Model comparison
GPT-5.3 Chat vs Qwen2.5 72B Instruct
GPT-5.3 Chat is the stronger model overall, scoring 42.1 to 31.5 on the Noometry Index. Qwen2.5 72B Instruct costs 2.0× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. GPT-5.3 Chat scores higher in 8 categories and Qwen2.5 72B Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.3 Chat leads 35.4 to 18.1.
- Qwen2.5 72B Instruct is cheaper at $1.40 / $5.60 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- Qwen2.5 72B Instruct accepts more context: 131K tokens versus 128K.
- Qwen2.5 72B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Chat | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.1 | 31.5 |
| Released | 2026-03-03 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 128K | 131K |
| Max output | 16K | 8K |
| Input $ / M tokens | $1.75 | $1.40 |
| Output $ / M tokens | $14 | $5.60 |
| Results tracked | 18 | 43 |
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Category by category
Coding GPT-5.3 Chat leads
GPT-5.3 Chat: 40.8 (#124), Qwen2.5 72B Instruct: 32.8 (#256)
| Benchmark | GPT-5.3 Chat | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Coding | 1408 | 1293 |
| WeirdML | — | 16% |
| BigCodeBench Instruct | — | 45.8% |
| BigCodeBench Complete | — | 55.9% |
Agentic & Tool Use Not comparable
GPT-5.3 Chat: —, Qwen2.5 72B Instruct: 22.0 (#124)
| Benchmark | GPT-5.3 Chat | Qwen2.5 72B Instruct |
|---|---|---|
| TheAgentCompany | — | 5.7% |
| BALROG | — | 16.2% |
| METR Time Horizons | — | 35.8% |
Reasoning GPT-5.3 Chat leads
GPT-5.3 Chat: 27.3 (#105), Qwen2.5 72B Instruct: 21.4 (#197)
| Benchmark | GPT-5.3 Chat | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1271 |
| DTBench | — | 62.9% |
| LMCA | — | 13.4% |
| BIG-Bench Hard | — | 79.8% |
| Epoch Capabilities Index | — | 129.01 |
| ForecastBench | — | 57.5 |
| HellaSwag | — | 84.8% |
| PIQA | — | 82.6% |
| WinoGrande | — | 82.3% |
Math GPT-5.3 Chat leads
GPT-5.3 Chat: 35.4 (#146), Qwen2.5 72B Instruct: 18.1 (#281)
| Benchmark | GPT-5.3 Chat | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Math | 1389 | 1283 |
| OTIS Mock AIME 2024-2025 | — | 8.1% |
| Omni-MATH | — | 33% |
| MATH Level 5 | — | 63.2% |
Knowledge GPT-5.3 Chat leads
GPT-5.3 Chat: 38.2 (#137), Qwen2.5 72B Instruct: 26.9 (#247)
| Benchmark | GPT-5.3 Chat | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Expert | 1397 | 1245 |
| GPQA Diamond | — | 49.1% |
| MMLU-Pro | — | 63.1% |
| Confabulations | — | 19.1% |
| GPQA (HELM) | — | 42.6% |
| ARC (AI2) Challenge | — | 94.5% |
| MMLU | — | 85.3% |
| TriviaQA | — | 71.9% |
Multilingual GPT-5.3 Chat leads
GPT-5.3 Chat: 50.5 (#119), Qwen2.5 72B Instruct: 41.2 (#208)
| Benchmark | GPT-5.3 Chat | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1383 | 1252 |
| LMArena Chinese | 1433 | 1272 |
| LMArena French | 1398 | 1279 |
| LMArena German | 1384 | 1234 |
| LMArena Japanese | 1351 | 1180 |
| LMArena Korean | 1347 | 1188 |
| LMArena Russian | 1400 | 1264 |
| LMArena Spanish | 1371 | 1255 |
Instruction Following GPT-5.3 Chat leads
GPT-5.3 Chat: 73.0 (#124), Qwen2.5 72B Instruct: 65.7 (#216)
| Benchmark | GPT-5.3 Chat | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1378 | 1254 |
| IFEval | — | 80.6% |
Long Context GPT-5.3 Chat leads
GPT-5.3 Chat: 42.6 (#115), Qwen2.5 72B Instruct: 38.9 (#183)
| Benchmark | GPT-5.3 Chat | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1396 | 1282 |
Writing & Preference GPT-5.3 Chat leads
GPT-5.3 Chat: 63.3 (#65), Qwen2.5 72B Instruct: 46.9 (#210)
| Benchmark | GPT-5.3 Chat | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1389 | 1269 |
| LMArena Creative Writing | 1356 | 1221 |
| LMArena Multi-Turn | 1413 | 1272 |
| EQ-Bench Creative Writing | 1690 | — |
| WildBench | — | 80.2% |
Frequently asked questions
Is GPT-5.3 Chat better than Qwen2.5 72B Instruct?
GPT-5.3 Chat is the stronger model overall, scoring 42.1 to 31.5 on the Noometry Index. Qwen2.5 72B Instruct costs 2.0× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Which is cheaper, GPT-5.3 Chat or Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct is cheaper. It lists at $1.40 per million input tokens and $5.60 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.
Is GPT-5.3 Chat or Qwen2.5 72B Instruct better for coding?
GPT-5.3 Chat scores higher on coding benchmarks: 40.8 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
Qwen2.5 72B Instruct does, with 131K tokens against 128K.
How many benchmarks do GPT-5.3 Chat and Qwen2.5 72B Instruct share?
17 benchmarks have published results for both models. GPT-5.3 Chat has 18 scored results on Noometry and Qwen2.5 72B Instruct has 43.