Model comparison
GPT-6.1 Sol vs Qwen2.5-Coder-32B
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 5.4× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. GPT-6.1 Sol 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 reasoning, where GPT-6.1 Sol leads 81.9 to 21.2.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.
Side by side
| GPT-6.1 Sol | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 65.6 | 33.4 |
| Released | 2026-09-29 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 33K |
| Max output | 128K | 29K |
| Input $ / M tokens | $2 | $0.66 |
| Output $ / M tokens | $10 | $1 |
| Results tracked | 34 | 31 |
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Category by category
Coding GPT-6.1 Sol leads
GPT-6.1 Sol: 63.2 (#8), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1487 | 1276 |
| DeepSWE | 75.2% | — |
| FrontierCode | 50.2% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1755 | — |
| SciCode | 55.8% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
GPT-6.1 Sol: 39.6 (#26), Qwen2.5-Coder-32B: —
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| APEX-Agents | 60% | — |
| GDP.pdf | 32% | — |
Reasoning GPT-6.1 Sol leads
GPT-6.1 Sol: 81.9 (#2), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1466 | 1251 |
| Epoch Capabilities Index | 166.09 | 119.49 |
| ARC-AGI-2 | 94.2% | — |
| NYT Connections (extended) | 95.5% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| Chess Puzzles | 61% | — |
| EBR-Bench | 54.3% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 80% | — |
| LiveBench Data Analysis | — | 49.9% |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math GPT-6.1 Sol leads
GPT-6.1 Sol: 93.7 (#1), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1464 | 1251 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 100% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 99% | — |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |
Knowledge GPT-6.1 Sol leads
GPT-6.1 Sol: 71.8 (#4), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1502 | 1221 |
| GPQA Diamond | 95.4% | — |
| SimpleQA Verified | 73.9% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
GPT-6.1 Sol: 52.7 (#5), Qwen2.5-Coder-32B: —
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1288 | — |
| Furniture Assembly | 80% | — |
Multilingual GPT-6.1 Sol leads
GPT-6.1 Sol: 54.3 (#46), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1438 | 1205 |
| LMArena Chinese | 1477 | 1222 |
| LMArena Russian | 1455 | 1228 |
Instruction Following GPT-6.1 Sol leads
GPT-6.1 Sol: 77.0 (#29), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1468 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
Long Context GPT-6.1 Sol leads
GPT-6.1 Sol: 44.9 (#54), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1465 | 1251 |
Writing & Preference GPT-6.1 Sol leads
GPT-6.1 Sol: 63.6 (#63), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | GPT-6.1 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1447 | 1230 |
| LMArena Creative Writing | 1432 | 1174 |
| LMArena Multi-Turn | 1449 | 1222 |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is GPT-6.1 Sol better than Qwen2.5-Coder-32B?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 5.4× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6.1 Sol 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-6.1 Sol lists at $2 and $10.
Is GPT-6.1 Sol or Qwen2.5-Coder-32B better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 33K.
How many benchmarks do GPT-6.1 Sol and Qwen2.5-Coder-32B share?
13 benchmarks have published results for both models. GPT-6.1 Sol has 34 scored results on Noometry and Qwen2.5-Coder-32B has 31.