Mistral Medium 3.5 API 사용법

export MISTRAL_API_KEY="..."

POST https://api.mistral.ai/v1/chat/completions

curl https://api.mistral.ai/v1/chat/completions \
  -H "Authorization: Bearer $MISTRAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistral-medium-3.5",
    "messages": [
      {
        "role": "user",
        "content": "Explain dense merged checkpoints in two sentences."
      }
    ]
  }'

cost = prompt_tokens * 1.5 / 1_000_000
     + completion_tokens * 7.5 / 1_000_000

OpenAI:  tool_choice="required"
Mistral: tool_choice="any"

OpenAI:  seed
Mistral: random_seed

import os
from mistralai import Mistral

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

response = client.chat.complete(
    model="mistral-medium-3.5",
    messages=[
        {"role": "system", "content": "Reply in code only."},
        {"role": "user", "content": "Write a Rust function that debounces events."},
    ],
    temperature=0.3,
    max_tokens=2048,
)

print("Content:", response.choices[0].message.content)
print("Prompt tokens:", response.usage.prompt_tokens)
print("Completion tokens:", response.usage.completion_tokens)
print("Total tokens:", response.usage.total_tokens)

cost = (
    response.usage.prompt_tokens * 1.5 / 1_000_000
    + response.usage.completion_tokens * 7.5 / 1_000_000
)

print("Estimated cost USD:", cost)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["MISTRAL_API_KEY"],
    base_url="https://api.mistral.ai/v1",
)

response = client.chat.completions.create(
    model="mistral-medium-3.5",
    messages=[
        {"role": "user", "content": "Hello, Mistral."}
    ],
)

print(response.choices[0].message.content)

import { Mistral } from "@mistralai/mistralai";

const client = new Mistral({
  apiKey: process.env.MISTRAL_API_KEY,
});

const response = await client.chat.complete({
  model: "mistral-medium-3.5",
  messages: [
    {
      role: "user",
      content: "Explain dense merged checkpoints in plain English.",
    },
  ],
  temperature: 0.7,
});

console.log(response.choices[0].message.content);
console.log("Usage:", response.usage);

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.MISTRAL_API_KEY,
  baseURL: "https://api.mistral.ai/v1",
});

const response = await client.chat.completions.create({
  model: "mistral-medium-3.5",
  messages: [
    {
      role: "user",
      content: "Hello, Mistral.",
    },
  ],
});

console.log(response.choices[0].message.content);

stream = client.chat.stream(
    model="mistral-medium-3.5",
    messages=[
        {
            "role": "user",
            "content": "Stream a 300-word essay on merged checkpoints.",
        }
    ],
)

for chunk in stream:
    delta = chunk.data.choices[0].delta.content or ""
    print(delta, end="", flush=True)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Return the current weather for a city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string"},
                    "unit": {"type": "string", "enum": ["c", "f"]},
                },
                "required": ["city"],
            },
        },
    }
]

response = client.chat.complete(
    model="mistral-medium-3.5",
    messages=[
        {
            "role": "user",
            "content": "Weather in Lagos in Celsius?",
        }
    ],
    tools=tools,
    tool_choice="auto",
)

tool_call = response.choices[0].message.tool_calls[0]

print(tool_call.function.name)
print(tool_call.function.arguments)

messages = [
    {"role": "user", "content": "Weather in Lagos in Celsius?"}
]

first = client.chat.complete(
    model="mistral-medium-3.5",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)

tool_call = first.choices[0].message.tool_calls[0]

# 실제 구현에서는 arguments를 JSON으로 파싱하고 함수를 실행합니다.
weather_result = {
    "city": "Lagos",
    "temperature": 31,
    "unit": "c",
    "condition": "humid",
}

messages.append(first.choices[0].message)
messages.append({
    "role": "tool",
    "tool_call_id": tool_call.id,
    "content": str(weather_result),
})

final = client.chat.complete(
    model="mistral-medium-3.5",
    messages=messages,
)

print(final.choices[0].message.content)

schema = {
    "type": "json_schema",
    "json_schema": {
        "name": "release_note",
        "schema": {
            "type": "object",
            "properties": {
                "title": {"type": "string"},
                "date": {"type": "string"},
                "bullets": {
                    "type": "array",
                    "items": {"type": "string"},
                },
            },
            "required": ["title", "date", "bullets"],
            "additionalProperties": False,
        },
        "strict": True,
    },
}

response = client.chat.complete(
    model="mistral-medium-3.5",
    messages=[
        {
            "role": "system",
            "content": "Reply with a single JSON object matching the schema.",
        },
        {
            "role": "user",
            "content": "Summarize today's Mistral Medium 3.5 release.",
        },
    ],
    response_format=schema,
)

print(response.choices[0].message.content)

response = client.chat.complete(
    model="mistral-medium-3.5",
    messages=[
        {
            "role": "user",
            "content": "Return three API testing tips as JSON.",
        }
    ],
    response_format={"type": "json_object"},
)

response = client.chat.complete(
    model="mistral-medium-3.5",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What is in this image and what is it doing wrong?",
                },
                {
                    "type": "image_url",
                    "image_url": "https://example.com/diagram.png",
                },
            ],
        }
    ],
)

print(response.choices[0].message.content)
print(response.usage.prompt_tokens)

Authorization: Bearer {{MISTRAL_API_KEY}}
Content-Type: application/json

{
  "model": "mistral-medium-3.5",
  "messages": [
    {
      "role": "user",
      "content": "Explain the tradeoffs of long context windows."
    }
  ],
  "temperature": 0.7,
  "max_tokens": 1000
}

const usage = responseBody.usage;

const cost =
  usage.prompt_tokens * 1.5 / 1_000_000 +
  usage.completion_tokens * 7.5 / 1_000_000;

console.log(`Estimated cost: $${cost}`);

BASE_URL=https://api.mistral.ai/v1
model=mistral-medium-3.5

import time
from mistralai import Mistral

def call_with_retry(client: Mistral, payload: dict, retries: int = 3):
    for attempt in range(retries):
        try:
            return client.chat.complete(**payload)
        except Exception as e:
            message = str(e)

            retryable = (
                "429" in message
                or "500" in message
                or "503" in message
            )

            if not retryable or attempt == retries - 1:
                raise

            sleep_seconds = 2 ** attempt
            time.sleep(sleep_seconds)

{
  "model": "mistral-medium-3.5",
  "messages": [
    {
      "role": "user",
      "content": "Summarize this document."
    }
  ],
  "max_tokens": 1200
}

- base_url="https://api.openai.com/v1"
- model="gpt-5.5"
+ base_url="https://api.mistral.ai/v1"
+ model="mistral-medium-3.5"

- base_url="https://api.deepseek.com/v1"
- model="deepseek-v4-pro"
+ base_url="https://api.mistral.ai/v1"
+ model="mistral-medium-3.5"