Qwen3.5 397B A17B — Benchmarks
Benchmark scores for Qwen3.5 397B A17B aggregated from public leaderboards (10 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.
- #20 of 100 open models
- 10 boards · updated Oct 2026
- 100 = average open model
- Hardware requirements →
Qwen3.5 397B A17B reasoning benchmarks
Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.
86.4%
86.4%
median 54.0%open leader 93.1%
Overall best: Gemini 4 Argon (high) 96.4%
82nd percentile · #18 of 98 open · #81 of 300 all
IndependentEpoch AI ·
24.9%
24.9%
median 40.2%open leader 59.5%
Overall best: Gemini 4 Argon (unspecified) 82.4%
20th percentile · #13 of 16 open · #51 of 54 all
Lab-reportedEpoch AI ·
87.5%
87.5%
median 67.7%open leader 93.9%
Overall best: Claude Opus 5.5 Max 98.9%
83rd percentile · #12 of 68 open · #84 of 214 all
Lab-reportedEpoch AI ·
37.9%
37.9%
median 24.7%open leader 55.5%
Overall best: Claude Opus 5.5 Max 68.2%
83rd percentile · #10 of 54 open · #95 of 176 all
Lab-reportedEpoch AI ·
18.0%
18.0%
median 18.0%open leader 43.0%
Overall best: GPT 6 Astra Max 84.0%
50th percentile · #10 of 18 open · #70 of 133 all
IndependentEpoch AI ·
Qwen3.5 397B A17B coding benchmarks
Writing, editing, and fixing real code — measured by whether the resulting program actually runs and passes tests.
1400.5
1400.5
median 1411.4open leader 1655.2
Overall best: Claude Opus 5.5 (Max) 1807.3
47th percentile · #28 of 52 open · #90 of 138 all
ArenaArena ·
Qwen3.5 397B A17B math benchmarks
Symbolic and quantitative problem-solving, from competition math to multi-step calculation.
Qwen3.5 397B A17B knowledge benchmarks
Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.
Qwen3.5 397B A17B 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.
1441.6
1441.6
median 1321.5open leader 1487.1
Overall best: Gemini 4 Argon (High) 1523.9
91st percentile · #17 of 187 open · #93 of 405 all
ArenaArena ·
Score vs VRAM in the ≤256 GB tier
- Qwen3.5 397B A17B
- Other models
- Best Score for its VRAM
#9 of 84 that fit ≤256 GB
Each dot is a model at the best quant that fits ≤256 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 ≤256 GB ranking →Qwen3.5 397B A17B vs its neighbours
Same boards for the nearest open models per llmrun Score. The highest score in each row is marked.
| Board | Qwen3.5 397B A17Bthis model | Qwen3.8 27Bbest in classQ4_K_M · 18 GB | MiniMax M3nearest by ScoreQ4_K_M · 258 GB | MiniMax M2.5nearest by ScoreQ4_K_M · 140 GB | GLM 5nearest by ScoreQ4_K_M · 454 GB |
|---|---|---|---|---|---|
| llmrun Score | 113.0 | 122.1 | 112.5 | 111.8 | 111.6 |
| GPQA Diamond | 86.4% | — | 90.9% | — | 87.8% |
| APEX-Agents | 24.9% | 47.5% | 37.7% | — | — |
| DTBench | 87.5% | 88.0% | 78.9% | — | — |
| LMCA | 37.9% | 41.4% | 33.7% | — | — |
| Mystery Game Puzzles | 18.0% | — | 8.0% | — | — |
| LMArena WebDev | 1400.5 | 1593.2 | 1482.5 | 1386.3 | 1435.4 |
| AIME 2024/2025 | 88.9% | — | 71.1% | — | 80.0% |
| MMLU-Pro | 87.8% | — | — | 80.1% | 86.0% |
| LMArena Text | 1441.6 | 1437.8 | 1441.3 | 1390.6 | 1457.3 |
| Compare head to head | Compare | Compare | Compare | Compare |
Scores aggregated from public benchmark sources (Epoch AI: 7 boards, Arena: 2 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.