Model comparison
GPT-5 vs Qwen3.7 Plus
GPT-5 is the stronger model overall, scoring 50.9 to 45.3 on the Noometry Index. Qwen3.7 Plus costs 4.9× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
Alibaba (Qwen)Summary
- They share 28 benchmarks with published results for both. GPT-5 scores higher in 6 categories and Qwen3.7 Plus in 4 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 44.5.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 55.4% for GPT-5 and 34.4% for Qwen3.7 Plus.
- Qwen3.7 Plus is cheaper at $0.40 / $1.60 per million input/output tokens, against $1.25 / $10 for GPT-5.
- Qwen3.7 Plus accepts more context: 1M tokens versus 400K.
Side by side
| GPT-5 | Qwen3.7 Plus | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 50.9 | 45.3 |
| Released | 2025-08-07 | 2026-06-02 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $1.25 | $0.40 |
| Output $ / M tokens | $10 | $1.60 |
| Results tracked | 69 | 32 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5 leads
GPT-5: 50.3 (#47), Qwen3.7 Plus: 36.6 (#206)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| SciCode | 42.9% | 45.5% |
| LMArena Coding | 1436 | 1473 |
| SWE-bench Verified | 73.6% | — |
| FrontierCode | — | 10.2% |
| SWE-bench Verified (bash only) | 65% | — |
| Aider Polyglot | 88% | — |
| LMArena WebDev | 1418 | — |
| GSO | 6.9% | — |
| WeirdML | 60.7% | — |
| ALE-Bench | 1,162 | — |
| AlgoTune | 1.67 | — |
Agentic & Tool Use GPT-5 leads
GPT-5: 33.1 (#56), Qwen3.7 Plus: 21.4 (#138)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| Terminal-Bench | 49.6% | — |
| OSWorld 2.0 | — | 2.8% |
| GDPval | 34.8% | — |
| Remote Labor Index | 1.7% | — |
| DeepResearch Bench | 49.6% | — |
| BALROG | 32.8% | — |
| LMArena Search | 1133 | — |
| METR Time Horizons | 69.6% | — |
Reasoning Qwen3.7 Plus leads
GPT-5: 38.3 (#64), Qwen3.7 Plus: 39.3 (#59)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| CritPt | 12.6% | 9.1% |
| Chess Puzzles | 37% | 24% |
| LMArena Hard Prompts | 1416 | 1460 |
| Mystery Game Puzzles | 23% | 17% |
| DTBench | 90.7% | 84% |
| LMCA | 40% | 37.6% |
| Epoch Capabilities Index | 150 | 147.37 |
| ARC-AGI-2 | 9.9% | — |
| SimpleBench | 56.7% | — |
| Kagi LLM Benchmark | 72.7% | — |
| NYT Connections (extended) | — | 74.8% |
| ARC-AGI-1 | 65.7% | — |
| EnigmaEval | 10.5% | — |
| EBR-Bench | 12.7% | — |
| ForecastBench | 61.4 | — |
Math GPT-5 leads
GPT-5: 55.0 (#44), Qwen3.7 Plus: 50.5 (#56)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.4% | 34.4% |
| OTIS Mock AIME 2024-2025 | 91.4% | 93.3% |
| LMArena Math | 1407 | 1466 |
| FrontierMath Tier 4 | 22% | — |
| ProofBench | 18% | — |
| Omni-MATH | 64.7% | — |
| MATH Level 5 | 98.1% | — |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GPT-5 leads
GPT-5: 56.6 (#43), Qwen3.7 Plus: 54.9 (#51)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| GPQA Diamond | 86.2% | 87.9% |
| LMArena Expert | 1419 | 1467 |
| Humanity's Last Exam | 25.3% | — |
| SimpleQA Verified | 50.1% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.3% | — |
| Vectara Hallucination Rate | 14.7% | — |
| GPQA (HELM) | 79.2% | — |
Multimodal GPT-5 leads
GPT-5: 46.8 (#13), Qwen3.7 Plus: 41.8 (#33)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| LMArena Vision | 1232 | 1279 |
| GeoBench | 81% | — |
| VPCT | 66% | — |
| LMArena Document | — | 1444 |
Multilingual Qwen3.7 Plus leads
GPT-5: 51.4 (#110), Qwen3.7 Plus: 54.8 (#38)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| LMArena Non-English | 1397 | 1445 |
| LMArena Chinese | 1422 | 1510 |
| LMArena French | 1410 | 1473 |
| LMArena German | 1416 | 1471 |
| LMArena Japanese | 1409 | 1413 |
| LMArena Korean | 1360 | 1415 |
| LMArena Russian | 1406 | 1457 |
| LMArena Spanish | 1399 | 1457 |
Instruction Following Qwen3.7 Plus leads
GPT-5: 73.8 (#113), Qwen3.7 Plus: 75.8 (#52)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| LMArena Instruction Following | 1388 | 1440 |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), Qwen3.7 Plus: 44.5 (#65)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| LMArena Longer Query | 1399 | 1455 |
| Fiction.LiveBench | 97.2% | — |
Writing & Preference Too close to call
GPT-5: 63.4 (#65), Qwen3.7 Plus: 64.3 (#56)
| Benchmark | GPT-5 | Qwen3.7 Plus |
|---|---|---|
| LMArena Text | 1406 | 1455 |
| LMArena Creative Writing | 1365 | 1439 |
| LMArena Multi-Turn | 1426 | 1460 |
| Short-Story Creative Writing | 86% | — |
| EQ-Bench Creative Writing | 1627 | — |
| WildBench | 85.7% | — |
Frequently asked questions
Is GPT-5 better than Qwen3.7 Plus?
GPT-5 is the stronger model overall, scoring 50.9 to 45.3 on the Noometry Index. Qwen3.7 Plus costs 4.9× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Which is cheaper, GPT-5 or Qwen3.7 Plus?
Qwen3.7 Plus is cheaper. It lists at $0.40 per million input tokens and $1.60 per million output tokens; GPT-5 lists at $1.25 and $10.
Is GPT-5 or Qwen3.7 Plus better for coding?
GPT-5 scores higher on coding benchmarks: 50.3 versus 36.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.7 Plus does, with 1M tokens against 400K.
How many benchmarks do GPT-5 and Qwen3.7 Plus share?
28 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and Qwen3.7 Plus has 32.