Model comparison
GPT-4o vs o3-pro
o3-pro is the stronger model overall, scoring 42.9 to 28.6 on the Noometry Index. GPT-4o costs 8.0× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. GPT-4o scores higher in 0 categories and o3-pro in 5 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.2 to 39.4.
- The biggest single-benchmark swing is ARC-AGI-1: 4.5% for GPT-4o and 59.3% for o3-pro.
- GPT-4o is cheaper at $2.50 / $10 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 128K.
Side by side
| GPT-4o | o3-pro | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 28.6 | 42.9 |
| Released | 2024-05-13 | 2025-06-10 |
| Weights | Proprietary | Proprietary |
| Context window | 128K | 200K |
| Max output | 16K | 100K |
| Input $ / M tokens | $2.50 | $20 |
| Output $ / M tokens | $10 | $80 |
| Results tracked | 72 | 12 |
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Category by category
Coding o3-pro leads
GPT-4o: 24.8 (#328), o3-pro: 55.5 (#24)
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| Aider Polyglot | 45.3% | 84.9% |
| WeirdML | 25.1% | 58.2% |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| GSO | 0% | — |
| BigCodeBench Instruct | 51.1% | — |
| LiveBench Coding | 51.4% | — |
| LMArena Coding | 1297 | — |
| BigCodeBench Complete | 61.1% | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Not comparable
GPT-4o: 21.0 (#141), o3-pro: —
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning o3-pro leads
GPT-4o: 9.4 (#343), o3-pro: 23.8 (#171)
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| ARC-AGI-2 | 0% | 4.9% |
| ARC-AGI-1 | 4.5% | 59.3% |
| DTBench | 64.5% | 86.9% |
| LMCA | 16.6% | 38.5% |
| Epoch Capabilities Index | 128.97 | 147.42 |
| SimpleBench | 17.8% | — |
| Kagi LLM Benchmark | — | 72.1% |
| CritPt | 0% | — |
| Chess Puzzles | 13% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| LMArena Hard Prompts | 1281 | — |
| LiveBench Data Analysis | 60.9% | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
Math Not comparable
GPT-4o: 10.6 (#312), o3-pro: —
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0.4% | — |
| OTIS Mock AIME 2024-2025 | 6.4% | — |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| LMArena Math | 1285 | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Too close to call
GPT-4o: 28.8 (#242), o3-pro: 29.5 (#238)
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| Confabulations | 15.3% | 14.2% |
| Vectara Hallucination Rate | 9.6% | 23.3% |
| GPQA Diamond | 49.2% | — |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| MMLU-Pro | 71.3% | — |
| GPQA (HELM) | 52% | — |
| LMArena Expert | 1250 | — |
| MMLU | 88.1% | — |
Multimodal Not comparable
GPT-4o: 34.5 (#91), o3-pro: —
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |
Multilingual Not comparable
GPT-4o: 43.2 (#186), o3-pro: —
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| LMArena Non-English | 1283 | — |
| LMArena Chinese | 1277 | — |
| LMArena French | 1304 | — |
| LMArena German | 1282 | — |
| LMArena Japanese | 1257 | — |
| LMArena Korean | 1234 | — |
| LMArena Russian | 1286 | — |
| LMArena Spanish | 1292 | — |
Instruction Following Not comparable
GPT-4o: 66.6 (#207), o3-pro: —
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |
| LMArena Instruction Following | 1278 | — |
Long Context o3-pro leads
GPT-4o: 39.4 (#179), o3-pro: 72.2 (#1)
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| Fiction.LiveBench | 66.7% | 97.2% |
| LMArena Longer Query | 1289 | — |
Writing & Preference o3-pro leads
GPT-4o: 52.6 (#166), o3-pro: 57.1 (#133)
| Benchmark | GPT-4o | o3-pro |
|---|---|---|
| Short-Story Creative Writing | 81.8% | 84.4% |
| LMArena Text | 1300 | — |
| LMArena Creative Writing | 1292 | — |
| WildBench | 82.8% | — |
| LMArena Multi-Turn | 1302 | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than o3-pro?
o3-pro is the stronger model overall, scoring 42.9 to 28.6 on the Noometry Index. GPT-4o costs 8.0× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Which is cheaper, GPT-4o or o3-pro?
GPT-4o is cheaper. It lists at $2.50 per million input tokens and $10 per million output tokens; o3-pro lists at $20 and $80.
Is GPT-4o or o3-pro better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 24.8 in the Noometry coding category.
Which has the bigger context window?
o3-pro does, with 200K tokens against 128K.
How many benchmarks do GPT-4o and o3-pro share?
11 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and o3-pro has 12.