Model comparison
GPT-5 Pro vs Qwen1.5-72B
GPT-5 Pro is the stronger model overall, scoring 46.2 to 30.3 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in knowledge, where GPT-5 Pro leads 56.2 to 11.5.
- Qwen1.5-72B has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Pro | Qwen1.5-72B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 46.2 | 30.3 |
| Released | 2025-10-06 | 2024-02-04 |
| Weights | Proprietary | Open |
| Context window | 400K | — |
| Max output | 272K | — |
| Input $ / M tokens | $15 | — |
| Output $ / M tokens | $120 | — |
| Results tracked | 12 | 22 |
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Category by category
Coding GPT-5 Pro leads
GPT-5 Pro: 43.4 (#77), Qwen1.5-72B: 31.4 (#272)
| Benchmark | GPT-5 Pro | Qwen1.5-72B |
|---|---|---|
| WeirdML | 60.4% | — |
| BigCodeBench Instruct | — | 33.2% |
| LMArena Coding | — | 1166 |
| BigCodeBench Complete | — | 40.3% |
| AlgoTune | 1.31 | — |
| HumanEval+ | — | 59.1% |
| MBPP+ | — | 61.6% |
Reasoning GPT-5 Pro leads
GPT-5 Pro: 39.2 (#52), Qwen1.5-72B: 21.2 (#201)
| Benchmark | GPT-5 Pro | Qwen1.5-72B |
|---|---|---|
| ARC-AGI-2 | 18.3% | — |
| SimpleBench | 61.6% | — |
| Kagi LLM Benchmark | 76.8% | — |
| ARC-AGI-1 | 70.2% | — |
| EnigmaEval | 18.8% | — |
| LMArena Hard Prompts | — | 1148 |
| Epoch Capabilities Index | 150.26 | — |
Math GPT-5 Pro leads
GPT-5 Pro: 48.0 (#53), Qwen1.5-72B: 30.7 (#205)
| Benchmark | GPT-5 Pro | Qwen1.5-72B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 19.5% | — |
| LMArena Math | — | 1164 |
| FrontierMath Tier 4 (v1) | 14.6% | — |
Knowledge GPT-5 Pro leads
GPT-5 Pro: 56.2 (#39), Qwen1.5-72B: 11.5 (#295)
| Benchmark | GPT-5 Pro | Qwen1.5-72B |
|---|---|---|
| GPQA Diamond | — | 28.8% |
| Humanity's Last Exam | 31.6% | — |
| LMArena Expert | — | 1136 |
Multilingual Not comparable
GPT-5 Pro: —, Qwen1.5-72B: 33.4 (#248)
| Benchmark | GPT-5 Pro | Qwen1.5-72B |
|---|---|---|
| LMArena Non-English | — | 1135 |
| LMArena Chinese | — | 1186 |
| LMArena French | — | 1158 |
| LMArena German | — | 1084 |
| LMArena Japanese | — | 1061 |
| LMArena Korean | — | 1049 |
| LMArena Russian | — | 1104 |
| LMArena Spanish | — | 1110 |
Instruction Following Not comparable
GPT-5 Pro: —, Qwen1.5-72B: 59.6 (#251)
| Benchmark | GPT-5 Pro | Qwen1.5-72B |
|---|---|---|
| LMArena Instruction Following | — | 1142 |
Long Context Not comparable
GPT-5 Pro: —, Qwen1.5-72B: 35.0 (#238)
| Benchmark | GPT-5 Pro | Qwen1.5-72B |
|---|---|---|
| LMArena Longer Query | — | 1157 |
Writing & Preference Not comparable
GPT-5 Pro: —, Qwen1.5-72B: 37.4 (#253)
| Benchmark | GPT-5 Pro | Qwen1.5-72B |
|---|---|---|
| LMArena Text | — | 1166 |
| LMArena Creative Writing | — | 1137 |
| LMArena Multi-Turn | — | 1160 |
Frequently asked questions
Is GPT-5 Pro better than Qwen1.5-72B?
GPT-5 Pro is the stronger model overall, scoring 46.2 to 30.3 on the Noometry Index.
Is GPT-5 Pro or Qwen1.5-72B better for coding?
GPT-5 Pro scores higher on coding benchmarks: 43.4 versus 31.4 in the Noometry coding category.
How many benchmarks do GPT-5 Pro and Qwen1.5-72B share?
0 benchmarks have published results for both models. GPT-5 Pro has 12 scored results on Noometry and Qwen1.5-72B has 22.