Model comparison
GPT-5 vs GPT-5.2
GPT-5.2 is the stronger model overall, scoring 54.1 to 50.9 on the Noometry Index.
Last verified . 55 shared benchmarks.
Summary
- They share 55 benchmarks with published results for both. GPT-5 scores higher in 1 category and GPT-5.2 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 44.0.
- The biggest single-benchmark swing is ARC-AGI-2: 9.9% for GPT-5 and 52.9% for GPT-5.2.
- GPT-5 is cheaper at $1.25 / $10 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
Side by side
| GPT-5 | GPT-5.2 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 50.9 | 54.1 |
| Released | 2025-08-07 | 2025-12-11 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 400K |
| Max output | 128K | 128K |
| Input $ / M tokens | $1.25 | $1.75 |
| Output $ / M tokens | $10 | $14 |
| Results tracked | 69 | 67 |
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Category by category
Coding GPT-5.2 leads
GPT-5: 50.3 (#47), GPT-5.2: 51.6 (#37)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| SWE-bench Verified | 73.6% | 73.8% |
| SWE-bench Verified (bash only) | 65% | 72.8% |
| LMArena WebDev | 1418 | 1416 |
| GSO | 6.9% | 27.4% |
| WeirdML | 60.7% | 72.2% |
| LMArena Coding | 1436 | 1447 |
| ALE-Bench | 1,162 | 1,294 |
| AlgoTune | 1.67 | 2.05 |
| Aider Polyglot | 88% | — |
| SWE-bench Multilingual | — | 66.7% |
| SciCode | 42.9% | — |
Agentic & Tool Use GPT-5.2 leads
GPT-5: 33.1 (#56), GPT-5.2: 40.2 (#24)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| Terminal-Bench | 49.6% | 64.9% |
| GDPval | 34.8% | 49.7% |
| Remote Labor Index | 1.7% | 2.5% |
| DeepResearch Bench | 49.6% | 41.1% |
| LMArena Search | 1133 | 1207 |
| METR Time Horizons | 69.6% | 75.3% |
| Berkeley Function Calling Leaderboard | — | 55.9% |
| τ²-bench Airline | — | 83% |
| τ²-bench Banking | — | 32.2% |
| τ²-bench Retail | — | 81.6% |
| τ²-bench Telecom | — | 89.7% |
| BALROG | 32.8% | — |
| Vending-Bench 2 | — | 3,591 |
Reasoning GPT-5.2 leads
GPT-5: 38.3 (#64), GPT-5.2: 50.2 (#35)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| ARC-AGI-2 | 9.9% | 52.9% |
| SimpleBench | 56.7% | 45.8% |
| Kagi LLM Benchmark | 72.7% | 73.3% |
| ARC-AGI-1 | 65.7% | 86.2% |
| Chess Puzzles | 37% | 49% |
| EnigmaEval | 10.5% | 10.4% |
| EBR-Bench | 12.7% | 23% |
| LMArena Hard Prompts | 1416 | 1445 |
| Mystery Game Puzzles | 23% | 23% |
| DTBench | 90.7% | 90.9% |
| LMCA | 40% | 43.9% |
| Epoch Capabilities Index | 150 | 153.45 |
| ForecastBench | 61.4 | 60.1 |
| NYT Connections (extended) | — | 83.6% |
| CritPt | 12.6% | — |
Math GPT-5.2 leads
GPT-5: 55.0 (#44), GPT-5.2: 60.0 (#38)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.4% | 67.4% |
| FrontierMath Tier 4 | 22% | 31.7% |
| OTIS Mock AIME 2024-2025 | 91.4% | 96.1% |
| ProofBench | 18% | 15% |
| LMArena Math | 1407 | 1440 |
| FrontierMath (Feb 2025 set) | 32.4% | 40.7% |
| FrontierMath Tier 4 (v1) | 12.5% | 18.8% |
| MathArena Final-Answer Competitions | — | 72% |
| Omni-MATH | 64.7% | — |
| MATH Level 5 | 98.1% | — |
Knowledge GPT-5.2 leads
GPT-5: 56.6 (#43), GPT-5.2: 59.3 (#32)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| GPQA Diamond | 86.2% | 91.4% |
| Humanity's Last Exam | 25.3% | 27.8% |
| SimpleQA Verified | 50.1% | 37.1% |
| Vectara Hallucination Rate | 14.7% | 8.4% |
| LMArena Expert | 1419 | 1445 |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.3% | — |
| GPQA (HELM) | 79.2% | — |
Multimodal GPT-5.2 leads
GPT-5: 46.8 (#13), GPT-5.2: 51.3 (#7)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| LMArena Vision | 1232 | 1268 |
| VPCT | 66% | 84% |
| GeoBench | 81% | — |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |
Multilingual GPT-5.2 leads
GPT-5: 51.4 (#110), GPT-5.2: 53.4 (#67)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1397 | 1425 |
| LMArena Chinese | 1422 | 1460 |
| LMArena French | 1410 | 1455 |
| LMArena German | 1416 | 1448 |
| LMArena Japanese | 1409 | 1420 |
| LMArena Korean | 1360 | 1392 |
| LMArena Russian | 1406 | 1440 |
| LMArena Spanish | 1399 | 1433 |
Instruction Following Too close to call
GPT-5: 73.8 (#113), GPT-5.2: 74.7 (#89)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1388 | 1417 |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), GPT-5.2: 44.0 (#78)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| LMArena Longer Query | 1399 | 1428 |
| Fiction.LiveBench | 97.2% | — |
| CL-bench | — | 18.2% |
Writing & Preference GPT-5.2 leads
GPT-5: 63.4 (#65), GPT-5.2: 66.8 (#32)
| Benchmark | GPT-5 | GPT-5.2 |
|---|---|---|
| LMArena Text | 1406 | 1439 |
| LMArena Creative Writing | 1365 | 1401 |
| EQ-Bench Creative Writing | 1627 | 1703 |
| LMArena Multi-Turn | 1426 | 1458 |
| Short-Story Creative Writing | 86% | — |
| WildBench | 85.7% | — |
Frequently asked questions
Is GPT-5 better than GPT-5.2?
GPT-5.2 is the stronger model overall, scoring 54.1 to 50.9 on the Noometry Index.
Which is cheaper, GPT-5 or GPT-5.2?
GPT-5 is cheaper. It lists at $1.25 per million input tokens and $10 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GPT-5 or GPT-5.2 better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 50.3 in the Noometry coding category.
Which has the bigger context window?
Both accept 400K tokens.
How many benchmarks do GPT-5 and GPT-5.2 share?
55 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and GPT-5.2 has 67.