Model comparison
GPT-5.6 Sol vs Qwen2-72B
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 29.5 on the Noometry Index.
Last verified . 20 shared benchmarks.
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. GPT-5.6 Sol scores higher in 9 categories and Qwen2-72B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.5 to 27.9.
- The biggest single-benchmark swing is WeirdML: 89.4% for GPT-5.6 Sol and 11.3% for Qwen2-72B.
- Qwen2-72B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Sol | Qwen2-72B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 64.4 | 29.5 |
| Released | 2026-07-09 | 2024-06-07 |
| Weights | Proprietary | Open |
| Context window | 1.05M | — |
| Max output | 128K | — |
| Input $ / M tokens | $4 | — |
| Output $ / M tokens | $20 | — |
| Results tracked | 64 | 26 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 61.1 (#7), Qwen2-72B: 28.7 (#303)
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| WeirdML | 89.4% | 11.3% |
| LMArena Coding | 1493 | 1196 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1619 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| BigCodeBench Instruct | — | 38.5% |
| MirrorCode | 20% | — |
| BigCodeBench Complete | — | 54% |
| ALE-Bench | 2,177 | — |
Agentic & Tool Use GPT-5.6 Sol leads
GPT-5.6 Sol: 50.2 (#7), Qwen2-72B: 16.9 (#138)
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| TheAgentCompany | — | 1.1% |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| METR Time Horizons | — | 29.9% |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.7 (#6), Qwen2-72B: 22.2 (#184)
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| LMArena Hard Prompts | 1481 | 1191 |
| Epoch Capabilities Index | 161.99 | 125.27 |
| 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% | — |
| Mystery Game Puzzles | 58% | — |
| DTBench | 96% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.5 (#7), Qwen2-72B: 27.9 (#232)
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| LMArena Math | 1472 | 1235 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| MATH Level 5 | — | 39.1% |
| FrontierMath Erdős | 0% | — |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#16), Qwen2-72B: 21.2 (#270)
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| GPQA Diamond | 93.5% | 40.8% |
| LMArena Expert | 1517 | 1171 |
| SimpleQA Verified | 69.7% | — |
| Vectara Hallucination Rate | 12.4% | — |
| MMLU | — | 82.4% |
Multimodal Not comparable
GPT-5.6 Sol: 48.9 (#8), Qwen2-72B: —
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.4 (#32), Qwen2-72B: 36.0 (#239)
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| LMArena Non-English | 1450 | 1176 |
| LMArena Chinese | 1523 | 1240 |
| LMArena French | 1475 | 1169 |
| LMArena German | 1476 | 1151 |
| LMArena Japanese | 1475 | 1111 |
| LMArena Korean | 1434 | 1083 |
| LMArena Russian | 1466 | 1169 |
| LMArena Spanish | 1449 | 1168 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.8 (#16), Qwen2-72B: 62.0 (#236)
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1181 |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.3 (#42), Qwen2-72B: 36.1 (#230)
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| LMArena Longer Query | 1479 | 1193 |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.5 (#12), Qwen2-72B: 41.0 (#236)
| Benchmark | GPT-5.6 Sol | Qwen2-72B |
|---|---|---|
| LMArena Text | 1456 | 1203 |
| LMArena Creative Writing | 1450 | 1181 |
| LMArena Multi-Turn | 1456 | 1195 |
| EQ-Bench Creative Writing | 1972 | — |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than Qwen2-72B?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 29.5 on the Noometry Index.
Is GPT-5.6 Sol or Qwen2-72B better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 28.7 in the Noometry coding category.
How many benchmarks do GPT-5.6 Sol and Qwen2-72B share?
20 benchmarks have published results for both models. GPT-5.6 Sol has 64 scored results on Noometry and Qwen2-72B has 26.