Qwen2.5 32B Instruct — Benchmarks
Benchmark scores for Qwen2.5 32B Instruct aggregated from public leaderboards (4 boards), with how it ranks among open models and how it compares with its nearest neighbours. See hardware requirements for what you need to run it.
- #82 of 100 open models
- #24 of 35 that fit 24 GB
- 4 boards · updated Oct 2026
- 100 = average open model
- Hardware requirements →
Qwen2.5 32B Instruct reasoning benchmarks
Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.
46.1%
46.1%
median 54.0%open leader 93.1%
Overall best: Gemini 4 Argon (high) 96.4%
36th percentile · #63 of 98 open · #243 of 300 all
IndependentEpoch AI ·
Qwen2.5 32B Instruct math benchmarks
Symbolic and quantitative problem-solving, from competition math to multi-step calculation.
7.4%
7.4%
median 52.8%open leader 98.6%
Overall best: Claude Fable 5 (high) 100.0%
25th percentile · #65 of 86 open · #241 of 283 all
IndependentEpoch AI ·
56.1%
56.1%
median 41.6%open leader 96.6%
Overall best: GPT 5 (Aug 07, 2025, high) 98.1%
68th percentile · #13 of 39 open · #57 of 107 all
IndependentEpoch AI ·
Qwen2.5 32B Instruct knowledge benchmarks
Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.
Score vs VRAM in the 24 GB tier
- Qwen2.5 32B Instruct
- Other models
- Best Score for its VRAM
#24 of 35 that fit 24 GB
Each dot is a model at the best quant that fits a 24 GB GPU. The line joins the models with the best Score for their VRAM; a dot below it is beaten by a model that needs no more memory.
Full 24 GB ranking →Qwen2.5 32B Instruct vs its neighbours
Same boards for the nearest open models per llmrun Score and the best model that fits 24 GB. The highest score in each row is marked.
| Board | Qwen2.5 32B Instructthis model | Qwen3.8 27Bbest in classQ4_K_M · 18 GB | Qwen2.5 Coder 32B Instructnearest by ScoreQ4_K_S · 23 GB | Gemma 3 27B ITnearest by ScoreQ4_K_M · 22 GB | Mistral Small 3.1 24B Instruct 2503nearest by ScoreQ4_K_M · 18 GB |
|---|---|---|---|---|---|
| llmrun Score | 80.4 | 122.1 | 81.9 | 79.4 | 79.0 |
| GPQA Diamond | 46.1% | — | — | 47.7% | 47.5% |
| AIME 2024/2025 | 7.4% | — | — | 22.5% | 5.8% |
| MATH Level 5 | 56.1% | — | — | 74.0% | 46.8% |
| MMLU-Pro | 69.2% | — | — | 67.5% | — |
| Compare head to head | Compare | Compare | Compare | Compare |
Scores aggregated from public benchmark sources (Epoch AI: 3 boards, TIGER-Lab: 1 board) — each linked from the benchmark pages. llmrun does not run these benchmarks. Percentiles and ranks are among open models in the llmrun catalogue; the median and leader marks refer to the same field.