Model comparison
GPT-5.3 Codex vs Qwen1.5-32B
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 30.5 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in coding, where GPT-5.3 Codex leads 48.6 to 31.7.
- Qwen1.5-32B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Codex | Qwen1.5-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 45.8 | 30.5 |
| Released | 2026-02-05 | 2024-02-04 |
| Weights | Proprietary | Open |
| Context window | 400K | — |
| Max output | 128K | — |
| Input $ / M tokens | $1.75 | — |
| Output $ / M tokens | $14 | — |
| Results tracked | 8 | 21 |
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Category by category
Coding GPT-5.3 Codex leads
GPT-5.3 Codex: 48.6 (#56), Qwen1.5-32B: 31.7 (#282)
| Benchmark | GPT-5.3 Codex | Qwen1.5-32B |
|---|---|---|
| SWE-bench Verified | 74.8% | — |
| LMArena WebDev | 1409 | — |
| WeirdML | 79.3% | — |
| BigCodeBench Instruct | — | 32.3% |
| LMArena Coding | — | 1155 |
| BigCodeBench Complete | — | 42% |
| ALE-Bench | 1,655 | — |
Agentic & Tool Use Not comparable
GPT-5.3 Codex: 48.0 (#9), Qwen1.5-32B: —
| Benchmark | GPT-5.3 Codex | Qwen1.5-32B |
|---|---|---|
| Terminal-Bench | 78.4% | — |
| METR Time Horizons | 74.5% | — |
| Vending-Bench 2 | 5,940 | — |
Reasoning Not comparable
GPT-5.3 Codex: —, Qwen1.5-32B: 21.8 (#212)
| Benchmark | GPT-5.3 Codex | Qwen1.5-32B |
|---|---|---|
| LMArena Hard Prompts | — | 1130 |
| Epoch Capabilities Index | 156.77 | — |
Math Not comparable
GPT-5.3 Codex: —, Qwen1.5-32B: 33.0 (#207)
| Benchmark | GPT-5.3 Codex | Qwen1.5-32B |
|---|---|---|
| LMArena Math | — | 1155 |
Knowledge Not comparable
GPT-5.3 Codex: —, Qwen1.5-32B: 13.5 (#296)
| Benchmark | GPT-5.3 Codex | Qwen1.5-32B |
|---|---|---|
| GPQA Diamond | — | 30.7% |
| LMArena Expert | — | 1126 |
| MMLU | — | 74.4% |
Multilingual Not comparable
GPT-5.3 Codex: —, Qwen1.5-32B: 31.4 (#259)
| Benchmark | GPT-5.3 Codex | Qwen1.5-32B |
|---|---|---|
| LMArena Non-English | — | 1106 |
| LMArena Chinese | — | 1177 |
| LMArena French | — | 1101 |
| LMArena German | — | 1058 |
| LMArena Japanese | — | 1027 |
| LMArena Korean | — | 1008 |
| LMArena Russian | — | 1073 |
| LMArena Spanish | — | 1089 |
Instruction Following Not comparable
GPT-5.3 Codex: —, Qwen1.5-32B: 57.7 (#265)
| Benchmark | GPT-5.3 Codex | Qwen1.5-32B |
|---|---|---|
| LMArena Instruction Following | — | 1116 |
Long Context Not comparable
GPT-5.3 Codex: —, Qwen1.5-32B: 34.7 (#246)
| Benchmark | GPT-5.3 Codex | Qwen1.5-32B |
|---|---|---|
| LMArena Longer Query | — | 1146 |
Writing & Preference Not comparable
GPT-5.3 Codex: —, Qwen1.5-32B: 34.2 (#271)
| Benchmark | GPT-5.3 Codex | Qwen1.5-32B |
|---|---|---|
| LMArena Text | — | 1137 |
| LMArena Creative Writing | — | 1083 |
| LMArena Multi-Turn | — | 1140 |
Frequently asked questions
Is GPT-5.3 Codex better than Qwen1.5-32B?
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 30.5 on the Noometry Index.
Is GPT-5.3 Codex or Qwen1.5-32B better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 31.7 in the Noometry coding category.
How many benchmarks do GPT-5.3 Codex and Qwen1.5-32B share?
0 benchmarks have published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and Qwen1.5-32B has 21.