Model comparison
o4-mini vs Qwen3.5 397B-A17B
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 41.6 on the Noometry Index.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. o4-mini scores higher in 2 categories and Qwen3.5 397B-A17B in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5 397B-A17B leads 34.5 to 24.6.
- The biggest single-benchmark swing is Chess Puzzles: 26% for o4-mini and 13% for Qwen3.5 397B-A17B.
- Qwen3.5 397B-A17B is cheaper at $0.60 / $3.60 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- Qwen3.5 397B-A17B accepts more context: 262K tokens versus 200K.
- Qwen3.5 397B-A17B has downloadable open weights; the other is API-only.
Side by side
| o4-mini | Qwen3.5 397B-A17B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 46.0 |
| Released | 2025-04-16 | 2026-02-01 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 100K | 66K |
| Input $ / M tokens | $1.10 | $0.60 |
| Output $ / M tokens | $4.40 | $3.60 |
| Results tracked | 60 | 36 |
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Category by category
Coding Qwen3.5 397B-A17B leads
o4-mini: 40.9 (#127), Qwen3.5 397B-A17B: 42.0 (#114)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Coding | 1368 | 1465 |
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| LMArena WebDev | — | 1400 |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use Too close to call
o4-mini: 32.6 (#61), Qwen3.5 397B-A17B: 33.3 (#53)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| APEX-Agents | — | 24.9% |
| Berkeley Function Calling Leaderboard | 53.2% | — |
| GDPval | 25.3% | — |
| τ²-bench Airline | — | 81.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 84.4% |
| τ²-bench Telecom | — | 97.8% |
| METR Time Horizons | 63.9% | — |
Reasoning Qwen3.5 397B-A17B leads
o4-mini: 24.6 (#162), Qwen3.5 397B-A17B: 34.5 (#70)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| Kagi LLM Benchmark | 67.6% | 73.7% |
| Chess Puzzles | 26% | 13% |
| LMArena Hard Prompts | 1351 | 1448 |
| Mystery Game Puzzles | 5% | 18% |
| DTBench | 77.6% | 87.5% |
| LMCA | 26.5% | 37.9% |
| Epoch Capabilities Index | 145.64 | 146.65 |
| ARC-AGI-2 | 6.1% | — |
| SimpleBench | 38.7% | — |
| NYT Connections (extended) | — | 58.9% |
| ARC-AGI-1 | 58.7% | — |
| CritPt | 0.6% | — |
| EnigmaEval | 9.2% | — |
| Thematic Generalization | — | 65.1% |
| ForecastBench | 61.8 | — |
Math Qwen3.5 397B-A17B leads
o4-mini: 40.8 (#89), Qwen3.5 397B-A17B: 46.1 (#73)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 36.1% | 31.2% |
| OTIS Mock AIME 2024-2025 | 81.7% | 88.9% |
| LMArena Math | 1389 | 1454 |
| FrontierMath Tier 4 | 4.9% | — |
| Omni-MATH | 72% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge Qwen3.5 397B-A17B leads
o4-mini: 43.6 (#91), Qwen3.5 397B-A17B: 53.3 (#58)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| GPQA Diamond | 79.6% | 86.4% |
| LMArena Expert | 1343 | 1462 |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| MMLU-Pro | 82% | — |
| Confabulations | 15.8% | — |
| Vectara Hallucination Rate | 18.6% | — |
| GPQA (HELM) | 73.5% | — |
Multimodal Too close to call
o4-mini: 40.2 (#49), Qwen3.5 397B-A17B: 40.7 (#44)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Vision | 1194 | 1263 |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual Qwen3.5 397B-A17B leads
o4-mini: 47.0 (#154), Qwen3.5 397B-A17B: 53.7 (#59)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Non-English | 1337 | 1430 |
| LMArena Chinese | 1354 | 1500 |
| LMArena French | 1364 | 1461 |
| LMArena German | 1336 | 1447 |
| LMArena Japanese | 1308 | 1426 |
| LMArena Korean | 1312 | 1384 |
| LMArena Russian | 1334 | 1429 |
| LMArena Spanish | 1347 | 1441 |
Instruction Following Too close to call
o4-mini: 75.2 (#68), Qwen3.5 397B-A17B: 75.0 (#77)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Instruction Following | 1321 | 1424 |
| IFEval | 92.8% | — |
Long Context o4-mini leads
o4-mini: 45.5 (#33), Qwen3.5 397B-A17B: 44.1 (#74)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Longer Query | 1315 | 1442 |
| Fiction.LiveBench | 77.8% | — |
Writing & Preference Qwen3.5 397B-A17B leads
o4-mini: 54.0 (#152), Qwen3.5 397B-A17B: 62.3 (#79)
| Benchmark | o4-mini | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Text | 1353 | 1438 |
| LMArena Creative Writing | 1294 | 1401 |
| LMArena Multi-Turn | 1350 | 1446 |
| Short-Story Creative Writing | 75% | — |
| EQ-Bench Creative Writing | — | 1478 |
| WildBench | 85.4% | — |
Frequently asked questions
Is o4-mini better than Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 41.6 on the Noometry Index.
Which is cheaper, o4-mini or Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is cheaper. It lists at $0.60 per million input tokens and $3.60 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is o4-mini or Qwen3.5 397B-A17B better for coding?
Qwen3.5 397B-A17B scores higher on coding benchmarks: 42.0 versus 40.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 397B-A17B does, with 262K tokens against 200K.
How many benchmarks do o4-mini and Qwen3.5 397B-A17B share?
27 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen3.5 397B-A17B has 36.