# GPT-5.6 Sol vs Qwen2.5-Coder-32B

> GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 32.9 on the Noometry Index. Qwen2.5-Coder-32B costs 11× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-6-sol-vs-qwen2-5-coder-32b
- Last updated: 2026-10-10
- Shared benchmarks: 13

## Summary

- They share 13 benchmarks with published results for both. GPT-5.6 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 math, where GPT-5.6 Sol leads 85.5 to 30.8.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 64.4 | 32.9 |
| Rank | 6 | 239 |
| Context | 1.05M | 33K |
| Input $/M | $4 | $0.66 |
| Output $/M | $20 | $1 |
| Weights | Proprietary | Open |

## Coding

- GPT-5.6 Sol: 61.1 (#7)
- Qwen2.5-Coder-32B: 22.4 (#328)

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1493 | 1276 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1619 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| WeirdML | 89.4% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| MirrorCode | 20% | — |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 2,177 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |

## Agentic & Tool Use

- GPT-5.6 Sol: 50.2 (#7)
- Qwen2.5-Coder-32B: —

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |

## Reasoning

- GPT-5.6 Sol: 74.7 (#6)
- Qwen2.5-Coder-32B: 20.4 (#220)

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1481 | 1251 |
| Epoch Capabilities Index | 161.99 | 119.41 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 32.3% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 58% | — |
| DTBench | 96% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |

## Math

- GPT-5.6 Sol: 85.5 (#7)
- Qwen2.5-Coder-32B: 30.8 (#203)

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1472 | 1251 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| LiveBench Math | — | 46.6% |
| FrontierMath Erdős | 0% | — |
| GSM8K | — | 93% |

## Knowledge

- GPT-5.6 Sol: 64.3 (#16)
- Qwen2.5-Coder-32B: 32.9 (#200)

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1517 | 1221 |
| GPQA Diamond | 93.5% | — |
| SimpleQA Verified | 69.7% | — |
| Vectara Hallucination Rate | 12.4% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |

## Multimodal

- GPT-5.6 Sol: 48.9 (#8)
- Qwen2.5-Coder-32B: —

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |

## Multilingual

- GPT-5.6 Sol: 55.4 (#32)
- Qwen2.5-Coder-32B: 38.0 (#230)

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1450 | 1205 |
| LMArena Chinese | 1523 | 1222 |
| LMArena Russian | 1466 | 1228 |
| LMArena French | 1475 | — |
| LMArena German | 1476 | — |
| LMArena Japanese | 1475 | — |
| LMArena Korean | 1434 | — |
| LMArena Spanish | 1449 | — |

## Instruction Following

- GPT-5.6 Sol: 77.8 (#16)
- Qwen2.5-Coder-32B: 61.6 (#240)

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1223 |
| LiveBench Instruction Following | — | 58.7% |

## Long Context

- GPT-5.6 Sol: 45.3 (#42)
- Qwen2.5-Coder-32B: 37.9 (#204)

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1479 | 1251 |

## Writing & Preference

- GPT-5.6 Sol: 73.5 (#12)
- Qwen2.5-Coder-32B: 41.8 (#235)

| Benchmark | GPT-5.6 Sol | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1456 | 1230 |
| LMArena Creative Writing | 1450 | 1174 |
| LMArena Multi-Turn | 1456 | 1222 |
| EQ-Bench Creative Writing | 1972 | — |
| EQ-Bench 4 | 1250 | — |
| LiveBench Language | — | 23.3% |

## FAQ

### Is GPT-5.6 Sol better than Qwen2.5-Coder-32B?

GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 32.9 on the Noometry Index. Qwen2.5-Coder-32B costs 11× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

### Which is cheaper, GPT-5.6 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-5.6 Sol lists at $4 and $20.

### Is GPT-5.6 Sol or Qwen2.5-Coder-32B better for coding?

GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 22.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.6 Sol does, with 1.05M tokens against 33K.

### How many benchmarks do GPT-5.6 Sol and Qwen2.5-Coder-32B share?

13 benchmarks have published results for both models. GPT-5.6 Sol has 64 scored results on Noometry and Qwen2.5-Coder-32B has 31.