Model comparison
GPT-5.1 vs Qwen3.7 Max
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 49.0 on the Noometry Index.
Last verified . 26 shared benchmarks.
Alibaba (Qwen)Summary
- They share 26 benchmarks with published results for both. GPT-5.1 scores higher in 3 categories and Qwen3.7 Max in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.7 Max leads 61.6 to 50.6.
- The biggest single-benchmark swing is SimpleBench: 53.2% for GPT-5.1 and 70.4% for Qwen3.7 Max.
- GPT-5.1 is cheaper at $1.25 / $10 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- Qwen3.7 Max accepts more context: 1M tokens versus 400K.
Side by side
| GPT-5.1 | Qwen3.7 Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 49.0 | 51.5 |
| Released | 2025-11-13 | 2026-05-19 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $1.25 | $2.50 |
| Output $ / M tokens | $10 | $7.50 |
| Results tracked | 63 | 33 |
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Category by category
Coding Qwen3.7 Max leads
GPT-5.1: 46.4 (#66), Qwen3.7 Max: 50.4 (#45)
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| SWE-bench Verified | 68% | 77.3% |
| LMArena WebDev | 1395 | 1515 |
| SciCode | 43.3% | 48.8% |
| LMArena Coding | 1454 | 1498 |
| ALE-Bench | 1,192 | 1,189 |
| SWE-bench Verified (bash only) | 66% | — |
| GSO | 13.7% | — |
| WeirdML | 60.8% | — |
| LiveBench Coding | 72.5% | — |
Agentic & Tool Use GPT-5.1 leads
GPT-5.1: 32.7 (#60), Qwen3.7 Max: 22.1 (#135)
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| DeepResearch Bench | 42.8% | — |
| GBAEval | — | 0.4% |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |
Reasoning Qwen3.7 Max leads
GPT-5.1: 39.8 (#58), Qwen3.7 Max: 49.2 (#38)
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| SimpleBench | 53.2% | 70.4% |
| CritPt | 4.9% | 13.4% |
| Chess Puzzles | 32% | 19% |
| LMArena Hard Prompts | 1457 | 1483 |
| Mystery Game Puzzles | 19% | 32% |
| DTBench | 90.1% | 92.3% |
| LMCA | 43.9% | 44% |
| Epoch Capabilities Index | 149.64 | 153.68 |
| ARC-AGI-2 | 17.6% | — |
| NYT Connections (extended) | — | 85.1% |
| ARC-AGI-1 | 72.8% | — |
| EnigmaEval | 11.2% | — |
| EBR-Bench | — | 9.5% |
| LiveBench Reasoning | 95.8% | — |
| LiveBench Data Analysis | 72.1% | — |
| ForecastBench | 58.1 | — |
| LiveBench | 78.8% | — |
Math Qwen3.7 Max leads
GPT-5.1: 52.2 (#51), Qwen3.7 Max: 62.4 (#32)
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.6% | 95.6% |
| LMArena Math | 1447 | 1490 |
| FrontierMath (Tiers 1-3) | — | 64.6% |
| FrontierMath Tier 4 | — | 34.1% |
| ProofBench | — | 26% |
| Omni-MATH | 46.4% | — |
| LiveBench Math | 94.5% | — |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge Qwen3.7 Max leads
GPT-5.1: 50.6 (#71), Qwen3.7 Max: 61.6 (#28)
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 87.6% | 90.9% |
| SimpleQA Verified | 48% | 55.8% |
| LMArena Expert | 1470 | 1488 |
| Humanity's Last Exam | 23.7% | — |
| MMLU-Pro | 57.9% | — |
| Vectara Hallucination Rate | 10.9% | — |
| GPQA (HELM) | 44.2% | — |
Multimodal Not comparable
GPT-5.1: 44.8 (#19), Qwen3.7 Max: —
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual Qwen3.7 Max leads
GPT-5.1: 53.8 (#56), Qwen3.7 Max: 56.9 (#15)
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1431 | 1474 |
| LMArena Chinese | 1495 | 1530 |
| LMArena Russian | 1435 | 1484 |
| LMArena French | 1450 | — |
| LMArena German | 1438 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1401 | — |
| LMArena Spanish | 1433 | — |
Instruction Following GPT-5.1 leads
GPT-5.1: 83.9 (#1), Qwen3.7 Max: 76.7 (#38)
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1443 | 1460 |
| LiveBench Instruction Following | 93.3% | — |
| IFEval | 93.5% | — |
Long Context GPT-5.1 leads
GPT-5.1: 47.6 (#14), Qwen3.7 Max: 45.4 (#40)
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1447 | 1482 |
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
Writing & Preference Too close to call
GPT-5.1: 64.5 (#55), Qwen3.7 Max: 65.0 (#54)
| Benchmark | GPT-5.1 | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1443 | 1476 |
| LMArena Creative Writing | 1427 | 1449 |
| LMArena Multi-Turn | 1450 | 1481 |
| WildBench | 86.3% | — |
| EQ-Bench 4 | — | 1110 |
| LiveBench Language | 80.2% | — |
Frequently asked questions
Is GPT-5.1 better than Qwen3.7 Max?
Qwen3.7 Max is the stronger model overall, scoring 51.5 to 49.0 on the Noometry Index.
Which is cheaper, GPT-5.1 or Qwen3.7 Max?
GPT-5.1 is cheaper. It lists at $1.25 per million input tokens and $10 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.
Is GPT-5.1 or Qwen3.7 Max better for coding?
Qwen3.7 Max scores higher on coding benchmarks: 50.4 versus 46.4 in the Noometry coding category.
Which has the bigger context window?
Qwen3.7 Max does, with 1M tokens against 400K.
How many benchmarks do GPT-5.1 and Qwen3.7 Max share?
26 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Qwen3.7 Max has 33.