Model comparison
GPT-5 vs Qwen2.5-Max
GPT-5 is the stronger model overall, scoring 50.9 to 40.7 on the Noometry Index.
Last verified . 20 shared benchmarks.
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. GPT-5 scores higher in 8 categories and Qwen2.5-Max in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 41.4.
- The biggest single-benchmark swing is Short-Story Creative Writing: 86% for GPT-5 and 72.9% for Qwen2.5-Max.
Side by side
| GPT-5 | Qwen2.5-Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 50.9 | 40.7 |
| Released | 2025-08-07 | 2025-01-25 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | — |
| Max output | 128K | — |
| Input $ / M tokens | $1.25 | — |
| Output $ / M tokens | $10 | — |
| Results tracked | 69 | 27 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5 leads
GPT-5: 50.3 (#47), Qwen2.5-Max: 41.8 (#117)
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| LMArena Coding | 1436 | 1359 |
| 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% | — |
| LiveBench Coding | — | 64.4% |
| ALE-Bench | 1,162 | — |
| AlgoTune | 1.67 | — |
Agentic & Tool Use Not comparable
GPT-5: 33.1 (#56), Qwen2.5-Max: —
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| 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), Qwen2.5-Max: 25.6 (#147)
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| LMArena Hard Prompts | 1416 | 1360 |
| Epoch Capabilities Index | 150 | 132.53 |
| 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% | — |
| LiveBench Reasoning | — | 51.4% |
| Mystery Game Puzzles | 23% | — |
| DTBench | 90.7% | — |
| LiveBench Data Analysis | — | 67.9% |
| LMCA | 40% | — |
| ForecastBench | 61.4 | — |
| LiveBench | — | 62.3% |
Math GPT-5 leads
GPT-5: 55.0 (#44), Qwen2.5-Max: 36.9 (#162)
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| LMArena Math | 1407 | 1369 |
| FrontierMath (Tiers 1-3) | 55.4% | — |
| FrontierMath Tier 4 | 22% | — |
| OTIS Mock AIME 2024-2025 | 91.4% | — |
| ProofBench | 18% | — |
| Omni-MATH | 64.7% | — |
| LiveBench Math | — | 58.4% |
| 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), Qwen2.5-Max: 35.3 (#186)
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| Confabulations | 10.3% | 21.8% |
| LMArena Expert | 1419 | 1337 |
| GPQA Diamond | 86.2% | — |
| Humanity's Last Exam | 25.3% | — |
| SimpleQA Verified | 50.1% | — |
| MMLU-Pro | 86.3% | — |
| Vectara Hallucination Rate | 14.7% | — |
| GPQA (HELM) | 79.2% | — |
Multimodal Not comparable
GPT-5: 46.8 (#13), Qwen2.5-Max: —
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| LMArena Vision | 1232 | — |
| GeoBench | 81% | — |
| VPCT | 66% | — |
Multilingual GPT-5 leads
GPT-5: 51.4 (#110), Qwen2.5-Max: 48.1 (#146)
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| LMArena Non-English | 1397 | 1352 |
| LMArena Chinese | 1422 | 1382 |
| LMArena French | 1410 | 1396 |
| LMArena German | 1416 | 1350 |
| LMArena Japanese | 1409 | 1300 |
| LMArena Korean | 1360 | 1304 |
| LMArena Russian | 1406 | 1353 |
| LMArena Spanish | 1399 | 1377 |
Instruction Following GPT-5 leads
GPT-5: 73.8 (#113), Qwen2.5-Max: 71.3 (#152)
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| LMArena Instruction Following | 1388 | 1335 |
| LiveBench Instruction Following | — | 75.3% |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), Qwen2.5-Max: 41.4 (#142)
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| LMArena Longer Query | 1399 | 1358 |
| Fiction.LiveBench | 97.2% | — |
Writing & Preference GPT-5 leads
GPT-5: 63.4 (#65), Qwen2.5-Max: 55.4 (#146)
| Benchmark | GPT-5 | Qwen2.5-Max |
|---|---|---|
| LMArena Text | 1406 | 1367 |
| LMArena Creative Writing | 1365 | 1339 |
| Short-Story Creative Writing | 86% | 72.9% |
| LMArena Multi-Turn | 1426 | 1364 |
| EQ-Bench Creative Writing | 1627 | — |
| WildBench | 85.7% | — |
| LiveBench Language | — | 56.3% |
Frequently asked questions
Is GPT-5 better than Qwen2.5-Max?
GPT-5 is the stronger model overall, scoring 50.9 to 40.7 on the Noometry Index.
Is GPT-5 or Qwen2.5-Max better for coding?
GPT-5 scores higher on coding benchmarks: 50.3 versus 41.8 in the Noometry coding category.
How many benchmarks do GPT-5 and Qwen2.5-Max share?
20 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and Qwen2.5-Max has 27.