What is Mistral: Mixtral 8x22B Instruct?

Sparse MoE LLM with 39B active parameters from 141B total. Excels in math, coding, multilingual tasks. Instruct version fine-tuned for instructions.

How to access Mistral: Mixtral 8x22B Instruct API?

Use our LLM endpoint with standard chat completion format. Supports 64K context and function calling. Deploy via API key instantly.

What context length for mistral mixtral 8x22b instruct?

64K tokens standard, up to 65K reported. Handles large documents for recall. Matches dense 70B models in speed.

Is Mistral: Mixtral 8x22B Instruct model good for coding?

Tops HumanEval benchmarks. Generates clean code with reasoning. Native function calling aids tool use.

Best Mistral: Mixtral 8x22B Instruct alternative?

Offers top open performance-to-cost ratio. Faster than dense 70B models. Use for math (90% GSM8K) and multilingual apps.

mistral: mixtral 8x22b instruct API pricing?

Cost-efficient due to sparse activation. Check endpoint for token rates. Scales for production via mistral mixtral 8x22b instruct api.

Available now on ModelsLab · Language Model

Mistral: Mixtral 8x22B Instruct
Sparse Power, Dense Results

Try Mistral: Mixtral 8x22B Instruct API Documentation

Run Mixtral Efficiently

MoE Architecture

39B Active Parameters

Uses 39B active out of 141B total for fast inference on Mistral: Mixtral 8x22B Instruct API.

64K Context

Long Document Recall

Processes 64K tokens for precise recall in Mistral: Mixtral 8x22B Instruct model tasks.

Native Function Calling

Build Applications Fast

Supports function calling and constrained output in mistral mixtral 8x22b instruct.

Examples

See what Mistral: Mixtral 8x22B Instruct can create

Copy any prompt below and try it yourself in the playground.

Code Generator

“Write a Python function to parse JSON logs, extract error counts by type, and output a summary table using pandas. Include error handling for malformed JSON.”

Math Solver

“Solve this system of equations step-by-step: 2x + 3y = 8, 4x - y = 5. Explain each algebraic manipulation and verify the solution.”

Multilingual Summary

“Summarize this French technical article on renewable energy trends in English, highlighting key statistics and projections for 2030. Article text: [insert article].”

Function Caller

“You have tools: get_weather(city), calculate_distance(loc1, loc2). User asks: What's the distance from Paris to London and current weather in London? Call tools sequentially.”

For Developers

A few lines of code.
Instruct model. One call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

Serverless: scales to zero, scales to millions
Pay per token, no minimums
Python and JavaScript SDKs, plus REST API

API Documentation

import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Mistral: Mixtral 8x22B Instruct

Read the docs

Ready to create?

Start generating with Mistral: Mixtral 8x22B Instruct on ModelsLab.