Model comparison
GPT-5.3 Chat vs Qwen2.5-Coder-32B
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 6.5× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
Alibaba (Qwen)Summary
- They share 12 benchmarks with published results for both. GPT-5.3 Chat scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-5.3 Chat leads 63.1 to 41.6.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- GPT-5.3 Chat accepts more context: 128K tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Chat | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.8 | 33.4 |
| Released | 2026-03-03 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | 128K | 33K |
| Max output | 16K | 29K |
| Input $ / M tokens | $1.75 | $0.66 |
| Output $ / M tokens | $14 | $1 |
| Results tracked | 18 | 31 |
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Category by category
Coding GPT-5.3 Chat leads
GPT-5.3 Chat: 41.4 (#124), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | GPT-5.3 Chat | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1408 | 1276 |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Reasoning GPT-5.3 Chat leads
GPT-5.3 Chat: 28.5 (#102), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | GPT-5.3 Chat | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1251 |
| LiveBench Reasoning | — | 42.1% |
| LiveBench Data Analysis | — | 49.9% |
| Epoch Capabilities Index | — | 119.49 |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math GPT-5.3 Chat leads
GPT-5.3 Chat: 38.2 (#142), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | GPT-5.3 Chat | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1389 | 1251 |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |
Knowledge GPT-5.3 Chat leads
GPT-5.3 Chat: 38.8 (#140), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | GPT-5.3 Chat | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1397 | 1221 |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multilingual GPT-5.3 Chat leads
GPT-5.3 Chat: 50.3 (#124), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | GPT-5.3 Chat | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1382 | 1205 |
| LMArena Chinese | 1432 | 1222 |
| LMArena Russian | 1400 | 1228 |
| LMArena French | 1397 | — |
| LMArena German | 1384 | — |
| LMArena Japanese | 1352 | — |
| LMArena Korean | 1346 | — |
| LMArena Spanish | 1371 | — |
Instruction Following GPT-5.3 Chat leads
GPT-5.3 Chat: 72.8 (#129), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | GPT-5.3 Chat | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1378 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
Long Context GPT-5.3 Chat leads
GPT-5.3 Chat: 42.6 (#120), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | GPT-5.3 Chat | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1396 | 1251 |
Writing & Preference GPT-5.3 Chat leads
GPT-5.3 Chat: 63.1 (#68), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | GPT-5.3 Chat | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1389 | 1230 |
| LMArena Creative Writing | 1355 | 1174 |
| LMArena Multi-Turn | 1412 | 1222 |
| EQ-Bench Creative Writing | 1690 | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is GPT-5.3 Chat better than Qwen2.5-Coder-32B?
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 6.5× 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-Coder-32B?
Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.
Is GPT-5.3 Chat or Qwen2.5-Coder-32B better for coding?
GPT-5.3 Chat scores higher on coding benchmarks: 41.4 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Chat does, with 128K tokens against 33K.
How many benchmarks do GPT-5.3 Chat and Qwen2.5-Coder-32B share?
12 benchmarks have published results for both models. GPT-5.3 Chat has 18 scored results on Noometry and Qwen2.5-Coder-32B has 31.