Model comparison
GPT-5.6 Sol vs o3
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 47.5 on the Noometry Index. o3 costs 2.3× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 38 shared benchmarks.
Summary
- They share 38 benchmarks with published results for both. GPT-5.6 Sol scores higher in 9 categories and o3 in 1 category; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.8 to 32.0.
- The biggest single-benchmark swing is ARC-AGI-2: 92.5% for GPT-5.6 Sol and 6.5% for o3.
- o3 is cheaper at $2 / $8 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 200K.
Side by side
| GPT-5.6 Sol | o3 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 65.0 | 47.5 |
| Released | 2026-07-09 | 2025-04-16 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $4 | $2 |
| Output $ / M tokens | $20 | $8 |
| Results tracked | 65 | 63 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 65.1 (#7), o3: 46.8 (#64)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| GSO | 76.5% | 8.8% |
| WeirdML | 89.4% | 52.4% |
| LMArena Coding | 1498 | 1408 |
| ALE-Bench | 2,177 | 933.55 |
| SWE-bench Verified | — | 62.3% |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1618 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| MirrorCode | 20% | — |
| CadEval | — | 74% |
Agentic & Tool Use GPT-5.6 Sol leads
GPT-5.6 Sol: 50.3 (#7), o3: 34.5 (#44)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| LMArena Search | 1257 | 1144 |
| APEX-Agents | 51.4% | — |
| Berkeley Function Calling Leaderboard | — | 63% |
| OSWorld 2.0 | 27.3% | — |
| GDPval | — | 30.8% |
| τ²-bench Banking | 46.9% | — |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| METR Time Horizons | — | 65.4% |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.8 (#8), o3: 32.0 (#78)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| ARC-AGI-2 | 92.5% | 6.5% |
| SimpleBench | 71.7% | 53.1% |
| Kagi LLM Benchmark | 67% | 67.6% |
| ARC-AGI-1 | 97.5% | 60.8% |
| CritPt | 32.3% | 1.4% |
| Chess Puzzles | 64% | 38% |
| EnigmaEval | 37.1% | 13.1% |
| LMArena Hard Prompts | 1484 | 1402 |
| Mystery Game Puzzles | 58% | 29% |
| DTBench | 96% | 84.8% |
| LMCA | 59.2% | 39.7% |
| Epoch Capabilities Index | 161.66 | 146.86 |
| NYT Connections (extended) | 93.8% | — |
| EBR-Bench | 44.8% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | — | 62.5 |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.6 (#9), o3: 50.2 (#58)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 89.1% | 33.3% |
| OTIS Mock AIME 2024-2025 | 100% | 84.4% |
| LMArena Math | 1474 | 1426 |
| FrontierMath Tier 4 | 82.9% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 71.4% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#18), o3: 54.6 (#52)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| GPQA Diamond | 93.5% | 81.8% |
| SimpleQA Verified | 69.7% | 49.4% |
| LMArena Expert | 1516 | 1402 |
| Humanity's Last Exam | — | 20.3% |
| MMLU-Pro | — | 85.9% |
| Confabulations | — | 14.4% |
| Vectara Hallucination Rate | 12.4% | — |
| GPQA (HELM) | — | 75.3% |
Multimodal GPT-5.6 Sol leads
GPT-5.6 Sol: 48.6 (#9), o3: 41.4 (#36)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| LMArena Vision | 1281 | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.3 (#32), o3: 51.7 (#105)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| LMArena Non-English | 1452 | 1401 |
| LMArena Chinese | 1527 | 1437 |
| LMArena French | 1477 | 1430 |
| LMArena German | 1476 | 1420 |
| LMArena Japanese | 1471 | 1403 |
| LMArena Korean | 1442 | 1370 |
| LMArena Russian | 1468 | 1406 |
| LMArena Spanish | 1441 | 1395 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.7 (#16), o3: 72.8 (#127)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| LMArena Instruction Following | 1482 | 1368 |
| IFEval | — | 86.9% |
Long Context o3 leads
GPT-5.6 Sol: 45.4 (#42), o3: 53.3 (#6)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| LMArena Longer Query | 1480 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.3 (#12), o3: 63.5 (#64)
| Benchmark | GPT-5.6 Sol | o3 |
|---|---|---|
| LMArena Text | 1457 | 1410 |
| LMArena Creative Writing | 1448 | 1359 |
| EQ-Bench Creative Writing | 1972 | 1676 |
| LMArena Multi-Turn | 1460 | 1405 |
| Short-Story Creative Writing | — | 83.9% |
| WildBench | — | 86.1% |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than o3?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 47.5 on the Noometry Index. o3 costs 2.3× 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 o3?
o3 is cheaper. It lists at $2 per million input tokens and $8 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or o3 better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 46.8 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 o3 share?
38 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and o3 has 63.