Model comparison
GPT-5.3 Codex vs Qwen3-Coder 480B-A35B Instruct
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 38.1 on the Noometry Index. Qwen3-Coder 480B-A35B Instruct costs 1.6× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. GPT-5.3 Codex scores higher in 2 categories and Qwen3-Coder 480B-A35B Instruct in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 48.0 to 23.9.
- The biggest single-benchmark swing is Terminal-Bench: 78.4% for GPT-5.3 Codex and 27.2% for Qwen3-Coder 480B-A35B Instruct.
- Qwen3-Coder 480B-A35B Instruct is cheaper at $1.50 / $7.50 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 262K.
- Qwen3-Coder 480B-A35B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 45.8 | 38.1 |
| Released | 2026-02-05 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $1.75 | $1.50 |
| Output $ / M tokens | $14 | $7.50 |
| Results tracked | 8 | 25 |
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Category by category
Coding GPT-5.3 Codex leads
GPT-5.3 Codex: 48.6 (#56), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)
| Benchmark | GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1409 | 1275 |
| WeirdML | 79.3% | 41.2% |
| ALE-Bench | 1,655 | 461.45 |
| SWE-bench Verified | 74.8% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| GSO | — | 4.9% |
| LMArena Coding | — | 1412 |
| AlgoTune | — | 1.44 |
Agentic & Tool Use GPT-5.3 Codex leads
GPT-5.3 Codex: 48.0 (#9), Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)
| Benchmark | GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | 78.4% | 27.2% |
| METR Time Horizons | 74.5% | — |
| Vending-Bench 2 | 5,940 | — |
Reasoning Not comparable
GPT-5.3 Codex: —, Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)
| Benchmark | GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Kagi LLM Benchmark | — | 49.5% |
| LMArena Hard Prompts | — | 1372 |
| Epoch Capabilities Index | 156.77 | — |
Math Not comparable
GPT-5.3 Codex: —, Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)
| Benchmark | GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | — | 1365 |
Knowledge Not comparable
GPT-5.3 Codex: —, Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)
| Benchmark | GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | — | 1338 |
Multilingual Not comparable
GPT-5.3 Codex: —, Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)
| Benchmark | GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | — | 1346 |
| LMArena Chinese | — | 1357 |
| LMArena French | — | 1398 |
| LMArena German | — | 1325 |
| LMArena Japanese | — | 1310 |
| LMArena Korean | — | 1305 |
| LMArena Russian | — | 1366 |
| LMArena Spanish | — | 1360 |
Instruction Following Not comparable
GPT-5.3 Codex: —, Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)
| Benchmark | GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | — | 1355 |
Long Context Not comparable
GPT-5.3 Codex: —, Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)
| Benchmark | GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | — | 1378 |
Writing & Preference Not comparable
GPT-5.3 Codex: —, Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)
| Benchmark | GPT-5.3 Codex | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | — | 1357 |
| LMArena Creative Writing | — | 1333 |
| LMArena Multi-Turn | — | 1365 |
Frequently asked questions
Is GPT-5.3 Codex better than Qwen3-Coder 480B-A35B Instruct?
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 38.1 on the Noometry Index. Qwen3-Coder 480B-A35B Instruct costs 1.6× 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 Qwen3-Coder 480B-A35B Instruct?
Qwen3-Coder 480B-A35B Instruct is cheaper. It lists at $1.50 per million input tokens and $7.50 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.
Is GPT-5.3 Codex or Qwen3-Coder 480B-A35B Instruct better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 35.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 262K.
How many benchmarks do GPT-5.3 Codex and Qwen3-Coder 480B-A35B Instruct share?
4 benchmarks have published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.