Model comparison
GPT-5.6 Sol vs o3-mini
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 36.7 on the Noometry Index. o3-mini costs 4.2× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
Summary
- They share 34 benchmarks with published results for both. GPT-5.6 Sol scores higher in 9 categories and o3-mini in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.8 to 16.3.
- The biggest single-benchmark swing is ARC-AGI-2: 92.5% for GPT-5.6 Sol and 3% for o3-mini.
- o3-mini is cheaper at $1.10 / $4.40 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-mini | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 65.0 | 36.7 |
| Released | 2026-07-09 | 2024-12-20 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $4 | $1.10 |
| Output $ / M tokens | $20 | $4.40 |
| Results tracked | 65 | 51 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 65.1 (#7), o3-mini: 40.8 (#132)
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| SciCode | 57.1% | 39.8% |
| GSO | 76.5% | 1.3% |
| WeirdML | 89.4% | 43.7% |
| LMArena Coding | 1498 | 1378 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| Aider Polyglot | — | 60.4% |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1618 | — |
| FrontierSWE | 32.2% | — |
| LiveBench Coding | — | 82.7% |
| MirrorCode | 20% | — |
| CadEval | — | 54% |
| ALE-Bench | 2,177 | — |
Agentic & Tool Use GPT-5.6 Sol leads
GPT-5.6 Sol: 50.3 (#7), o3-mini: 29.6 (#84)
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| Cybench | — | 22.5% |
| 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 leads
GPT-5.6 Sol: 74.8 (#8), o3-mini: 16.3 (#305)
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| ARC-AGI-2 | 92.5% | 3% |
| SimpleBench | 71.7% | 22.8% |
| ARC-AGI-1 | 97.5% | 34.5% |
| CritPt | 32.3% | 0.3% |
| Chess Puzzles | 64% | 17% |
| LMArena Hard Prompts | 1484 | 1366 |
| Mystery Game Puzzles | 58% | 7% |
| DTBench | 96% | 68.8% |
| LMCA | 59.2% | 19% |
| Epoch Capabilities Index | 161.66 | 140.34 |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| LiveBench Reasoning | — | 89.6% |
| LiveBench Data Analysis | — | 70.6% |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.6 (#9), o3-mini: 28.1 (#244)
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 89.1% | 18.6% |
| FrontierMath Tier 4 | 82.9% | 0% |
| OTIS Mock AIME 2024-2025 | 100% | 76.9% |
| LMArena Math | 1474 | 1396 |
| ProofBench | 83% | — |
| LiveBench Math | — | 77.3% |
| MATH Level 5 | — | 96.5% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#18), o3-mini: 38.3 (#146)
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| GPQA Diamond | 93.5% | 77% |
| SimpleQA Verified | 69.7% | 15.3% |
| LMArena Expert | 1516 | 1364 |
| Confabulations | — | 17.9% |
| Vectara Hallucination Rate | 12.4% | — |
Multimodal Not comparable
GPT-5.6 Sol: 48.6 (#9), o3-mini: —
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| 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: 55.3 (#32), o3-mini: 45.7 (#164)
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| LMArena Non-English | 1452 | 1319 |
| LMArena Chinese | 1527 | 1379 |
| LMArena French | 1477 | 1334 |
| LMArena German | 1476 | 1303 |
| LMArena Japanese | 1471 | 1286 |
| LMArena Korean | 1442 | 1314 |
| LMArena Russian | 1468 | 1304 |
| LMArena Spanish | 1441 | 1321 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.7 (#16), o3-mini: 75.1 (#72)
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1482 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.4 (#42), o3-mini: 33.8 (#256)
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| LMArena Longer Query | 1480 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.3 (#12), o3-mini: 50.3 (#182)
| Benchmark | GPT-5.6 Sol | o3-mini |
|---|---|---|
| LMArena Text | 1457 | 1337 |
| LMArena Creative Writing | 1448 | 1286 |
| LMArena Multi-Turn | 1460 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| EQ-Bench Creative Writing | 1972 | — |
| EQ-Bench 4 | 1250 | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is GPT-5.6 Sol better than o3-mini?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 36.7 on the Noometry Index. o3-mini costs 4.2× 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-mini?
o3-mini is cheaper. It lists at $1.10 per million input tokens and $4.40 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or o3-mini better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 40.8 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-mini share?
34 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and o3-mini has 51.