Model comparison
GPT-5.3 Codex vs Qwen3 235B-A22B
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 43.5 on the Noometry Index. Qwen3 235B-A22B costs 3.9× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. GPT-5.3 Codex scores higher in 2 categories and Qwen3 235B-A22B 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 33.9.
- The biggest single-benchmark swing is WeirdML: 79.3% for GPT-5.3 Codex and 41% for Qwen3 235B-A22B.
- Qwen3 235B-A22B is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 131K.
- Qwen3 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Codex | Qwen3 235B-A22B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 45.8 | 43.5 |
| Released | 2026-02-05 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 400K | 131K |
| Max output | 128K | 16K |
| Input $ / M tokens | $1.75 | $0.70 |
| Output $ / M tokens | $14 | $2.80 |
| Results tracked | 8 | 49 |
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Category by category
Coding GPT-5.3 Codex leads
GPT-5.3 Codex: 48.6 (#56), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | GPT-5.3 Codex | Qwen3 235B-A22B |
|---|---|---|
| WeirdML | 79.3% | 41% |
| SWE-bench Verified | 74.8% | — |
| Aider Polyglot | — | 59.6% |
| LMArena WebDev | 1409 | — |
| SciCode | — | 42.4% |
| LMArena Coding | — | 1445 |
| ALE-Bench | 1,655 | — |
Agentic & Tool Use GPT-5.3 Codex leads
GPT-5.3 Codex: 48.0 (#9), Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | GPT-5.3 Codex | Qwen3 235B-A22B |
|---|---|---|
| Vending-Bench 2 | 5,940 | -11.34 |
| Terminal-Bench | 78.4% | — |
| Berkeley Function Calling Leaderboard | — | 52.1% |
| METR Time Horizons | 74.5% | — |
Reasoning Not comparable
GPT-5.3 Codex: —, Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | GPT-5.3 Codex | Qwen3 235B-A22B |
|---|---|---|
| Epoch Capabilities Index | 156.77 | 143.85 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| LMArena Hard Prompts | — | 1433 |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 29.3% |
| ForecastBench | — | 59.7 |
Math Not comparable
GPT-5.3 Codex: —, Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | GPT-5.3 Codex | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| Omni-MATH | — | 71.8% |
| LMArena Math | — | 1432 |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Not comparable
GPT-5.3 Codex: —, Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | GPT-5.3 Codex | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | — | 80.1% |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
| LMArena Expert | — | 1463 |
Multilingual Not comparable
GPT-5.3 Codex: —, Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | GPT-5.3 Codex | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | — | 1409 |
| LMArena Chinese | — | 1481 |
| LMArena French | — | 1445 |
| LMArena German | — | 1433 |
| LMArena Japanese | — | 1399 |
| LMArena Korean | — | 1391 |
| LMArena Russian | — | 1411 |
| LMArena Spanish | — | 1430 |
Instruction Following Not comparable
GPT-5.3 Codex: —, Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | GPT-5.3 Codex | Qwen3 235B-A22B |
|---|---|---|
| IFEval | — | 83.5% |
| LMArena Instruction Following | — | 1408 |
Long Context Not comparable
GPT-5.3 Codex: —, Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | GPT-5.3 Codex | Qwen3 235B-A22B |
|---|---|---|
| Fiction.LiveBench | — | 75% |
| LMArena Longer Query | — | 1426 |
Writing & Preference Not comparable
GPT-5.3 Codex: —, Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | GPT-5.3 Codex | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | — | 1419 |
| LMArena Creative Writing | — | 1384 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
| LMArena Multi-Turn | — | 1432 |
Frequently asked questions
Is GPT-5.3 Codex better than Qwen3 235B-A22B?
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 43.5 on the Noometry Index. Qwen3 235B-A22B costs 3.9× 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 235B-A22B?
Qwen3 235B-A22B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.
Is GPT-5.3 Codex or Qwen3 235B-A22B better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 44.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 131K.
How many benchmarks do GPT-5.3 Codex and Qwen3 235B-A22B share?
3 benchmarks have published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and Qwen3 235B-A22B has 49.