Model comparison
GPT-5.3 Codex vs Qwen1.5-14B
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 32.7 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 33.1.
- Qwen1.5-14B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Codex | Qwen1.5-14B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 45.8 | 32.7 |
| 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 | 17 |
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Category by category
Coding GPT-5.3 Codex leads
GPT-5.3 Codex: 48.6 (#56), Qwen1.5-14B: 33.1 (#263)
| Benchmark | GPT-5.3 Codex | Qwen1.5-14B |
|---|---|---|
| SWE-bench Verified | 74.8% | — |
| LMArena WebDev | 1409 | — |
| WeirdML | 79.3% | — |
| LMArena Coding | — | 1138 |
| ALE-Bench | 1,655 | — |
Agentic & Tool Use Not comparable
GPT-5.3 Codex: 48.0 (#9), Qwen1.5-14B: —
| Benchmark | GPT-5.3 Codex | Qwen1.5-14B |
|---|---|---|
| Terminal-Bench | 78.4% | — |
| METR Time Horizons | 74.5% | — |
| Vending-Bench 2 | 5,940 | — |
Reasoning Not comparable
GPT-5.3 Codex: —, Qwen1.5-14B: 21.4 (#223)
| Benchmark | GPT-5.3 Codex | Qwen1.5-14B |
|---|---|---|
| LMArena Hard Prompts | — | 1113 |
| Epoch Capabilities Index | 156.77 | — |
Math Not comparable
GPT-5.3 Codex: —, Qwen1.5-14B: 32.4 (#215)
| Benchmark | GPT-5.3 Codex | Qwen1.5-14B |
|---|---|---|
| LMArena Math | — | 1125 |
Knowledge Not comparable
GPT-5.3 Codex: —, Qwen1.5-14B: 29.8 (#232)
| Benchmark | GPT-5.3 Codex | Qwen1.5-14B |
|---|---|---|
| LMArena Expert | — | 1094 |
| MMLU | — | 68.6% |
Multilingual Not comparable
GPT-5.3 Codex: —, Qwen1.5-14B: 30.7 (#262)
| Benchmark | GPT-5.3 Codex | Qwen1.5-14B |
|---|---|---|
| LMArena Non-English | — | 1095 |
| LMArena Chinese | — | 1147 |
| LMArena French | — | 1116 |
| LMArena German | — | 1043 |
| LMArena Japanese | — | 1019 |
| LMArena Russian | — | 1046 |
| LMArena Spanish | — | 1085 |
Instruction Following Not comparable
GPT-5.3 Codex: —, Qwen1.5-14B: 56.8 (#271)
| Benchmark | GPT-5.3 Codex | Qwen1.5-14B |
|---|---|---|
| LMArena Instruction Following | — | 1102 |
Long Context Not comparable
GPT-5.3 Codex: —, Qwen1.5-14B: 33.7 (#257)
| Benchmark | GPT-5.3 Codex | Qwen1.5-14B |
|---|---|---|
| LMArena Longer Query | — | 1113 |
Writing & Preference Not comparable
GPT-5.3 Codex: —, Qwen1.5-14B: 33.6 (#276)
| Benchmark | GPT-5.3 Codex | Qwen1.5-14B |
|---|---|---|
| LMArena Text | — | 1128 |
| LMArena Creative Writing | — | 1091 |
| LMArena Multi-Turn | — | 1110 |
Frequently asked questions
Is GPT-5.3 Codex better than Qwen1.5-14B?
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 32.7 on the Noometry Index.
Is GPT-5.3 Codex or Qwen1.5-14B better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 33.1 in the Noometry coding category.
How many benchmarks do GPT-5.3 Codex and Qwen1.5-14B share?
0 benchmarks have published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and Qwen1.5-14B has 17.