Mistral · model passport

Mistral: Mixtral 8x22B Instruct

mixtral-8x22b-instruct

Open weights
140.6B params
65,536 context
Released 2024-04-17(curated)

Best percentile: 20.6MMLU-Pro (Artificial Analysis)
Cohort standing: 37.9median 32.8

Thin coverage — read with caution

This model appears on only 2 of 27 benchmark axes considered. The overall standing has been shrunk toward the neutral 50 to reflect the small sample, and no weaknesses have been claimed — a low percentile on 2 axes is a coverage artifact, not a measured weakness.

What the data says

Benchmarked on 2 of 27 tracked axes. Every figure below is the model's own published result on that board.

On only 2 board(s), a ranked list of strengths would be false authority — the per-axis figures are shown below instead, without a verdict.

Uneven: 1.9 percentile points between its best and worst axis — 20.6 pct on MMLU-Pro (Artificial Analysis) versus 18.7 pct on LiveCodeBench (Artificial Analysis).

MMLU-Pro (Artificial Analysis)20.6 pct53.7%Verify →
LiveCodeBench (Artificial Analysis)18.7 pct14.8%Verify →
OverallNot benchmarked
WebDev ArenaNot benchmarked
Vision ArenaNot benchmarked
Factuality ArenaNot benchmarked
Search ArenaNot benchmarked
Document ArenaNot benchmarked
Agent ArenaNot benchmarked
GPQA DiamondNot benchmarked
AIME (math)Not benchmarked
FrontierMathNot benchmarked
SimpleQA VerifiedNot benchmarked
ARC-AGI-2Not benchmarked
Humanity's Last ExamNot benchmarked
SimpleBenchNot benchmarked
LiveBenchNot benchmarked
BFCL v4 (function calling)Not benchmarked
SWE-bench Verified (live board)Not benchmarked
Terminal-Bench 4.0Not benchmarked
IFBench (Artificial Analysis)Not benchmarked
HHEM Hallucination (Vectara)Not benchmarked
SWE-bench VerifiedNot benchmarked
Terminal-BenchNot benchmarked
CodingNot benchmarked
Instruction followingNot benchmarked
Hard promptsNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositorymistralai/Mixtral-8x22B-Instruct-v0.1

Parameters140.6B

ArchitectureMixtralForCausalLM

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mixtral-8x22B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Use it hosted

API model IDmistralai/mixtral-8x22b-instruct

Context length65,536 tokens

Max output52,428 tokens

Acceptstext, file

Input price$2 per 1M tokens

Cached input price$0.20 per 1M tokens

Output price$6 per 1M tokens

API parameters this endpoint supports

Frequency penaltyMax tokensPresence penaltyResponse formatSeedStop sequencesStructured outputsTemperatureTool choiceTool callingTop-p

Example request

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"mistralai/mixtral-8x22b-instruct","messages":[{"role":"user","content":"Hello"}]}'

View on OpenRouter →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

Mixtral 8x22B Instruct stands at 37.9 (raw mean 19.7) — measured on only 2 of 27 axes, so the standing is shrunk toward the neutral 50.
standingtemplateVerify →
Mixtral 8x22B Instruct stands at 37.9 versus a cohort median of 32.8 across 117 models released within 180 days — above the peer median.
cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →