Model comparison
GPT-5.2 vs GPT-5 Nano
GPT-5.2 is the stronger model overall, scoring 54.1 to 33.5 on the Noometry Index. GPT-5 Nano costs 35× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Last verified . 42 shared benchmarks.
Summary
- They share 42 benchmarks with published results for both. GPT-5.2 scores higher in 9 categories and GPT-5 Nano in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.2 leads 50.2 to 16.3.
- The biggest single-benchmark swing is ARC-AGI-1: 86.2% for GPT-5.2 and 20.7% for GPT-5 Nano.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
Side by side
| GPT-5.2 | GPT-5 Nano | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 54.1 | 33.5 |
| Released | 2025-12-11 | 2025-08-07 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 400K |
| Max output | 128K | 128K |
| Input $ / M tokens | $1.75 | $0.05 |
| Output $ / M tokens | $14 | $0.40 |
| Results tracked | 67 | 49 |
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Category by category
Coding GPT-5.2 leads
GPT-5.2: 51.6 (#37), GPT-5 Nano: 33.6 (#254)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| SWE-bench Verified (bash only) | 72.8% | 34.8% |
| WeirdML | 72.2% | 38.1% |
| LMArena Coding | 1447 | 1351 |
| ALE-Bench | 1,294 | 718.67 |
| SWE-bench Verified | 73.8% | — |
| LMArena WebDev | 1416 | — |
| SWE-bench Multilingual | 66.7% | — |
| GSO | 27.4% | — |
| AlgoTune | 2.05 | — |
Agentic & Tool Use GPT-5.2 leads
GPT-5.2: 40.2 (#24), GPT-5 Nano: 25.8 (#106)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | 64.9% | 21.8% |
| Berkeley Function Calling Leaderboard | 55.9% | 51.5% |
| 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% | — |
| 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), GPT-5 Nano: 16.3 (#306)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| ARC-AGI-2 | 52.9% | 2.6% |
| Kagi LLM Benchmark | 73.3% | 62.2% |
| ARC-AGI-1 | 86.2% | 20.7% |
| Chess Puzzles | 49% | 27% |
| LMArena Hard Prompts | 1445 | 1328 |
| Mystery Game Puzzles | 23% | 9% |
| DTBench | 90.9% | 62.7% |
| LMCA | 43.9% | 7.9% |
| Epoch Capabilities Index | 153.45 | 139.38 |
| ForecastBench | 60.1 | 59.1 |
| SimpleBench | 45.8% | — |
| NYT Connections (extended) | 83.6% | — |
| EnigmaEval | 10.4% | — |
| EBR-Bench | 23% | — |
Math GPT-5.2 leads
GPT-5.2: 60.0 (#38), GPT-5 Nano: 29.4 (#241)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| FrontierMath (Tiers 1-3) | 67.4% | 20% |
| FrontierMath Tier 4 | 31.7% | 2.4% |
| OTIS Mock AIME 2024-2025 | 96.1% | 81.1% |
| ProofBench | 15% | 12% |
| LMArena Math | 1440 | 1317 |
| FrontierMath (Feb 2025 set) | 40.7% | 8.3% |
| FrontierMath Tier 4 (v1) | 18.8% | 2.1% |
| MathArena Final-Answer Competitions | 72% | — |
| Omni-MATH | — | 54.6% |
| MATH Level 5 | — | 95.2% |
Knowledge GPT-5.2 leads
GPT-5.2: 59.3 (#32), GPT-5 Nano: 35.9 (#178)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | 91.4% | 69.4% |
| SimpleQA Verified | 37.1% | 11.7% |
| Vectara Hallucination Rate | 8.4% | 10.5% |
| LMArena Expert | 1445 | 1321 |
| Humanity's Last Exam | 27.8% | — |
| MMLU-Pro | — | 77.8% |
| GPQA (HELM) | — | 67.9% |
Multimodal GPT-5.2 leads
GPT-5.2: 51.3 (#7), GPT-5 Nano: 31.3 (#108)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Vision | 1268 | 1159 |
| VPCT | 84% | 37.2% |
| Furniture Assembly | 38.3% | — |
| LMArena Document | 1405 | — |
Multilingual GPT-5.2 leads
GPT-5.2: 53.4 (#67), GPT-5 Nano: 45.3 (#172)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | 1425 | 1313 |
| LMArena Chinese | 1460 | 1356 |
| LMArena German | 1448 | 1327 |
| LMArena Japanese | 1420 | 1226 |
| LMArena Korean | 1392 | 1269 |
| LMArena Russian | 1440 | 1296 |
| LMArena Spanish | 1433 | 1360 |
| LMArena French | 1455 | — |
Instruction Following Too close to call
GPT-5.2: 74.7 (#89), GPT-5 Nano: 75.0 (#79)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Instruction Following | 1417 | 1306 |
| IFEval | — | 93.2% |
Long Context GPT-5.2 leads
GPT-5.2: 44.0 (#78), GPT-5 Nano: 31.3 (#281)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Longer Query | 1428 | 1312 |
| Fiction.LiveBench | — | 44.4% |
| CL-bench | 18.2% | — |
Writing & Preference GPT-5.2 leads
GPT-5.2: 66.8 (#32), GPT-5 Nano: 39.1 (#249)
| Benchmark | GPT-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Text | 1439 | 1320 |
| LMArena Creative Writing | 1401 | 1249 |
| EQ-Bench Creative Writing | 1703 | 705 |
| LMArena Multi-Turn | 1458 | 1311 |
| WildBench | — | 80.6% |
Frequently asked questions
Is GPT-5.2 better than GPT-5 Nano?
GPT-5.2 is the stronger model overall, scoring 54.1 to 33.5 on the Noometry Index. GPT-5 Nano costs 35× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Which is cheaper, GPT-5.2 or GPT-5 Nano?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GPT-5.2 or GPT-5 Nano better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 33.6 in the Noometry coding category.
Which has the bigger context window?
Both accept 400K tokens.
How many benchmarks do GPT-5.2 and GPT-5 Nano share?
42 benchmarks have published results for both models. GPT-5.2 has 67 scored results on Noometry and GPT-5 Nano has 49.