Model comparison
GPT-5.6 Sol vs o1
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 40.9 on the Noometry Index.
Last verified . 30 shared benchmarks.
Summary
- They share 30 benchmarks with published results for both. GPT-5.6 Sol scores higher in 9 categories and o1 in 1 category; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.6 to 36.1.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 89.1% for GPT-5.6 Sol and 14.7% for o1.
- GPT-5.6 Sol is cheaper at $4 / $20 per million input/output tokens, against $15 / $60 for o1.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 200K.
Side by side
| GPT-5.6 Sol | o1 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 65.0 | 40.9 |
| Released | 2026-07-09 | 2024-09-12 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $4 | $15 |
| Output $ / M tokens | $20 | $60 |
| Results tracked | 65 | 52 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 65.1 (#7), o1: 46.1 (#70)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| WeirdML | 89.4% | 47.6% |
| LMArena Coding | 1498 | 1367 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| Aider Polyglot | — | 61.7% |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1618 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| GSO | 76.5% | — |
| LiveBench Coding | — | 69.7% |
| MirrorCode | 20% | — |
| CadEval | — | 56% |
| ALE-Bench | 2,177 | — |
| HumanEval+ | — | 89% |
| MBPP+ | — | 80.2% |
Agentic & Tool Use GPT-5.6 Sol leads
GPT-5.6 Sol: 50.3 (#7), o1: 24.6 (#117)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| Cybench | — | 10% |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| METR Time Horizons | — | 51.1% |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.8 (#8), o1: 27.9 (#111)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| SimpleBench | 71.7% | 41.7% |
| ARC-AGI-1 | 97.5% | 30.7% |
| Chess Puzzles | 64% | 15% |
| EnigmaEval | 37.1% | 5.7% |
| LMArena Hard Prompts | 1484 | 1371 |
| DTBench | 96% | 74.7% |
| LMCA | 59.2% | 22.3% |
| Epoch Capabilities Index | 161.66 | 141.91 |
| ARC-AGI-2 | 92.5% | — |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| CritPt | 32.3% | — |
| EBR-Bench | 44.8% | — |
| LiveBench Reasoning | — | 91.6% |
| Mystery Game Puzzles | 58% | — |
| LiveBench Data Analysis | — | 65.5% |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
| LiveBench | — | 75.7% |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.6 (#9), o1: 36.1 (#175)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 89.1% | 14.7% |
| OTIS Mock AIME 2024-2025 | 100% | 73.3% |
| LMArena Math | 1474 | 1388 |
| FrontierMath Tier 4 | 82.9% | — |
| ProofBench | 83% | — |
| LiveBench Math | — | 80.3% |
| MATH Level 5 | — | 94.7% |
| FrontierMath (Feb 2025 set) | — | 9.3% |
| FrontierMath Erdős | 0% | — |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#18), o1: 41.5 (#110)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| GPQA Diamond | 93.5% | 76.8% |
| SimpleQA Verified | 69.7% | 41.1% |
| LMArena Expert | 1516 | 1361 |
| Humanity's Last Exam | — | 8% |
| Confabulations | — | 11.7% |
| Vectara Hallucination Rate | 12.4% | — |
Multimodal GPT-5.6 Sol leads
GPT-5.6 Sol: 48.6 (#9), o1: 34.2 (#93)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| LMArena Vision | 1281 | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
| SpatialViz-Bench | — | 41.4% |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.3 (#32), o1: 48.6 (#142)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| LMArena Non-English | 1452 | 1358 |
| LMArena Chinese | 1527 | 1394 |
| LMArena French | 1477 | 1344 |
| LMArena German | 1476 | 1337 |
| LMArena Japanese | 1471 | 1346 |
| LMArena Korean | 1442 | 1396 |
| LMArena Russian | 1468 | 1356 |
| LMArena Spanish | 1441 | 1345 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.7 (#16), o1: 74.8 (#86)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| LMArena Instruction Following | 1482 | 1367 |
| LiveBench Instruction Following | — | 81.5% |
Long Context o1 leads
GPT-5.6 Sol: 45.4 (#42), o1: 50.3 (#9)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| LMArena Longer Query | 1480 | 1378 |
| Fiction.LiveBench | — | 83.3% |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.3 (#12), o1: 55.6 (#144)
| Benchmark | GPT-5.6 Sol | o1 |
|---|---|---|
| LMArena Text | 1457 | 1366 |
| LMArena Creative Writing | 1448 | 1348 |
| LMArena Multi-Turn | 1460 | 1369 |
| Short-Story Creative Writing | — | 70.2% |
| EQ-Bench Creative Writing | 1972 | — |
| EQ-Bench 4 | 1250 | — |
| LiveBench Language | — | 65.4% |
Frequently asked questions
Is GPT-5.6 Sol better than o1?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 40.9 on the Noometry Index.
Which is cheaper, GPT-5.6 Sol or o1?
GPT-5.6 Sol is cheaper. It lists at $4 per million input tokens and $20 per million output tokens; o1 lists at $15 and $60.
Is GPT-5.6 Sol or o1 better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 46.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 200K.
How many benchmarks do GPT-5.6 Sol and o1 share?
30 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and o1 has 52.