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Mistral · Open weights

Mixtral 8x22B

Added Dec 11, 2025Open weights

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Use in the app

Mixtral 8x22B is a powerful sparse Mixture of Experts (MoE) model with 141B total parameters and 39B active per token. Features a 64K context window, exceptional math performance, and cost-efficient inference. Supports English, French, German, Spanish, and Italian. Apache 2.0 licensed.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$2.00
Output
$6.00
Cache read
$0.20

Reference prices as published by the model provider. API example

Specifications

Context window
65.5K
Max output
52.4K
Average output (7 days)
292 tokens
Longer than 30% of 468 models

Side by side

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TryLlama 3.2 3b InstructPhi 4 MultimodalReka Flash 3All 3

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

Intelligence Index

5.7

Better than 2% of 326 scored models

3.8Catalog models by score55.1

Reasoning and knowledge

  • Humanity’s Last Exam

    4.0%

    Expert-level questions across academic fields

    Better than 7% of 323 scored models

  • GPQA Diamond

    33.2%

    Graduate-level science questions

    Better than 2% of 291 scored models

  • MMLU-Pro

    53.7%

    Multi-subject knowledge and reasoning

    Better than 7% of 132 scored models

Coding

  • LiveCodeBench

    14.8%

    Recent competitive programming problems

    Better than 6% of 129 scored models

Math

  • AIME

    0.0%

    Competition mathematics

    Better than 0% of 72 scored models

  • MATH-500

    54.5%

    Competition math problems

    Better than 9% of 73 scored models

Scores from Artificial Analysis, updated Oct 26, 2026.

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits
33.0%
About $1.41 per 1M input tokens with caching

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistralai/mixtral-8x22b-instruct-v0.1",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="mistralai/mixtral-8x22b-instruct-v0.1",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

Other Mistral models, starting with the same family.