Model comparison
GPT-5 vs Qwen1.5-32B
GPT-5 is the stronger model overall, scoring 50.9 to 30.5 on the Noometry Index.
Last verified . 18 shared benchmarks.
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. GPT-5 scores higher in 8 categories and Qwen1.5-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5 leads 56.6 to 13.5.
- The biggest single-benchmark swing is GPQA Diamond: 86.2% for GPT-5 and 30.7% for Qwen1.5-32B.
- Qwen1.5-32B has downloadable open weights; the other is API-only.
Side by side
| GPT-5 | Qwen1.5-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 50.9 | 30.5 |
| Released | 2025-08-07 | 2024-02-04 |
| Weights | Proprietary | Open |
| Context window | 400K | — |
| Max output | 128K | — |
| Input $ / M tokens | $1.25 | — |
| Output $ / M tokens | $10 | — |
| Results tracked | 69 | 21 |
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Category by category
Coding GPT-5 leads
GPT-5: 50.3 (#47), Qwen1.5-32B: 31.7 (#282)
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| LMArena Coding | 1436 | 1155 |
| SWE-bench Verified | 73.6% | — |
| SWE-bench Verified (bash only) | 65% | — |
| Aider Polyglot | 88% | — |
| LMArena WebDev | 1418 | — |
| SciCode | 42.9% | — |
| GSO | 6.9% | — |
| WeirdML | 60.7% | — |
| BigCodeBench Instruct | — | 32.3% |
| BigCodeBench Complete | — | 42% |
| ALE-Bench | 1,162 | — |
| AlgoTune | 1.67 | — |
Agentic & Tool Use Not comparable
GPT-5: 33.1 (#56), Qwen1.5-32B: —
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| Terminal-Bench | 49.6% | — |
| GDPval | 34.8% | — |
| Remote Labor Index | 1.7% | — |
| DeepResearch Bench | 49.6% | — |
| BALROG | 32.8% | — |
| LMArena Search | 1133 | — |
| METR Time Horizons | 69.6% | — |
Reasoning GPT-5 leads
GPT-5: 38.3 (#64), Qwen1.5-32B: 21.8 (#212)
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| LMArena Hard Prompts | 1416 | 1130 |
| ARC-AGI-2 | 9.9% | — |
| SimpleBench | 56.7% | — |
| Kagi LLM Benchmark | 72.7% | — |
| ARC-AGI-1 | 65.7% | — |
| CritPt | 12.6% | — |
| Chess Puzzles | 37% | — |
| EnigmaEval | 10.5% | — |
| EBR-Bench | 12.7% | — |
| Mystery Game Puzzles | 23% | — |
| DTBench | 90.7% | — |
| LMCA | 40% | — |
| Epoch Capabilities Index | 150 | — |
| ForecastBench | 61.4 | — |
Math GPT-5 leads
GPT-5: 55.0 (#44), Qwen1.5-32B: 33.0 (#207)
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| LMArena Math | 1407 | 1155 |
| FrontierMath (Tiers 1-3) | 55.4% | — |
| FrontierMath Tier 4 | 22% | — |
| OTIS Mock AIME 2024-2025 | 91.4% | — |
| 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), Qwen1.5-32B: 13.5 (#296)
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| GPQA Diamond | 86.2% | 30.7% |
| LMArena Expert | 1419 | 1126 |
| 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% | — |
| MMLU | — | 74.4% |
Multimodal Not comparable
GPT-5: 46.8 (#13), Qwen1.5-32B: —
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| LMArena Vision | 1232 | — |
| GeoBench | 81% | — |
| VPCT | 66% | — |
Multilingual GPT-5 leads
GPT-5: 51.4 (#110), Qwen1.5-32B: 31.4 (#259)
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| LMArena Non-English | 1397 | 1106 |
| LMArena Chinese | 1422 | 1177 |
| LMArena French | 1410 | 1101 |
| LMArena German | 1416 | 1058 |
| LMArena Japanese | 1409 | 1027 |
| LMArena Korean | 1360 | 1008 |
| LMArena Russian | 1406 | 1073 |
| LMArena Spanish | 1399 | 1089 |
Instruction Following GPT-5 leads
GPT-5: 73.8 (#113), Qwen1.5-32B: 57.7 (#265)
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| LMArena Instruction Following | 1388 | 1116 |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), Qwen1.5-32B: 34.7 (#246)
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| LMArena Longer Query | 1399 | 1146 |
| Fiction.LiveBench | 97.2% | — |
Writing & Preference GPT-5 leads
GPT-5: 63.4 (#65), Qwen1.5-32B: 34.2 (#271)
| Benchmark | GPT-5 | Qwen1.5-32B |
|---|---|---|
| LMArena Text | 1406 | 1137 |
| LMArena Creative Writing | 1365 | 1083 |
| LMArena Multi-Turn | 1426 | 1140 |
| Short-Story Creative Writing | 86% | — |
| EQ-Bench Creative Writing | 1627 | — |
| WildBench | 85.7% | — |
Frequently asked questions
Is GPT-5 better than Qwen1.5-32B?
GPT-5 is the stronger model overall, scoring 50.9 to 30.5 on the Noometry Index.
Is GPT-5 or Qwen1.5-32B better for coding?
GPT-5 scores higher on coding benchmarks: 50.3 versus 31.7 in the Noometry coding category.
How many benchmarks do GPT-5 and Qwen1.5-32B share?
18 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and Qwen1.5-32B has 21.