Model comparison
GPT-5.1 vs Qwen2.5-Coder-32B
GPT-5.1 is the stronger model overall, scoring 49.0 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 4.6× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. GPT-5.1 scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GPT-5.1 leads 46.4 to 22.6.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 66% for GPT-5.1 and 9% for Qwen2.5-Coder-32B.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $1.25 / $10 for GPT-5.1.
- GPT-5.1 accepts more context: 400K tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1 | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 49.0 | 33.4 |
| Released | 2025-11-13 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | 400K | 33K |
| Max output | 128K | 29K |
| Input $ / M tokens | $1.25 | $0.66 |
| Output $ / M tokens | $10 | $1 |
| Results tracked | 63 | 31 |
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Category by category
Coding GPT-5.1 leads
GPT-5.1: 46.4 (#66), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| SWE-bench Verified (bash only) | 66% | 9% |
| LiveBench Coding | 72.5% | 56.9% |
| LMArena Coding | 1454 | 1276 |
| SWE-bench Verified | 68% | — |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1395 | — |
| SciCode | 43.3% | — |
| GSO | 13.7% | — |
| WeirdML | 60.8% | — |
| BigCodeBench Instruct | — | 49% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 1,192 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
GPT-5.1: 32.7 (#60), Qwen2.5-Coder-32B: —
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| DeepResearch Bench | 42.8% | — |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |
Reasoning GPT-5.1 leads
GPT-5.1: 39.8 (#58), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| LiveBench Reasoning | 95.8% | 42.1% |
| LMArena Hard Prompts | 1457 | 1251 |
| LiveBench Data Analysis | 72.1% | 49.9% |
| Epoch Capabilities Index | 149.64 | 119.49 |
| LiveBench | 78.8% | 46.2% |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| ARC-AGI-1 | 72.8% | — |
| CritPt | 4.9% | — |
| Chess Puzzles | 32% | — |
| EnigmaEval | 11.2% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 90.1% | — |
| LMCA | 43.9% | — |
| ForecastBench | 58.1 | — |
| HellaSwag | — | 83% |
| WinoGrande | — | 80.8% |
Math GPT-5.1 leads
GPT-5.1: 52.2 (#51), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| LiveBench Math | 94.5% | 46.6% |
| LMArena Math | 1447 | 1251 |
| OTIS Mock AIME 2024-2025 | 88.6% | — |
| Omni-MATH | 46.4% | — |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
| GSM8K | — | 93% |
Knowledge GPT-5.1 leads
GPT-5.1: 50.6 (#71), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1470 | 1221 |
| GPQA Diamond | 87.6% | — |
| Humanity's Last Exam | 23.7% | — |
| SimpleQA Verified | 48% | — |
| MMLU-Pro | 57.9% | — |
| Vectara Hallucination Rate | 10.9% | — |
| GPQA (HELM) | 44.2% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
GPT-5.1: 44.8 (#19), Qwen2.5-Coder-32B: —
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual GPT-5.1 leads
GPT-5.1: 53.8 (#56), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1431 | 1205 |
| LMArena Chinese | 1495 | 1222 |
| LMArena Russian | 1435 | 1228 |
| LMArena French | 1450 | — |
| LMArena German | 1438 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1401 | — |
| LMArena Spanish | 1433 | — |
Instruction Following GPT-5.1 leads
GPT-5.1: 83.9 (#1), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| LiveBench Instruction Following | 93.3% | 58.7% |
| LMArena Instruction Following | 1443 | 1223 |
| IFEval | 93.5% | — |
Long Context GPT-5.1 leads
GPT-5.1: 47.6 (#14), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1447 | 1251 |
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
Writing & Preference GPT-5.1 leads
GPT-5.1: 64.5 (#55), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | GPT-5.1 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1443 | 1230 |
| LMArena Creative Writing | 1427 | 1174 |
| LMArena Multi-Turn | 1450 | 1222 |
| LiveBench Language | 80.2% | 23.3% |
| WildBench | 86.3% | — |
Frequently asked questions
Is GPT-5.1 better than Qwen2.5-Coder-32B?
GPT-5.1 is the stronger model overall, scoring 49.0 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 4.6× 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 Qwen2.5-Coder-32B?
Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; GPT-5.1 lists at $1.25 and $10.
Is GPT-5.1 or Qwen2.5-Coder-32B better for coding?
GPT-5.1 scores higher on coding benchmarks: 46.4 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.1 does, with 400K tokens against 33K.
How many benchmarks do GPT-5.1 and Qwen2.5-Coder-32B share?
21 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Qwen2.5-Coder-32B has 31.