llmrun

Qwen2.5 72B Instruct — Benchmarks

Benchmark scores for Qwen2.5 72B Instruct aggregated from public leaderboards (12 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.

llmrun Composite Score →

81.7

90% range 77.4–85.9 · from 8 boards

Qwen2.5 72B Instruct reasoning benchmarks

Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.

  • GPQA Diamond

    49.1%

    median 54.0%open leader 93.1%

    Overall best: Gemini 4 Argon (high) 96.4%

    45th percentile · #54 of 98 open · #226 of 300 all

    Independent

    Epoch AI ·

  • BALROG

    16.2%

    median 16.2%open leader 34.9%

    Overall best: GPT 6 Astra Max 68.3%

    50th percentile · #8 of 15 open · #32 of 40 all

    Independent

    Epoch AI ·

  • DTBench

    62.9%

    median 67.7%open leader 93.9%

    Overall best: Claude Opus 5.5 Max 98.9%

    42nd percentile · #40 of 68 open · #156 of 214 all

    Lab-reported

    Epoch AI ·

  • ForecastBench

    57.5

    median 58.1open leader 61.9

    Overall best: O3 (Apr 16, 2025, unspecified) 62.5

    36th percentile · #18 of 26 open · #66 of 82 all

    Lab-reported

    Epoch AI ·

  • LMCA

    13.4%

    median 24.7%open leader 55.5%

    Overall best: Claude Opus 5.5 Max 68.2%

    19th percentile · #44 of 54 open · #158 of 176 all

    Lab-reported

    Epoch AI ·

Qwen2.5 72B Instruct coding benchmarks

Writing, editing, and fixing real code — measured by whether the resulting program actually runs and passes tests.

  • WeirdML

    16.0%

    median 39.5%open leader 82.6%

    Overall best: GPT 6 Astra Promax 93.6%

    11th percentile · #41 of 46 open · #151 of 162 all

    Independent

    Epoch AI ·

Other results (1)leaderboard-only, not part of the llmrun Score
  • METR Time Horizons

    35.8%

    median 49.6%open leader 59.2%

    Overall best: Claude Mythos Preview Early 85.2%

    25th percentile · #7 of 9 open · #36 of 46 all

    Lab-reported

    Epoch AI ·

Qwen2.5 72B Instruct math benchmarks

Symbolic and quantitative problem-solving, from competition math to multi-step calculation.

  • AIME 2024/2025

    8.1%

    median 52.8%open leader 98.6%

    Overall best: Claude Fable 5 (high) 100.0%

    30th percentile · #60 of 86 open · #236 of 283 all

    Independent

    Epoch AI ·

  • MATH Level 5

    63.2%

    median 41.6%open leader 96.6%

    Overall best: GPT 5 (Aug 07, 2025, high) 98.1%

    74th percentile · #11 of 39 open · #52 of 107 all

    Independent

    Epoch AI ·

Qwen2.5 72B Instruct knowledge benchmarks

Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.

  • MMLU-Pro

    71.6%

    median 52.1%open leader 88.0%

    Overall best: Gemini-3.1-Pro 91.2%

    76th percentile · #35 of 145 open · #95 of 257 all

    Independent

    TIGER-Lab

Other results (1)leaderboard-only, not part of the llmrun Score
  • MMLU

    85.3%

    median 62.5%open leader 87.2%

    Overall best: GPT 4o (Nov 20, 2024) 88.1%

    98th percentile · #3 of 85 open · #9 of 136 all

    Lab-reported

    Epoch AI ·

Qwen2.5 72B Instruct instruction following benchmarks

How precisely a model obeys explicit, verifiable instructions — format, length, and content constraints — and, more broadly, how much people prefer its responses head-to-head in blind human voting.

  • LMArena Text

    1302.9

    median 1321.5open leader 1487.1

    Overall best: Gemini 4 Argon (High) 1523.9

    44th percentile · #106 of 187 open · #275 of 405 all

    Arena

    Arena ·

Score vs VRAM in the ≤64 GB tier

  • Qwen2.5 72B Instruct
  • Other models
  • Best Score for its VRAM

#30 of 49 that fit ≤64 GB

Each dot is a model at the best quant that fits ≤64 GB. 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 ≤64 GB ranking →

Qwen2.5 72B Instruct vs its neighbours

Same boards for the nearest open models per llmrun Score. The highest score in each row is marked.

BoardQwen2.5 72B Instructthis modelQwen3.8 27Bbest in classQ4_K_M · 18 GBLlama 4 Scout 17B 16E Instructnearest by ScoreQ4_K_M · 77 GBMistral Large Instruct 2407nearest by ScoreQ4_K_M · 86 GBC4ai Command A 03 2025nearest by ScoreQ4_K_M · 79 GB
llmrun Score81.7122.181.881.179.8
GPQA Diamond49.1%—51.8%49.0%—
DTBench62.9%88.0%57.9%61.2%61.3%
ForecastBench57.5—57.557.1—
LMCA13.4%41.4%12.0%—10.3%
AIME 2024/20258.1%—7.8%8.5%—
MATH Level 563.2%—62.3%44.8%—
MMLU-Pro71.6%—74.3%65.9%—
LMArena Text1302.91437.81321.51314.41353.7
Compare head to headCompareCompareCompareCompare

Scores aggregated from public benchmark sources (Epoch AI: 10 boards, Arena: 1 board, 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.