Model comparison
GPT-4.1 nano vs o3-mini
o3-mini is the stronger model overall, scoring 36.7 to 27.9 on the Noometry Index. GPT-4.1 nano costs 11× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. GPT-4.1 nano scores higher in 0 categories and o3-mini in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where o3-mini leads 40.8 to 24.1.
- The biggest single-benchmark swing is Aider Polyglot: 8.9% for GPT-4.1 nano and 60.4% for o3-mini.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 200K.
Side by side
| GPT-4.1 nano | o3-mini | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 27.9 | 36.7 |
| Released | 2025-04-14 | 2024-12-20 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 33K | 100K |
| Input $ / M tokens | $0.10 | $1.10 |
| Output $ / M tokens | $0.40 | $4.40 |
| Results tracked | 38 | 51 |
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Category by category
Coding o3-mini leads
GPT-4.1 nano: 24.1 (#330), o3-mini: 40.8 (#132)
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| Aider Polyglot | 8.9% | 60.4% |
| SciCode | 25.9% | 39.8% |
| WeirdML | 19% | 43.7% |
| LMArena Coding | 1306 | 1378 |
| GSO | — | 1.3% |
| LiveBench Coding | — | 82.7% |
| CadEval | — | 54% |
Agentic & Tool Use o3-mini leads
GPT-4.1 nano: 26.5 (#104), o3-mini: 29.6 (#84)
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 33% | — |
| Cybench | — | 22.5% |
Reasoning o3-mini leads
GPT-4.1 nano: 8.5 (#349), o3-mini: 16.3 (#305)
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| ARC-AGI-2 | 0% | 3% |
| ARC-AGI-1 | 0% | 34.5% |
| CritPt | 0% | 0.3% |
| LMArena Hard Prompts | 1286 | 1366 |
| DTBench | 52.5% | 68.8% |
| LMCA | 5.5% | 19% |
| Epoch Capabilities Index | 129.62 | 140.34 |
| SimpleBench | — | 22.8% |
| Kagi LLM Benchmark | 33.3% | — |
| Chess Puzzles | — | 17% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| LiveBench Data Analysis | — | 70.6% |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math o3-mini leads
GPT-4.1 nano: 26.9 (#252), o3-mini: 28.1 (#244)
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 28.9% | 76.9% |
| LMArena Math | 1274 | 1396 |
| MATH Level 5 | 70% | 96.5% |
| FrontierMath (Feb 2025 set) | 1% | 12.4% |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| Omni-MATH | 36.7% | — |
| LiveBench Math | — | 77.3% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge o3-mini leads
GPT-4.1 nano: 21.8 (#273), o3-mini: 38.3 (#146)
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| GPQA Diamond | 48.9% | 77% |
| SimpleQA Verified | 6% | 15.3% |
| LMArena Expert | 1272 | 1364 |
| MMLU-Pro | 55% | — |
| Confabulations | — | 17.9% |
| GPQA (HELM) | 50.7% | — |
Multimodal Not comparable
GPT-4.1 nano: 29.2 (#113), o3-mini: —
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| LMArena Vision | 1063 | — |
Multilingual o3-mini leads
GPT-4.1 nano: 41.6 (#205), o3-mini: 45.7 (#164)
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| LMArena Non-English | 1260 | 1319 |
| LMArena Chinese | 1270 | 1379 |
| LMArena German | 1288 | 1303 |
| LMArena Japanese | 1198 | 1286 |
| LMArena Russian | 1261 | 1304 |
| LMArena French | — | 1334 |
| LMArena Korean | — | 1314 |
| LMArena Spanish | — | 1321 |
Instruction Following o3-mini leads
GPT-4.1 nano: 67.8 (#193), o3-mini: 75.1 (#72)
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1267 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
| IFEval | 84.3% | — |
Long Context o3-mini leads
GPT-4.1 nano: 23.7 (#296), o3-mini: 33.8 (#256)
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| Fiction.LiveBench | 25% | 50% |
| LMArena Longer Query | 1283 | 1343 |
Writing & Preference o3-mini leads
GPT-4.1 nano: 40.5 (#243), o3-mini: 50.3 (#182)
| Benchmark | GPT-4.1 nano | o3-mini |
|---|---|---|
| LMArena Text | 1285 | 1337 |
| LMArena Creative Writing | 1260 | 1286 |
| LMArena Multi-Turn | 1277 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| EQ-Bench Creative Writing | 946 | — |
| WildBench | 81.2% | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is GPT-4.1 nano better than o3-mini?
o3-mini is the stronger model overall, scoring 36.7 to 27.9 on the Noometry Index. GPT-4.1 nano costs 11× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 nano or o3-mini?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is GPT-4.1 nano or o3-mini better for coding?
o3-mini scores higher on coding benchmarks: 40.8 versus 24.1 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 200K.
How many benchmarks do GPT-4.1 nano and o3-mini share?
29 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and o3-mini has 51.