Model comparison
GPT-5.1 vs Qwen3 32B
GPT-5.1 is the stronger model overall, scoring 49.0 to 39.2 on the Noometry Index. Qwen3 32B costs 2.8× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Alibaba (Qwen)Summary
- They share 22 benchmarks with published results for both. GPT-5.1 scores higher in 9 categories and Qwen3 32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.1 leads 39.8 to 20.2.
- The biggest single-benchmark swing is Chess Puzzles: 32% for GPT-5.1 and 5% for Qwen3 32B.
- Qwen3 32B is cheaper at $0.70 / $2.80 per million input/output tokens, against $1.25 / $10 for GPT-5.1.
- GPT-5.1 accepts more context: 400K tokens versus 131K.
- Qwen3 32B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1 | Qwen3 32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 49.0 | 39.2 |
| Released | 2025-11-13 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 400K | 131K |
| Max output | 128K | 16K |
| Input $ / M tokens | $1.25 | $0.70 |
| Output $ / M tokens | $10 | $2.80 |
| Results tracked | 63 | 26 |
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Category by category
Coding GPT-5.1 leads
GPT-5.1: 46.4 (#66), Qwen3 32B: 37.7 (#190)
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| SciCode | 43.3% | 35.4% |
| LMArena Coding | 1454 | 1358 |
| SWE-bench Verified | 68% | — |
| SWE-bench Verified (bash only) | 66% | — |
| Aider Polyglot | — | 40% |
| LMArena WebDev | 1395 | — |
| GSO | 13.7% | — |
| WeirdML | 60.8% | — |
| LiveBench Coding | 72.5% | — |
| ALE-Bench | 1,192 | — |
Agentic & Tool Use Too close to call
GPT-5.1: 32.7 (#60), Qwen3 32B: 32.6 (#62)
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| Berkeley Function Calling Leaderboard | — | 48.7% |
| DeepResearch Bench | 42.8% | — |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |
Reasoning GPT-5.1 leads
GPT-5.1: 39.8 (#58), Qwen3 32B: 20.2 (#241)
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| CritPt | 4.9% | 0.3% |
| Chess Puzzles | 32% | 5% |
| LMArena Hard Prompts | 1457 | 1334 |
| DTBench | 90.1% | 67.5% |
| LMCA | 43.9% | 17.3% |
| Epoch Capabilities Index | 149.64 | 138.51 |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | — | 54.9% |
| ARC-AGI-1 | 72.8% | — |
| EnigmaEval | 11.2% | — |
| LiveBench Reasoning | 95.8% | — |
| Mystery Game Puzzles | 19% | — |
| LiveBench Data Analysis | 72.1% | — |
| ForecastBench | 58.1 | — |
| LiveBench | 78.8% | — |
Math GPT-5.1 leads
GPT-5.1: 52.2 (#51), Qwen3 32B: 39.7 (#99)
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.6% | 66.9% |
| LMArena Math | 1447 | 1399 |
| Omni-MATH | 46.4% | — |
| LiveBench Math | 94.5% | — |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GPT-5.1 leads
GPT-5.1: 50.6 (#71), Qwen3 32B: 40.0 (#125)
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| GPQA Diamond | 87.6% | 65.7% |
| Vectara Hallucination Rate | 10.9% | 5.9% |
| LMArena Expert | 1470 | 1362 |
| Humanity's Last Exam | 23.7% | — |
| SimpleQA Verified | 48% | — |
| MMLU-Pro | 57.9% | — |
| GPQA (HELM) | 44.2% | — |
Multimodal Not comparable
GPT-5.1: 44.8 (#19), Qwen3 32B: —
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual GPT-5.1 leads
GPT-5.1: 53.8 (#56), Qwen3 32B: 45.6 (#167)
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1431 | 1317 |
| LMArena Chinese | 1495 | 1357 |
| LMArena German | 1438 | 1341 |
| LMArena Russian | 1435 | 1311 |
| LMArena French | 1450 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1401 | — |
| LMArena Spanish | 1433 | — |
Instruction Following GPT-5.1 leads
GPT-5.1: 83.9 (#1), Qwen3 32B: 68.9 (#179)
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1443 | 1305 |
| LiveBench Instruction Following | 93.3% | — |
| IFEval | 93.5% | — |
Long Context GPT-5.1 leads
GPT-5.1: 47.6 (#14), Qwen3 32B: 43.8 (#87)
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| LMArena Longer Query | 1447 | 1327 |
| Fiction.LiveBench | — | 74.2% |
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
Writing & Preference GPT-5.1 leads
GPT-5.1: 64.5 (#55), Qwen3 32B: 52.9 (#163)
| Benchmark | GPT-5.1 | Qwen3 32B |
|---|---|---|
| LMArena Text | 1443 | 1340 |
| LMArena Creative Writing | 1427 | 1297 |
| LMArena Multi-Turn | 1450 | 1331 |
| WildBench | 86.3% | — |
| LiveBench Language | 80.2% | — |
Frequently asked questions
Is GPT-5.1 better than Qwen3 32B?
GPT-5.1 is the stronger model overall, scoring 49.0 to 39.2 on the Noometry Index. Qwen3 32B costs 2.8× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Which is cheaper, GPT-5.1 or Qwen3 32B?
Qwen3 32B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GPT-5.1 lists at $1.25 and $10.
Is GPT-5.1 or Qwen3 32B better for coding?
GPT-5.1 scores higher on coding benchmarks: 46.4 versus 37.7 in the Noometry coding category.
Which has the bigger context window?
GPT-5.1 does, with 400K tokens against 131K.
How many benchmarks do GPT-5.1 and Qwen3 32B share?
22 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Qwen3 32B has 26.