Model comparison
o1-pro vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 to 31.5 on the Noometry Index.
Last verified . 3 shared benchmarks.
Summary
- They share 3 benchmarks with published results for both. o1-pro scores higher in 0 categories and o4-mini in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o4-mini leads 43.6 to 29.7.
- The biggest single-benchmark swing is ARC-AGI-1: 23.3% for o1-pro and 58.7% for o4-mini.
- o4-mini is cheaper at $1.10 / $4.40 per million input/output tokens, against $150 / $600 for o1-pro.
Side by side
| o1-pro | o4-mini | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 31.5 | 41.6 |
| Released | 2025-03-19 | 2025-04-16 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 200K |
| Max output | 100K | 100K |
| Input $ / M tokens | $150 | $1.10 |
| Output $ / M tokens | $600 | $4.40 |
| Results tracked | 3 | 60 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Not comparable
o1-pro: —, o4-mini: 40.9 (#127)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| LMArena Coding | — | 1368 |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
Agentic & Tool Use Not comparable
o1-pro: —, o4-mini: 32.6 (#61)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning o4-mini leads
o1-pro: 20.4 (#239), o4-mini: 24.6 (#162)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| ARC-AGI-1 | 23.3% | 58.7% |
| EnigmaEval | 6.1% | 9.2% |
| ARC-AGI-2 | — | 6.1% |
| SimpleBench | — | 38.7% |
| Kagi LLM Benchmark | — | 67.6% |
| CritPt | — | 0.6% |
| Chess Puzzles | — | 26% |
| LMArena Hard Prompts | — | 1351 |
| Mystery Game Puzzles | — | 5% |
| DTBench | — | 77.6% |
| LMCA | — | 26.5% |
| Epoch Capabilities Index | — | 145.64 |
| ForecastBench | — | 61.8 |
Math Not comparable
o1-pro: —, o4-mini: 40.8 (#89)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 81.7% |
| Omni-MATH | — | 72% |
| LMArena Math | — | 1389 |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge o4-mini leads
o1-pro: 29.7 (#234), o4-mini: 43.6 (#91)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| Humanity's Last Exam | 8.1% | 18.1% |
| GPQA Diamond | — | 79.6% |
| SimpleQA Verified | — | 19.6% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| Vectara Hallucination Rate | — | 18.6% |
| GPQA (HELM) | — | 73.5% |
| LMArena Expert | — | 1343 |
Multimodal Not comparable
o1-pro: —, o4-mini: 40.2 (#49)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual Not comparable
o1-pro: —, o4-mini: 47.0 (#154)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| LMArena Non-English | — | 1337 |
| LMArena Chinese | — | 1354 |
| LMArena French | — | 1364 |
| LMArena German | — | 1336 |
| LMArena Japanese | — | 1308 |
| LMArena Korean | — | 1312 |
| LMArena Russian | — | 1334 |
| LMArena Spanish | — | 1347 |
Instruction Following Not comparable
o1-pro: —, o4-mini: 75.2 (#68)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| IFEval | — | 92.8% |
| LMArena Instruction Following | — | 1321 |
Long Context Not comparable
o1-pro: —, o4-mini: 45.5 (#33)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| Fiction.LiveBench | — | 77.8% |
| LMArena Longer Query | — | 1315 |
Writing & Preference Not comparable
o1-pro: —, o4-mini: 54.0 (#152)
| Benchmark | o1-pro | o4-mini |
|---|---|---|
| LMArena Text | — | 1353 |
| LMArena Creative Writing | — | 1294 |
| Short-Story Creative Writing | — | 75% |
| WildBench | — | 85.4% |
| LMArena Multi-Turn | — | 1350 |
Frequently asked questions
Is o1-pro better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 31.5 on the Noometry Index.
Which is cheaper, o1-pro or o4-mini?
o4-mini is cheaper. It lists at $1.10 per million input tokens and $4.40 per million output tokens; o1-pro lists at $150 and $600.
Which has the bigger context window?
Both accept 200K tokens.
How many benchmarks do o1-pro and o4-mini share?
3 benchmarks have published results for both models. o1-pro has 3 scored results on Noometry and o4-mini has 60.