct (Free)

mistral-7b-instruct:free

Mistral 7B Instruct (Free) by Mistral AI.

Context: 32.8K tokens
Endpoint

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Pricing

Input$0 / 1M

Output$0 / 1M

Cache Write$0 / 1M

Cache Read$0 / 1M

Prompt cache writes and reads are included at no additional cost.

Quick Start

Select an endpoint and copy a working example for this model.

Endpoint:

python

from openai import OpenAI client = OpenAI(    api_key="YOUR_API_KEY",    base_url="https://api.apertis.ai/v1") response = client.chat.completions.create(    model="mistral-7b-instruct:free",    messages=[        {"role": "user", "content": "Hello!"}    ],    max_tokens=1024,    temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(#     model="mistral-7b-instruct:free",#     messages=[{"role": "user", "content": "Hello!"}],#     extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )

Supported Parameters

API docs

Common7 params

modelmessagesmax_tokenstemperaturetop_pstreamtools

Extended4 params

reasoning_effortstream_optionsthinkingextra_body

Cursor IDE Model IDs

Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.

mistral-7b-instruct:free

Decision guidance

Fit the request contract before production.

Use this model when

The workload fits the text or chat tasks shown in this model's current catalog record.
The input fits within the published 32.8K-token context record, with output and system overhead budgeted separately.

Check before production

Confirm the production client uses one of the listed request surfaces: /v1/chat/completions, /v1/responses, /v1/messages.
Estimate a representative request from the current Free price fields instead of extrapolating from a tiny prompt.
Check the observed-availability card and run your own timeout, retry, and fallback test before relying on the model.

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