Model comparison
GPT-5.2 vs GPT-5.5
GPT-5.5 is the stronger model overall, scoring 63.4 to 54.1 on the Noometry Index. GPT-5.2 costs 2.3× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Last verified . 54 shared benchmarks.
Summary
- They share 54 benchmarks with published results for both. GPT-5.2 scores higher in 1 category and GPT-5.5 in 9 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 leads 72.8 to 50.2.
- The biggest single-benchmark swing is FrontierMath Tier 4: 31.7% for GPT-5.2 and 72.5% for GPT-5.5.
- GPT-5.2 is cheaper at $1.75 / $14 per million input/output tokens, against $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 400K.
Side by side
| GPT-5.2 | GPT-5.5 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 54.1 | 63.4 |
| Released | 2025-12-11 | 2026-04-23 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1.05M |
| Max output | 128K | 128K |
| Input $ / M tokens | $1.75 | $5 |
| Output $ / M tokens | $14 | $30 |
| Results tracked | 67 | 71 |
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Category by category
Coding GPT-5.5 leads
GPT-5.2: 51.6 (#37), GPT-5.5: 58.2 (#17)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| SWE-bench Verified | 73.8% | 80.6% |
| LMArena WebDev | 1416 | 1513 |
| GSO | 27.4% | 40.2% |
| WeirdML | 72.2% | 84.9% |
| LMArena Coding | 1447 | 1494 |
| ALE-Bench | 1,294 | 1,943 |
| DeepSWE | — | 67% |
| FrontierCode | — | 43% |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 66.7% | — |
| SciCode | — | 56.1% |
| MirrorCode | — | 10% |
| AlgoTune | 2.05 | — |
Agentic & Tool Use GPT-5.5 leads
GPT-5.2: 40.2 (#24), GPT-5.5: 50.7 (#6)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| Terminal-Bench | 64.9% | 84.7% |
| Remote Labor Index | 2.5% | 6.3% |
| τ²-bench Banking | 32.2% | 44.6% |
| DeepResearch Bench | 41.1% | 54% |
| LMArena Search | 1207 | 1242 |
| Vending-Bench 2 | 3,591 | 7,524 |
| APEX-Agents | — | 55.1% |
| Berkeley Function Calling Leaderboard | 55.9% | — |
| OSWorld 2.0 | — | 13% |
| GDPval | 49.7% | — |
| τ²-bench Airline | 83% | — |
| τ²-bench Retail | 81.6% | — |
| τ²-bench Telecom | 89.7% | — |
| PostTrainBench | — | 27.2% |
| ExploitBench | — | 47.4% |
| GBAEval | — | 53.2% |
| GDP.pdf | — | 26% |
| METR Time Horizons | 75.3% | — |
Reasoning GPT-5.5 leads
GPT-5.2: 50.2 (#35), GPT-5.5: 72.8 (#11)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| ARC-AGI-2 | 52.9% | 85% |
| SimpleBench | 45.8% | 69% |
| Kagi LLM Benchmark | 73.3% | 88.8% |
| NYT Connections (extended) | 83.6% | 96.2% |
| ARC-AGI-1 | 86.2% | 95% |
| Chess Puzzles | 49% | 54% |
| EBR-Bench | 23% | 34.3% |
| LMArena Hard Prompts | 1445 | 1489 |
| Mystery Game Puzzles | 23% | 56% |
| DTBench | 90.9% | 96% |
| LMCA | 43.9% | 54.3% |
| Epoch Capabilities Index | 153.45 | 159.1 |
| ForecastBench | 60.1 | 60.6 |
| CritPt | — | 27.1% |
| EnigmaEval | 10.4% | — |
| Surface Evolver Bench | — | 88.1% |
| Bench to the Future 3 | — | 0.14 |
Math GPT-5.5 leads
GPT-5.2: 60.0 (#38), GPT-5.5: 81.7 (#11)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 67.4% | 85.3% |
| FrontierMath Tier 4 | 31.7% | 72.5% |
| MathArena Final-Answer Competitions | 72% | 94.3% |
| OTIS Mock AIME 2024-2025 | 96.1% | 100% |
| ProofBench | 15% | 50% |
| LMArena Math | 1440 | 1486 |
| FrontierMath (Feb 2025 set) | 40.7% | 51.7% |
| FrontierMath Tier 4 (v1) | 18.8% | 35.4% |
| FrontierMath Erdős | — | 0% |
Knowledge GPT-5.5 leads
GPT-5.2: 59.3 (#32), GPT-5.5: 64.4 (#17)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| GPQA Diamond | 91.4% | 94% |
| SimpleQA Verified | 37.1% | 63% |
| Vectara Hallucination Rate | 8.4% | 9.3% |
| LMArena Expert | 1445 | 1508 |
| Humanity's Last Exam | 27.8% | — |
Multimodal GPT-5.2 leads
GPT-5.2: 51.3 (#7), GPT-5.5: 46.9 (#12)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| LMArena Vision | 1268 | 1297 |
| Furniture Assembly | 38.3% | 44.2% |
| LMArena Document | 1405 | 1486 |
| VPCT | 84% | — |
| Blueprint-Bench 2 | — | 36.2% |
Multilingual GPT-5.5 leads
GPT-5.2: 53.4 (#67), GPT-5.5: 56.4 (#20)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| LMArena Non-English | 1425 | 1467 |
| LMArena Chinese | 1460 | 1533 |
| LMArena French | 1455 | 1486 |
| LMArena German | 1448 | 1480 |
| LMArena Japanese | 1420 | 1498 |
| LMArena Korean | 1392 | 1460 |
| LMArena Russian | 1440 | 1473 |
| LMArena Spanish | 1433 | 1468 |
Instruction Following GPT-5.5 leads
GPT-5.2: 74.7 (#89), GPT-5.5: 77.5 (#18)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| LMArena Instruction Following | 1417 | 1479 |
Long Context GPT-5.5 leads
GPT-5.2: 44.0 (#78), GPT-5.5: 48.3 (#12)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| LMArena Longer Query | 1428 | 1484 |
| CL-bench | 18.2% | — |
| CL-bench Life | — | 22.2% |
Writing & Preference GPT-5.5 leads
GPT-5.2: 66.8 (#32), GPT-5.5: 72.7 (#13)
| Benchmark | GPT-5.2 | GPT-5.5 |
|---|---|---|
| LMArena Text | 1439 | 1472 |
| LMArena Creative Writing | 1401 | 1455 |
| EQ-Bench Creative Writing | 1703 | 1844 |
| LMArena Multi-Turn | 1458 | 1476 |
| EQ-Bench 4 | — | 1315 |
Frequently asked questions
Is GPT-5.2 better than GPT-5.5?
GPT-5.5 is the stronger model overall, scoring 63.4 to 54.1 on the Noometry Index. GPT-5.2 costs 2.3× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Which is cheaper, GPT-5.2 or GPT-5.5?
GPT-5.2 is cheaper. It lists at $1.75 per million input tokens and $14 per million output tokens; GPT-5.5 lists at $5 and $30.
Is GPT-5.2 or GPT-5.5 better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.2 versus 51.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.5 does, with 1.05M tokens against 400K.
How many benchmarks do GPT-5.2 and GPT-5.5 share?
54 benchmarks have published results for both models. GPT-5.2 has 67 scored results on Noometry and GPT-5.5 has 71.