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Mixtral 8x7b Instruct V0 1

mixtral-8x7b-instruct-v0-1
  • Open weights
  • Apache 2.0 licence
  • 46.7B params
  • Released 2023-12-11(curated)
6/27axes covered
Overall · LMArena
1,197#329 of 396
Best percentile
17.4Instruction following
Cohort standing
26.2median 29.2

What the data says

Benchmarked on 6 of 27 tracked axes. Every figure below is the model's own published result on that board.

Weakest axes

  • 7.9 pct — LiveBench (22.7%)
  • 10.9 pct — GPQA Diamond (30.6%)

Uneven: 9.5 percentile points between its best and worst axis — 17.4 pct on Instruction following versus 7.9 pct on LiveBench.

  • Instruction following17.4 pct1,181Verify →
  • Overall17.2 pct1,197Verify →
  • Hard prompts16.7 pct1,212Verify →
  • Coding15.9 pct1,240Verify →
  • GPQA Diamond10.9 pct30.6%Verify →
  • LiveBench7.9 pct22.7%Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • IFBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot 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-8x7B-Instruct-v0.1
Parameters46.7B
ArchitectureMixtralForCausalLM
LicenceApache 2.0

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mixtral-8x7B-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 →

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-8x7b-instruct-v0.1 stands at 26.2 (raw mean 14.3) — measured on 6 of 27 axes.

    standingtemplateVerify →

  • mixtral-8x7b-instruct-v0.1 stands at 26.2 versus a cohort median of 29.2 across 82 models released within 180 days — below 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 →