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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

Mixtral 8x22B compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Mixtral 8x22BMistralThis modelLlama 3.2 3b InstructMetaRemove
ProviderMistralMeta
Discovery tierOpen weightsOpen weights
AddedDec 11, 2025Jan 1, 2024
Limits
Context window65.54K131.07KBest
Max output52.43KBest8.19K
Average output292 tokens119 tokens
Pricing, USD per 1M tokens
Input$2.00$0.0306Best
Output$6.00$0.0493Best
Cache read$0.20—
Performance
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index5.75.7
Math Index—3.3
Capabilities
PDF inputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Mixtral 8x22B
  • Llama 3.2 3b Instruct

Intelligence Index

  • Mixtral 8x22B5.7
  • Llama 3.2 3b Instruct5.7

Mixtral 8x22B: Better than 2% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Mixtral 8x22B: —
    • Llama 3.2 3b Instruct: 3.3

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mixtral 8x22B: —
    • Llama 3.2 3b Instruct: 4.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mixtral 8x22B: 4.0%
    • Llama 3.2 3b Instruct: 5.3%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mixtral 8x22B: 33.2%Top
    • Llama 3.2 3b Instruct: 25.5%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mixtral 8x22B: 53.7%Top
    • Llama 3.2 3b Instruct: 34.7%

  • IFBench

    Following precise output instructions

    • Mixtral 8x22B: —
    • Llama 3.2 3b Instruct: 26.2%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Mixtral 8x22B: 14.8%Top
    • Llama 3.2 3b Instruct: 8.3%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mixtral 8x22B: —
    • Llama 3.2 3b Instruct: 3.3%

  • AIME

    Competition mathematics

    • Mixtral 8x22B: 0.0%
    • Llama 3.2 3b Instruct: 6.7%Top

  • MATH-500

    Competition math problems

    • Mixtral 8x22B: 54.5%Top
    • Llama 3.2 3b Instruct: 48.9%

Legacy evaluations

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mixtral 8x22B: —
    • Llama 3.2 3b Instruct: 21.1%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mixtral 8x22B: —
    • Llama 3.2 3b Instruct: 4.3%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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
40.6%
About $1.27 per 1M input tokens with caching
Request success (24h)
100.0%
Reliable

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.

Hourly request success · last 72 hoursLowest hour 100.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

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.