Model comparison
GPT-4.5 vs Qwen2.5 72B Instruct
GPT-4.5 is the stronger model overall, scoring 37.2 to 31.9 on the Noometry Index.
Last verified . 23 shared benchmarks.
Alibaba (Qwen)Summary
- They share 23 benchmarks with published results for both. GPT-4.5 scores higher in 8 categories and Qwen2.5 72B Instruct in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-4.5 leads 32.6 to 19.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for GPT-4.5 and 8.1% for Qwen2.5 72B Instruct.
- Qwen2.5 72B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-4.5 | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 37.2 | 31.9 |
| Released | 2025-02-27 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | — | 131K |
| Max output | — | 8K |
| Input $ / M tokens | — | $1.40 |
| Output $ / M tokens | — | $5.60 |
| Results tracked | 42 | 43 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-4.5 leads
GPT-4.5: 42.2 (#109), Qwen2.5 72B Instruct: 33.2 (#260)
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| WeirdML | 39.4% | 16% |
| LMArena Coding | 1396 | 1292 |
| Aider Polyglot | 44.9% | — |
| BigCodeBench Instruct | — | 45.8% |
| LiveBench Coding | 75.2% | — |
| BigCodeBench Complete | — | 55.9% |
Agentic & Tool Use GPT-4.5 leads
GPT-4.5: 27.9 (#97), Qwen2.5 72B Instruct: 22.1 (#133)
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| TheAgentCompany | — | 5.7% |
| Cybench | 17.5% | — |
| BALROG | — | 16.2% |
| METR Time Horizons | — | 35.8% |
Reasoning Qwen2.5 72B Instruct leads
GPT-4.5: 13.9 (#330), Qwen2.5 72B Instruct: 22.3 (#199)
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1403 | 1271 |
| Epoch Capabilities Index | 136.74 | 129 |
| ForecastBench | 61.7 | 57.5 |
| ARC-AGI-2 | 0.8% | — |
| SimpleBench | 34.5% | — |
| ARC-AGI-1 | 10.3% | — |
| EnigmaEval | 3.2% | — |
| LiveBench Reasoning | 71.1% | — |
| DTBench | — | 62.9% |
| LiveBench Data Analysis | 64.3% | — |
| LMCA | — | 13.4% |
| BIG-Bench Hard | — | 79.8% |
| HellaSwag | — | 84.8% |
| LiveBench | 69% | — |
| PIQA | — | 82.6% |
| WinoGrande | — | 82.3% |
Math GPT-4.5 leads
GPT-4.5: 32.6 (#211), Qwen2.5 72B Instruct: 19.3 (#287)
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 8.1% |
| LMArena Math | 1412 | 1283 |
| MATH Level 5 | 78.6% | 63.2% |
| Omni-MATH | — | 33% |
| LiveBench Math | 69.3% | — |
Knowledge GPT-4.5 leads
GPT-4.5: 32.5 (#211), Qwen2.5 72B Instruct: 27.0 (#253)
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| GPQA Diamond | 68.7% | 49.1% |
| Confabulations | 13.6% | 19.1% |
| LMArena Expert | 1394 | 1245 |
| Humanity's Last Exam | 5.4% | — |
| MMLU-Pro | — | 63.1% |
| GPQA (HELM) | — | 42.6% |
| ARC (AI2) Challenge | — | 94.5% |
| MMLU | — | 85.3% |
| TriviaQA | — | 71.9% |
Multimodal Not comparable
GPT-4.5: 37.6 (#71), Qwen2.5 72B Instruct: —
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Vision | 1195 | — |
| VPCT | 45% | — |
Multilingual GPT-4.5 leads
GPT-4.5: 52.5 (#83), Qwen2.5 72B Instruct: 41.0 (#213)
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1413 | 1252 |
| LMArena Chinese | 1421 | 1272 |
| LMArena French | 1418 | 1280 |
| LMArena German | 1457 | 1234 |
| LMArena Japanese | 1416 | 1180 |
| LMArena Korean | 1392 | 1188 |
| LMArena Russian | 1419 | 1264 |
| LMArena Spanish | — | 1256 |
Instruction Following GPT-4.5 leads
GPT-4.5: 72.6 (#134), Qwen2.5 72B Instruct: 65.5 (#221)
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1404 | 1254 |
| LiveBench Instruction Following | 72.3% | — |
| IFEval | — | 80.6% |
Long Context GPT-4.5 leads
GPT-4.5: 40.4 (#155), Qwen2.5 72B Instruct: 38.9 (#188)
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1406 | 1282 |
| Fiction.LiveBench | 63.9% | — |
Writing & Preference GPT-4.5 leads
GPT-4.5: 56.9 (#134), Qwen2.5 72B Instruct: 46.7 (#215)
| Benchmark | GPT-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1417 | 1269 |
| LMArena Creative Writing | 1394 | 1221 |
| LMArena Multi-Turn | 1444 | 1272 |
| Short-Story Creative Writing | 75.6% | — |
| EQ-Bench Creative Writing | 1258 | — |
| WildBench | — | 80.2% |
| LiveBench Language | 61.5% | — |
Frequently asked questions
Is GPT-4.5 better than Qwen2.5 72B Instruct?
GPT-4.5 is the stronger model overall, scoring 37.2 to 31.9 on the Noometry Index.
Is GPT-4.5 or Qwen2.5 72B Instruct better for coding?
GPT-4.5 scores higher on coding benchmarks: 42.2 versus 33.2 in the Noometry coding category.
How many benchmarks do GPT-4.5 and Qwen2.5 72B Instruct share?
23 benchmarks have published results for both models. GPT-4.5 has 42 scored results on Noometry and Qwen2.5 72B Instruct has 43.