Model comparison
GPT-5.3 Codex vs Qwen2.5-Coder-32B
GPT-5.3 Codex is the stronger model overall, scoring 45.8 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 Codex's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. GPT-5.3 Codex scores higher in 1 category and Qwen2.5-Coder-32B in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in coding, where GPT-5.3 Codex leads 48.6 to 22.6.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Codex | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 45.8 | 33.4 |
| Released | 2026-02-05 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | 400K | 33K |
| Max output | 128K | 29K |
| Input $ / M tokens | $1.75 | $0.66 |
| Output $ / M tokens | $14 | $1 |
| Results tracked | 8 | 31 |
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Category by category
Coding GPT-5.3 Codex leads
GPT-5.3 Codex: 48.6 (#56), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | GPT-5.3 Codex | Qwen2.5-Coder-32B |
|---|---|---|
| SWE-bench Verified | 74.8% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1409 | — |
| WeirdML | 79.3% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| LMArena Coding | — | 1276 |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 1,655 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
GPT-5.3 Codex: 48.0 (#9), Qwen2.5-Coder-32B: —
| Benchmark | GPT-5.3 Codex | Qwen2.5-Coder-32B |
|---|---|---|
| Terminal-Bench | 78.4% | — |
| METR Time Horizons | 74.5% | — |
| Vending-Bench 2 | 5,940 | — |
Reasoning Not comparable
GPT-5.3 Codex: —, Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | GPT-5.3 Codex | Qwen2.5-Coder-32B |
|---|---|---|
| Epoch Capabilities Index | 156.77 | 119.49 |
| LiveBench Reasoning | — | 42.1% |
| LMArena Hard Prompts | — | 1251 |
| LiveBench Data Analysis | — | 49.9% |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Not comparable
GPT-5.3 Codex: —, Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | GPT-5.3 Codex | Qwen2.5-Coder-32B |
|---|---|---|
| LiveBench Math | — | 46.6% |
| LMArena Math | — | 1251 |
| GSM8K | — | 93% |
Knowledge Not comparable
GPT-5.3 Codex: —, Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | GPT-5.3 Codex | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | — | 1221 |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multilingual Not comparable
GPT-5.3 Codex: —, Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | GPT-5.3 Codex | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | — | 1205 |
| LMArena Chinese | — | 1222 |
| LMArena Russian | — | 1228 |
Instruction Following Not comparable
GPT-5.3 Codex: —, Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | GPT-5.3 Codex | Qwen2.5-Coder-32B |
|---|---|---|
| LiveBench Instruction Following | — | 58.7% |
| LMArena Instruction Following | — | 1223 |
Long Context Not comparable
GPT-5.3 Codex: —, Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | GPT-5.3 Codex | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | — | 1251 |
Writing & Preference Not comparable
GPT-5.3 Codex: —, Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | GPT-5.3 Codex | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | — | 1230 |
| LMArena Creative Writing | — | 1174 |
| LMArena Multi-Turn | — | 1222 |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is GPT-5.3 Codex better than Qwen2.5-Coder-32B?
GPT-5.3 Codex is the stronger model overall, scoring 45.8 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 Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5.3 Codex 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 Codex lists at $1.75 and $14.
Is GPT-5.3 Codex or Qwen2.5-Coder-32B better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 33K.
How many benchmarks do GPT-5.3 Codex and Qwen2.5-Coder-32B share?
1 benchmark has published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and Qwen2.5-Coder-32B has 31.