Model comparison
GPT-5 vs Qwen3.7 Max
GPT-5 and Qwen3.7 Max score almost the same on the Noometry Index (50.9 vs 51.5), so choose on price, context window or the category you care about most.
Last verified . 30 shared benchmarks.
Summary
- They share 30 benchmarks with published results for both. GPT-5 scores higher in 2 categories and Qwen3.7 Max in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 45.4.
- The biggest single-benchmark swing is Chess Puzzles: 37% for GPT-5 and 19% for Qwen3.7 Max.
- GPT-5 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 | Qwen3.7 Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 50.9 | 51.5 |
| Released | 2025-08-07 | 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 | 69 | 33 |
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Category by category
Coding Too close to call
GPT-5: 50.3 (#47), Qwen3.7 Max: 50.4 (#45)
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| SWE-bench Verified | 73.6% | 77.3% |
| LMArena WebDev | 1418 | 1515 |
| SciCode | 42.9% | 48.8% |
| LMArena Coding | 1436 | 1498 |
| ALE-Bench | 1,162 | 1,189 |
| SWE-bench Verified (bash only) | 65% | — |
| Aider Polyglot | 88% | — |
| GSO | 6.9% | — |
| WeirdML | 60.7% | — |
| AlgoTune | 1.67 | — |
Agentic & Tool Use GPT-5 leads
GPT-5: 33.1 (#56), Qwen3.7 Max: 22.1 (#135)
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| Terminal-Bench | 49.6% | — |
| GDPval | 34.8% | — |
| Remote Labor Index | 1.7% | — |
| DeepResearch Bench | 49.6% | — |
| BALROG | 32.8% | — |
| GBAEval | — | 0.4% |
| LMArena Search | 1133 | — |
| METR Time Horizons | 69.6% | — |
Reasoning Qwen3.7 Max leads
GPT-5: 38.3 (#64), Qwen3.7 Max: 49.2 (#38)
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| SimpleBench | 56.7% | 70.4% |
| CritPt | 12.6% | 13.4% |
| Chess Puzzles | 37% | 19% |
| EBR-Bench | 12.7% | 9.5% |
| LMArena Hard Prompts | 1416 | 1483 |
| Mystery Game Puzzles | 23% | 32% |
| DTBench | 90.7% | 92.3% |
| LMCA | 40% | 44% |
| Epoch Capabilities Index | 150 | 153.68 |
| ARC-AGI-2 | 9.9% | — |
| Kagi LLM Benchmark | 72.7% | — |
| NYT Connections (extended) | — | 85.1% |
| ARC-AGI-1 | 65.7% | — |
| EnigmaEval | 10.5% | — |
| ForecastBench | 61.4 | — |
Math Qwen3.7 Max leads
GPT-5: 55.0 (#44), Qwen3.7 Max: 62.4 (#32)
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.4% | 64.6% |
| FrontierMath Tier 4 | 22% | 34.1% |
| OTIS Mock AIME 2024-2025 | 91.4% | 95.6% |
| ProofBench | 18% | 26% |
| LMArena Math | 1407 | 1490 |
| Omni-MATH | 64.7% | — |
| MATH Level 5 | 98.1% | — |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge Qwen3.7 Max leads
GPT-5: 56.6 (#43), Qwen3.7 Max: 61.6 (#28)
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 86.2% | 90.9% |
| SimpleQA Verified | 50.1% | 55.8% |
| LMArena Expert | 1419 | 1488 |
| Humanity's Last Exam | 25.3% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.3% | — |
| Vectara Hallucination Rate | 14.7% | — |
| GPQA (HELM) | 79.2% | — |
Multimodal Not comparable
GPT-5: 46.8 (#13), Qwen3.7 Max: —
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| LMArena Vision | 1232 | — |
| GeoBench | 81% | — |
| VPCT | 66% | — |
Multilingual Qwen3.7 Max leads
GPT-5: 51.4 (#110), Qwen3.7 Max: 56.9 (#15)
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1397 | 1474 |
| LMArena Chinese | 1422 | 1530 |
| LMArena Russian | 1406 | 1484 |
| LMArena French | 1410 | — |
| LMArena German | 1416 | — |
| LMArena Japanese | 1409 | — |
| LMArena Korean | 1360 | — |
| LMArena Spanish | 1399 | — |
Instruction Following Qwen3.7 Max leads
GPT-5: 73.8 (#113), Qwen3.7 Max: 76.7 (#38)
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1388 | 1460 |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), Qwen3.7 Max: 45.4 (#40)
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1399 | 1482 |
| Fiction.LiveBench | 97.2% | — |
Writing & Preference Qwen3.7 Max leads
GPT-5: 63.4 (#65), Qwen3.7 Max: 65.0 (#54)
| Benchmark | GPT-5 | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1406 | 1476 |
| LMArena Creative Writing | 1365 | 1449 |
| LMArena Multi-Turn | 1426 | 1481 |
| Short-Story Creative Writing | 86% | — |
| EQ-Bench Creative Writing | 1627 | — |
| WildBench | 85.7% | — |
| EQ-Bench 4 | — | 1110 |
Frequently asked questions
Is GPT-5 better than Qwen3.7 Max?
GPT-5 and Qwen3.7 Max score almost the same on the Noometry Index (50.9 vs 51.5), so choose on price, context window or the category you care about most.
Which is cheaper, GPT-5 or Qwen3.7 Max?
GPT-5 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 or Qwen3.7 Max better for coding?
They score almost the same on coding (50.3 vs 50.4); test both on your own repository before choosing.
Which has the bigger context window?
Qwen3.7 Max does, with 1M tokens against 400K.
How many benchmarks do GPT-5 and Qwen3.7 Max share?
30 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and Qwen3.7 Max has 33.