Mistral AI

Documentation Index

Fetch the complete documentation index at: https://docs.pandaprobe.com/llms.txt

Use this file to discover all available pages before exploring further.

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Installation

pip install "pandaprobe[mistral]"

uv add "pandaprobe[mistral]"

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Setup

from pandaprobe.wrappers import wrap_mistral
from mistralai.client import Mistral

client = wrap_mistral(Mistral(api_key="..."))

from pandaprobe.wrappers import wrap_mistral
from mistralai.client import Mistral

# The Mistral SDK exposes both sync and async on the same client.
client = wrap_mistral(Mistral(api_key="..."))

response = await client.chat.complete_async(
    model="mistral-small-latest",
    messages=[{"role": "user", "content": "Hello!"}],
)

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Chat API

response = client.chat.complete(
    model="mistral-small-latest",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Explain recursion in one sentence."},
    ],
    temperature=0.5,
    max_tokens=200,
)

• Input: messages list, captured directly (Mistral already speaks the universal schema)
• Output: assistant message from choices[0].message
• Model name (from the response body)
• Token usage
• Model parameters: temperature, top_p, max_tokens, random_seed, safe_prompt, response_format, tool_choice, presence_penalty, frequency_penalty, n, stop

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Streaming

res = client.chat.stream(
    model="mistral-small-latest",
    messages=[{"role": "user", "content": "Hello!"}],
)
with res as event_stream:
    for event in event_stream:
        delta = event.data.choices[0].delta
        if delta.content:
            print(delta.content, end="")

​

Async

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Token usage mapping