Mistral · Open weights
Mixtral 8x22B
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.
| Mixtral 8x22BMistralThis model | Mistral Code Agent LatestMistralRemove | |
|---|---|---|
| Provider | Mistral | Mistral |
| Discovery tier | Open weights | Open weights |
| Added | Dec 11, 2025 | Jun 2, 2026 |
| Limits | ||
| Context window | 65.54K | 262.14KBest |
| Max output | 52.43KBest | 32.77K |
| Average output | 292 tokens | 316 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $2.00 | $0.40Best |
| Output | $6.00 | $2.00Best |
| Cache read | $0.20 | — |
| Performance | ||
| Request success (24h) | 100.0% | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 5.7 | 8.6Best |
| Coding Index | — | 31.3 |
| Math Index | — | 36.7 |
| Capabilities | ||
| Tools | No | Yes |
| Structured output | No | Yes |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Mixtral 8x22B
- Mistral Code Agent Latest
Intelligence Index
- Mixtral 8x22B5.7
- Mistral Code Agent Latest8.6
Mixtral 8x22B: Better than 2% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 31.3
Math Index
Composite of the math evaluations
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 36.7
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 3.1%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 0.0%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 482 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 544 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 2.4%
AA-LCRv1.1
Reasoning across long documents
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 32.3%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Mixtral 8x22B: 4.0%Top
- Mistral Code Agent Latest: 3.6%
GPQA Diamond
Graduate-level science questions
- Mixtral 8x22B: 33.2%
- Mistral Code Agent Latest: 59.4%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- Mixtral 8x22B: 53.7%
- Mistral Code Agent Latest: 76.2%Top
IFBench
Following precise output instructions
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 38.1%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 0.0%
SciCode
Scientific computing in Python
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 32.8%
LiveCodeBench
Recent competitive programming problems
- Mixtral 8x22B: 14.8%
- Mistral Code Agent Latest: 44.8%Top
Math
AIME 2025
Competition mathematics, 2025 problems
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 36.7%
AIME
Competition mathematics
- Mixtral 8x22B: 0.0%
- Mistral Code Agent Latest: —
MATH-500
Competition math problems
- Mixtral 8x22B: 54.5%
- Mistral Code Agent Latest: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 18.9%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 24.9%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Mixtral 8x22B: —
- Mistral Code Agent Latest: 32.3%
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.
- 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.
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
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