Model comparison
o1 vs o3
o3 is the stronger model overall, scoring 47.5 to 40.9 on the Noometry Index.
Last verified . 41 shared benchmarks.
Summary
- They share 41 benchmarks with published results for both. o1 scores higher in 1 category and o3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where o3 leads 50.2 to 36.1.
- The biggest single-benchmark swing is ARC-AGI-1: 30.7% for o1 and 60.8% for o3.
- o3 is cheaper at $2 / $8 per million input/output tokens, against $15 / $60 for o1.
Side by side
| o1 | o3 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 40.9 | 47.5 |
| Released | 2024-09-12 | 2025-04-16 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 200K |
| Max output | 100K | 100K |
| Input $ / M tokens | $15 | $2 |
| Output $ / M tokens | $60 | $8 |
| Results tracked | 52 | 63 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Too close to call
o1: 46.1 (#70), o3: 46.8 (#64)
| Benchmark | o1 | o3 |
|---|---|---|
| Aider Polyglot | 61.7% | 81.3% |
| WeirdML | 47.6% | 52.4% |
| LMArena Coding | 1367 | 1408 |
| CadEval | 56% | 74% |
| SWE-bench Verified | — | 62.3% |
| SWE-bench Verified (bash only) | — | 58.4% |
| GSO | — | 8.8% |
| LiveBench Coding | 69.7% | — |
| ALE-Bench | — | 933.55 |
| HumanEval+ | 89% | — |
| MBPP+ | 80.2% | — |
Agentic & Tool Use o3 leads
o1: 24.6 (#117), o3: 34.5 (#44)
| Benchmark | o1 | o3 |
|---|---|---|
| METR Time Horizons | 51.1% | 65.4% |
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| Cybench | 10% | — |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| LMArena Search | — | 1144 |
Reasoning o3 leads
o1: 27.9 (#111), o3: 32.0 (#78)
| Benchmark | o1 | o3 |
|---|---|---|
| SimpleBench | 41.7% | 53.1% |
| ARC-AGI-1 | 30.7% | 60.8% |
| Chess Puzzles | 15% | 38% |
| EnigmaEval | 5.7% | 13.1% |
| LMArena Hard Prompts | 1371 | 1402 |
| DTBench | 74.7% | 84.8% |
| LMCA | 22.3% | 39.7% |
| Epoch Capabilities Index | 141.91 | 146.86 |
| ARC-AGI-2 | — | 6.5% |
| Kagi LLM Benchmark | — | 67.6% |
| CritPt | — | 1.4% |
| LiveBench Reasoning | 91.6% | — |
| Mystery Game Puzzles | — | 29% |
| LiveBench Data Analysis | 65.5% | — |
| ForecastBench | — | 62.5 |
| LiveBench | 75.7% | — |
Math o3 leads
o1: 36.1 (#175), o3: 50.2 (#58)
| Benchmark | o1 | o3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 14.7% | 33.3% |
| OTIS Mock AIME 2024-2025 | 73.3% | 84.4% |
| LMArena Math | 1388 | 1426 |
| MATH Level 5 | 94.7% | 97.8% |
| FrontierMath (Feb 2025 set) | 9.3% | 18.7% |
| Omni-MATH | — | 71.4% |
| LiveBench Math | 80.3% | — |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge o3 leads
o1: 41.5 (#110), o3: 54.6 (#52)
| Benchmark | o1 | o3 |
|---|---|---|
| GPQA Diamond | 76.8% | 81.8% |
| Humanity's Last Exam | 8% | 20.3% |
| SimpleQA Verified | 41.1% | 49.4% |
| Confabulations | 11.7% | 14.4% |
| LMArena Expert | 1361 | 1402 |
| MMLU-Pro | — | 85.9% |
| GPQA (HELM) | — | 75.3% |
Multimodal o3 leads
o1: 34.2 (#93), o3: 41.4 (#36)
| Benchmark | o1 | o3 |
|---|---|---|
| LMArena Vision | 1168 | 1214 |
| GeoBench | 80% | 74% |
| VPCT | 37% | 52% |
| SpatialViz-Bench | 41.4% | — |
Multilingual o3 leads
o1: 48.6 (#142), o3: 51.7 (#105)
| Benchmark | o1 | o3 |
|---|---|---|
| LMArena Non-English | 1358 | 1401 |
| LMArena Chinese | 1394 | 1437 |
| LMArena French | 1344 | 1430 |
| LMArena German | 1337 | 1420 |
| LMArena Japanese | 1346 | 1403 |
| LMArena Korean | 1396 | 1370 |
| LMArena Russian | 1356 | 1406 |
| LMArena Spanish | 1345 | 1395 |
Instruction Following o1 leads
o1: 74.8 (#86), o3: 72.8 (#127)
| Benchmark | o1 | o3 |
|---|---|---|
| LMArena Instruction Following | 1367 | 1368 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | — | 86.9% |
Long Context o3 leads
o1: 50.3 (#9), o3: 53.3 (#6)
| Benchmark | o1 | o3 |
|---|---|---|
| Fiction.LiveBench | 83.3% | 88.9% |
| LMArena Longer Query | 1378 | 1372 |
| CL-bench | — | 17.8% |
Writing & Preference o3 leads
o1: 55.6 (#144), o3: 63.5 (#64)
| Benchmark | o1 | o3 |
|---|---|---|
| LMArena Text | 1366 | 1410 |
| LMArena Creative Writing | 1348 | 1359 |
| Short-Story Creative Writing | 70.2% | 83.9% |
| LMArena Multi-Turn | 1369 | 1405 |
| EQ-Bench Creative Writing | — | 1676 |
| WildBench | — | 86.1% |
| LiveBench Language | 65.4% | — |
Frequently asked questions
Is o1 better than o3?
o3 is the stronger model overall, scoring 47.5 to 40.9 on the Noometry Index.
Which is cheaper, o1 or o3?
o3 is cheaper. It lists at $2 per million input tokens and $8 per million output tokens; o1 lists at $15 and $60.
Is o1 or o3 better for coding?
They score almost the same on coding (46.1 vs 46.8); test both on your own repository before choosing.
Which has the bigger context window?
Both accept 200K tokens.
How many benchmarks do o1 and o3 share?
41 benchmarks have published results for both models. o1 has 52 scored results on Noometry and o3 has 63.