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aiqbench

Alibaba · model passport

QwQ 32b

qwq-32b
  • Open weights
  • Apache 2.0 licence
  • 32.8B params
  • Released 2025-03-05(curated)
10/27axes covered
Overall · LMArena
1,336#221 of 396
Best percentile
80.8LiveBench
Cohort standing
51.0median 45.4

What the data says

Benchmarked on 10 of 27 tracked axes. Every figure below is the model's own published result on that board.

Uneven: 43.5 percentile points between its best and worst axis — 80.8 pct on LiveBench versus 37.3 pct on IFBench (Artificial Analysis).

  • LiveBench80.8 pct69.5%Verify →
  • LiveCodeBench (Artificial Analysis)66.8 pct63.1%Verify →
  • MMLU-Pro (Artificial Analysis)55.8 pct76.4%Verify →
  • Hard prompts46.5 pct1,356Verify →
  • Instruction following46 pct1,323Verify →
  • Coding45.8 pct1,383Verify →
  • GPQA Diamond45.7 pct65.3%Verify →
  • Overall44.4 pct1,336Verify →
  • AIME (math)43.5 pct59.2%Verify →
  • IFBench (Artificial Analysis)37.3 pct38.8%Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot 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
  • 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

RepositoryQwen/QwQ-32B
Parameters32.8B
ArchitectureQwen2ForCausalLM
LicenceApache 2.0

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Qwen/QwQ-32B"
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.

  • qwq-32b stands at 51 (raw mean 51.3) — measured on 10 of 27 axes.

    standingtemplateVerify →

  • qwq-32b stands at 51 versus a cohort median of 45.4 across 146 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 →