Model comparison
GPT-5.2 vs o1-mini
GPT-5.2 is the stronger model overall, scoring 54.1 to 34.0 on the Noometry Index.
Last verified . 25 shared benchmarks.
Summary
- They share 25 benchmarks with published results for both. GPT-5.2 scores higher in 9 categories and o1-mini in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.2 leads 50.2 to 8.8.
- The biggest single-benchmark swing is ARC-AGI-1: 86.2% for GPT-5.2 and 14% for o1-mini.
Side by side
| GPT-5.2 | o1-mini | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 54.1 | 34.0 |
| Released | 2025-12-11 | 2024-09-12 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | — |
| Max output | 128K | — |
| Input $ / M tokens | $1.75 | — |
| Output $ / M tokens | $14 | — |
| Results tracked | 67 | 39 |
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Category by category
Coding GPT-5.2 leads
GPT-5.2: 51.6 (#37), o1-mini: 35.5 (#224)
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| WeirdML | 72.2% | 36.3% |
| LMArena Coding | 1447 | 1362 |
| SWE-bench Verified | 73.8% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| Aider Polyglot | — | 32.9% |
| LMArena WebDev | 1416 | — |
| SWE-bench Multilingual | 66.7% | — |
| GSO | 27.4% | — |
| LiveBench Coding | — | 48% |
| ALE-Bench | 1,294 | — |
| AlgoTune | 2.05 | — |
| HumanEval+ | — | 89% |
| MBPP+ | — | 78.8% |
Agentic & Tool Use GPT-5.2 leads
GPT-5.2: 40.2 (#24), o1-mini: 24.6 (#118)
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| Terminal-Bench | 64.9% | — |
| Berkeley Function Calling Leaderboard | 55.9% | — |
| GDPval | 49.7% | — |
| Remote Labor Index | 2.5% | — |
| τ²-bench Airline | 83% | — |
| τ²-bench Banking | 32.2% | — |
| τ²-bench Retail | 81.6% | — |
| τ²-bench Telecom | 89.7% | — |
| Cybench | — | 10% |
| DeepResearch Bench | 41.1% | — |
| LMArena Search | 1207 | — |
| METR Time Horizons | 75.3% | — |
| Vending-Bench 2 | 3,591 | — |
Reasoning GPT-5.2 leads
GPT-5.2: 50.2 (#35), o1-mini: 8.8 (#346)
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| ARC-AGI-2 | 52.9% | 0.8% |
| SimpleBench | 45.8% | 18.1% |
| ARC-AGI-1 | 86.2% | 14% |
| LMArena Hard Prompts | 1445 | 1333 |
| Epoch Capabilities Index | 153.45 | 135.82 |
| Kagi LLM Benchmark | 73.3% | — |
| NYT Connections (extended) | 83.6% | — |
| Chess Puzzles | 49% | — |
| EnigmaEval | 10.4% | — |
| EBR-Bench | 23% | — |
| LiveBench Reasoning | — | 72.3% |
| Mystery Game Puzzles | 23% | — |
| DTBench | 90.9% | — |
| LiveBench Data Analysis | — | 57.9% |
| LMCA | 43.9% | — |
| ForecastBench | 60.1 | — |
| LiveBench | — | 57.8% |
Math GPT-5.2 leads
GPT-5.2: 60.0 (#38), o1-mini: 35.4 (#186)
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 96.1% | 46.9% |
| LMArena Math | 1440 | 1358 |
| FrontierMath (Feb 2025 set) | 40.7% | 1.7% |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 72% | — |
| ProofBench | 15% | — |
| LiveBench Math | — | 62% |
| MATH Level 5 | — | 89.2% |
| FrontierMath Tier 4 (v1) | 18.8% | — |
Knowledge GPT-5.2 leads
GPT-5.2: 59.3 (#32), o1-mini: 34.9 (#192)
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| GPQA Diamond | 91.4% | 62.4% |
| LMArena Expert | 1445 | 1316 |
| Humanity's Last Exam | 27.8% | — |
| SimpleQA Verified | 37.1% | — |
| Confabulations | — | 18.6% |
| Vectara Hallucination Rate | 8.4% | — |
Multimodal Not comparable
GPT-5.2: 51.3 (#7), o1-mini: —
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| LMArena Vision | 1268 | — |
| VPCT | 84% | — |
| Furniture Assembly | 38.3% | — |
| LMArena Document | 1405 | — |
Multilingual GPT-5.2 leads
GPT-5.2: 53.4 (#67), o1-mini: 43.6 (#182)
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| LMArena Non-English | 1425 | 1289 |
| LMArena Chinese | 1460 | 1314 |
| LMArena French | 1455 | 1293 |
| LMArena German | 1448 | 1278 |
| LMArena Japanese | 1420 | 1245 |
| LMArena Korean | 1392 | 1223 |
| LMArena Russian | 1440 | 1283 |
| LMArena Spanish | 1433 | 1303 |
Instruction Following GPT-5.2 leads
GPT-5.2: 74.7 (#89), o1-mini: 66.7 (#206)
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| LMArena Instruction Following | 1417 | 1304 |
| LiveBench Instruction Following | — | 65.4% |
Long Context GPT-5.2 leads
GPT-5.2: 44.0 (#78), o1-mini: 40.1 (#161)
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| LMArena Longer Query | 1428 | 1320 |
| CL-bench | 18.2% | — |
Writing & Preference GPT-5.2 leads
GPT-5.2: 66.8 (#32), o1-mini: 48.4 (#202)
| Benchmark | GPT-5.2 | o1-mini |
|---|---|---|
| LMArena Text | 1439 | 1317 |
| LMArena Creative Writing | 1401 | 1244 |
| LMArena Multi-Turn | 1458 | 1314 |
| Short-Story Creative Writing | — | 64.9% |
| EQ-Bench Creative Writing | 1703 | — |
| LiveBench Language | — | 40.9% |
Frequently asked questions
Is GPT-5.2 better than o1-mini?
GPT-5.2 is the stronger model overall, scoring 54.1 to 34.0 on the Noometry Index.
Is GPT-5.2 or o1-mini better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 35.5 in the Noometry coding category.
How many benchmarks do GPT-5.2 and o1-mini share?
25 benchmarks have published results for both models. GPT-5.2 has 67 scored results on Noometry and o1-mini has 39.