Model comparison
GPT-5.3 Chat vs Qwen3 235B-A22B
GPT-5.3 Chat and Qwen3 235B-A22B score almost the same on the Noometry Index (42.8 vs 43.5), so choose on price, context window or the category you care about most.
Last verified . 18 shared benchmarks.
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. GPT-5.3 Chat scores higher in 3 categories and Qwen3 235B-A22B in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.3 Chat leads 28.5 to 15.7.
- Qwen3 235B-A22B is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- Qwen3 235B-A22B accepts more context: 131K tokens versus 128K.
- Qwen3 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Chat | Qwen3 235B-A22B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.8 | 43.5 |
| Released | 2026-03-03 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 128K | 131K |
| Max output | 16K | 16K |
| Input $ / M tokens | $1.75 | $0.70 |
| Output $ / M tokens | $14 | $2.80 |
| Results tracked | 18 | 49 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3 235B-A22B leads
GPT-5.3 Chat: 41.4 (#124), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | GPT-5.3 Chat | Qwen3 235B-A22B |
|---|---|---|
| LMArena Coding | 1408 | 1445 |
| Aider Polyglot | — | 59.6% |
| SciCode | — | 42.4% |
| WeirdML | — | 41% |
Agentic & Tool Use Not comparable
GPT-5.3 Chat: —, Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | GPT-5.3 Chat | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning GPT-5.3 Chat leads
GPT-5.3 Chat: 28.5 (#102), Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | GPT-5.3 Chat | Qwen3 235B-A22B |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1433 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 29.3% |
| Epoch Capabilities Index | — | 143.85 |
| ForecastBench | — | 59.7 |
Math Qwen3 235B-A22B leads
GPT-5.3 Chat: 38.2 (#142), Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | GPT-5.3 Chat | Qwen3 235B-A22B |
|---|---|---|
| LMArena Math | 1389 | 1432 |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| Omni-MATH | — | 71.8% |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3 235B-A22B leads
GPT-5.3 Chat: 38.8 (#140), Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | GPT-5.3 Chat | Qwen3 235B-A22B |
|---|---|---|
| LMArena Expert | 1397 | 1463 |
| GPQA Diamond | — | 80.1% |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
Multilingual Qwen3 235B-A22B leads
GPT-5.3 Chat: 50.3 (#124), Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | GPT-5.3 Chat | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1382 | 1409 |
| LMArena Chinese | 1432 | 1481 |
| LMArena French | 1397 | 1445 |
| LMArena German | 1384 | 1433 |
| LMArena Japanese | 1352 | 1399 |
| LMArena Korean | 1346 | 1391 |
| LMArena Russian | 1400 | 1411 |
| LMArena Spanish | 1371 | 1430 |
Instruction Following Too close to call
GPT-5.3 Chat: 72.8 (#129), Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | GPT-5.3 Chat | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1378 | 1408 |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
GPT-5.3 Chat: 42.6 (#120), Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | GPT-5.3 Chat | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1396 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference GPT-5.3 Chat leads
GPT-5.3 Chat: 63.1 (#68), Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | GPT-5.3 Chat | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1389 | 1419 |
| LMArena Creative Writing | 1355 | 1384 |
| EQ-Bench Creative Writing | 1690 | 1366 |
| LMArena Multi-Turn | 1412 | 1432 |
| Short-Story Creative Writing | — | 83% |
| WildBench | — | 86.6% |
Frequently asked questions
Is GPT-5.3 Chat better than Qwen3 235B-A22B?
GPT-5.3 Chat and Qwen3 235B-A22B score almost the same on the Noometry Index (42.8 vs 43.5), so choose on price, context window or the category you care about most.
Which is cheaper, GPT-5.3 Chat 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 Chat lists at $1.75 and $14.
Is GPT-5.3 Chat or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 41.4 in the Noometry coding category.
Which has the bigger context window?
Qwen3 235B-A22B does, with 131K tokens against 128K.
How many benchmarks do GPT-5.3 Chat and Qwen3 235B-A22B share?
18 benchmarks have published results for both models. GPT-5.3 Chat has 18 scored results on Noometry and Qwen3 235B-A22B has 49.