Model comparison
GPT-5.3 Codex vs Qwen3.5 397B-A17B
GPT-5.3 Codex and Qwen3.5 397B-A17B score almost the same on the Noometry Index (45.8 vs 46.0), so choose on price, context window or the category you care about most.
Last verified . 2 shared benchmarks.
Alibaba (Qwen)Summary
- They share 2 benchmarks with published results for both. GPT-5.3 Codex scores higher in 2 categories and Qwen3.5 397B-A17B 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 33.3.
- Qwen3.5 397B-A17B is cheaper at $0.60 / $3.60 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.
- Qwen3.5 397B-A17B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Codex | Qwen3.5 397B-A17B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 45.8 | 46.0 |
| Released | 2026-02-05 | 2026-02-01 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $1.75 | $0.60 |
| Output $ / M tokens | $14 | $3.60 |
| Results tracked | 8 | 36 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.3 Codex leads
GPT-5.3 Codex: 48.6 (#56), Qwen3.5 397B-A17B: 42.0 (#114)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena WebDev | 1409 | 1400 |
| SWE-bench Verified | 74.8% | — |
| WeirdML | 79.3% | — |
| LMArena Coding | — | 1465 |
| ALE-Bench | 1,655 | — |
Agentic & Tool Use GPT-5.3 Codex leads
GPT-5.3 Codex: 48.0 (#9), Qwen3.5 397B-A17B: 33.3 (#53)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| Terminal-Bench | 78.4% | — |
| APEX-Agents | — | 24.9% |
| τ²-bench Airline | — | 81.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 84.4% |
| τ²-bench Telecom | — | 97.8% |
| METR Time Horizons | 74.5% | — |
| Vending-Bench 2 | 5,940 | — |
Reasoning Not comparable
GPT-5.3 Codex: —, Qwen3.5 397B-A17B: 34.5 (#70)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| Epoch Capabilities Index | 156.77 | 146.65 |
| Kagi LLM Benchmark | — | 73.7% |
| NYT Connections (extended) | — | 58.9% |
| Chess Puzzles | — | 13% |
| Thematic Generalization | — | 65.1% |
| LMArena Hard Prompts | — | 1448 |
| Mystery Game Puzzles | — | 18% |
| DTBench | — | 87.5% |
| LMCA | — | 37.9% |
Math Not comparable
GPT-5.3 Codex: —, Qwen3.5 397B-A17B: 46.1 (#73)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 31.2% |
| OTIS Mock AIME 2024-2025 | — | 88.9% |
| LMArena Math | — | 1454 |
Knowledge Not comparable
GPT-5.3 Codex: —, Qwen3.5 397B-A17B: 53.3 (#58)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| GPQA Diamond | — | 86.4% |
| LMArena Expert | — | 1462 |
Multimodal Not comparable
GPT-5.3 Codex: —, Qwen3.5 397B-A17B: 40.7 (#44)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Vision | — | 1263 |
Multilingual Not comparable
GPT-5.3 Codex: —, Qwen3.5 397B-A17B: 53.7 (#59)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Non-English | — | 1430 |
| LMArena Chinese | — | 1500 |
| LMArena French | — | 1461 |
| LMArena German | — | 1447 |
| LMArena Japanese | — | 1426 |
| LMArena Korean | — | 1384 |
| LMArena Russian | — | 1429 |
| LMArena Spanish | — | 1441 |
Instruction Following Not comparable
GPT-5.3 Codex: —, Qwen3.5 397B-A17B: 75.0 (#77)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Instruction Following | — | 1424 |
Long Context Not comparable
GPT-5.3 Codex: —, Qwen3.5 397B-A17B: 44.1 (#74)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Longer Query | — | 1442 |
Writing & Preference Not comparable
GPT-5.3 Codex: —, Qwen3.5 397B-A17B: 62.3 (#79)
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B-A17B |
|---|---|---|
| LMArena Text | — | 1438 |
| LMArena Creative Writing | — | 1401 |
| EQ-Bench Creative Writing | — | 1478 |
| LMArena Multi-Turn | — | 1446 |
Frequently asked questions
Is GPT-5.3 Codex better than Qwen3.5 397B-A17B?
GPT-5.3 Codex and Qwen3.5 397B-A17B score almost the same on the Noometry Index (45.8 vs 46.0), so choose on price, context window or the category you care about most.
Which is cheaper, GPT-5.3 Codex or Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is cheaper. It lists at $0.60 per million input tokens and $3.60 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.
Is GPT-5.3 Codex or Qwen3.5 397B-A17B better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 42.0 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 Qwen3.5 397B-A17B share?
2 benchmarks have published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and Qwen3.5 397B-A17B has 36.