Model comparison
GPT-5.4 Pro vs Qwen3 235B-A22B
GPT-5.4 Pro is the stronger model overall, scoring 58.9 to 43.5 on the Noometry Index. Qwen3 235B-A22B costs 55× less per token, which makes it the better buy when GPT-5.4 Pro's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
Alibaba (Qwen)Summary
- They share 12 benchmarks with published results for both. GPT-5.4 Pro scores higher in 3 categories and Qwen3 235B-A22B in 1 category; 4 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.4 Pro leads 70.7 to 15.7.
- The biggest single-benchmark swing is ARC-AGI-1: 94.5% for GPT-5.4 Pro and 11% for Qwen3 235B-A22B.
- Qwen3 235B-A22B is cheaper at $0.70 / $2.80 per million input/output tokens, against $30 / $180 for GPT-5.4 Pro.
- GPT-5.4 Pro accepts more context: 1.05M tokens versus 131K.
- Qwen3 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 Pro | Qwen3 235B-A22B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 58.9 | 43.5 |
| Released | 2026-03-05 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 16K |
| Input $ / M tokens | $30 | $0.70 |
| Output $ / M tokens | $180 | $2.80 |
| Results tracked | 16 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
GPT-5.4 Pro: 43.2 (#90), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | GPT-5.4 Pro | Qwen3 235B-A22B |
|---|---|---|
| WeirdML | 57.4% | 41% |
| Aider Polyglot | — | 59.6% |
| SciCode | — | 42.4% |
| LMArena Coding | — | 1445 |
Agentic & Tool Use Not comparable
GPT-5.4 Pro: —, Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | GPT-5.4 Pro | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning GPT-5.4 Pro leads
GPT-5.4 Pro: 70.7 (#13), Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | GPT-5.4 Pro | Qwen3 235B-A22B |
|---|---|---|
| ARC-AGI-2 | 83.3% | 1.3% |
| SimpleBench | 74.1% | 31% |
| ARC-AGI-1 | 94.5% | 11% |
| CritPt | 30% | 0% |
| Chess Puzzles | 58.6% | 12% |
| Epoch Capabilities Index | 158.93 | 143.85 |
| Kagi LLM Benchmark | — | 69.4% |
| EnigmaEval | 23.8% | — |
| LMArena Hard Prompts | — | 1433 |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 29.3% |
| ForecastBench | — | 59.7 |
Math GPT-5.4 Pro leads
GPT-5.4 Pro: 72.4 (#22), Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | GPT-5.4 Pro | Qwen3 235B-A22B |
|---|---|---|
| FrontierMath (Feb 2025 set) | 50% | 8.5% |
| FrontierMath Tier 4 (v1) | 37.5% | 0% |
| FrontierMath (Tiers 1-3) | 82.5% | — |
| FrontierMath Tier 4 | 58.5% | — |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| Omni-MATH | — | 71.8% |
| LMArena Math | — | 1432 |
| MATH Level 5 | — | 68.9% |
Knowledge GPT-5.4 Pro leads
GPT-5.4 Pro: 68.3 (#7), Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | GPT-5.4 Pro | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | 94.6% | 80.1% |
| SimpleQA Verified | 46.3% | 40.4% |
| Vectara Hallucination Rate | 8.3% | 9.3% |
| Humanity's Last Exam | 44.3% | — |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| GPQA (HELM) | — | 72.7% |
| LMArena Expert | — | 1463 |
Multilingual Not comparable
GPT-5.4 Pro: —, Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | GPT-5.4 Pro | 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-5.4 Pro: —, Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | GPT-5.4 Pro | Qwen3 235B-A22B |
|---|---|---|
| IFEval | — | 83.5% |
| LMArena Instruction Following | — | 1408 |
Long Context Not comparable
GPT-5.4 Pro: —, Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | GPT-5.4 Pro | Qwen3 235B-A22B |
|---|---|---|
| Fiction.LiveBench | — | 75% |
| LMArena Longer Query | — | 1426 |
Writing & Preference Not comparable
GPT-5.4 Pro: —, Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | GPT-5.4 Pro | 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-5.4 Pro better than Qwen3 235B-A22B?
GPT-5.4 Pro is the stronger model overall, scoring 58.9 to 43.5 on the Noometry Index. Qwen3 235B-A22B costs 55× less per token, which makes it the better buy when GPT-5.4 Pro's lead doesn't matter for your workload.
Which is cheaper, GPT-5.4 Pro 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-5.4 Pro lists at $30 and $180.
Is GPT-5.4 Pro or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 43.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 Pro does, with 1.05M tokens against 131K.
How many benchmarks do GPT-5.4 Pro and Qwen3 235B-A22B share?
12 benchmarks have published results for both models. GPT-5.4 Pro has 16 scored results on Noometry and Qwen3 235B-A22B has 49.