Model comparison
Gemini 3.6 Flash vs GPT-5.2
Gemini 3.6 Flash and GPT-5.2 score almost the same on the Noometry Index (54.1 vs 54.1), so choose on price, context window or the category you care about most.
Last verified . 39 shared benchmarks.
GoogleSummary
- They share 39 benchmarks with published results for both. Gemini 3.6 Flash scores higher in 6 categories and GPT-5.2 in 4 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in multimodal, where GPT-5.2 leads 51.3 to 38.5.
- The biggest single-benchmark swing is SimpleQA Verified: 66.2% for Gemini 3.6 Flash and 37.1% for GPT-5.2.
- Gemini 3.6 Flash is cheaper at $0.75 / $3.75 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- Gemini 3.6 Flash accepts more context: 1.05M tokens versus 400K.
Side by side
| Gemini 3.6 Flash | GPT-5.2 | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 54.1 | 54.1 |
| Released | 2026-07-21 | 2025-12-11 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 400K |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.75 | $1.75 |
| Output $ / M tokens | $3.75 | $14 |
| Results tracked | 46 | 67 |
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Category by category
Coding GPT-5.2 leads
Gemini 3.6 Flash: 50.0 (#48), GPT-5.2: 51.6 (#37)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| LMArena WebDev | 1538 | 1416 |
| WeirdML | 56.1% | 72.2% |
| LMArena Coding | 1491 | 1447 |
| ALE-Bench | 715.52 | 1,294 |
| SWE-bench Verified | — | 73.8% |
| DeepSWE | 46.7% | — |
| FrontierCode | 34.4% | — |
| SWE-bench Verified (bash only) | — | 72.8% |
| SWE-bench Multilingual | — | 66.7% |
| SciCode | 52.7% | — |
| GSO | — | 27.4% |
| AlgoTune | — | 2.05 |
Agentic & Tool Use GPT-5.2 leads
Gemini 3.6 Flash: 32.3 (#65), GPT-5.2: 40.2 (#24)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| Terminal-Bench | — | 64.9% |
| APEX-Agents | 46.9% | — |
| 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% |
| DeepResearch Bench | — | 41.1% |
| GDP.pdf | 14% | — |
| LMArena Search | — | 1207 |
| METR Time Horizons | — | 75.3% |
| Vending-Bench 2 | — | 3,591 |
Reasoning Gemini 3.6 Flash leads
Gemini 3.6 Flash: 58.8 (#22), GPT-5.2: 50.2 (#35)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| ARC-AGI-2 | 60.4% | 52.9% |
| NYT Connections (extended) | 89% | 83.6% |
| ARC-AGI-1 | 91.2% | 86.2% |
| Chess Puzzles | 43% | 49% |
| LMArena Hard Prompts | 1485 | 1445 |
| Mystery Game Puzzles | 30% | 23% |
| DTBench | 95.5% | 90.9% |
| LMCA | 44.9% | 43.9% |
| Epoch Capabilities Index | 154.25 | 153.45 |
| SimpleBench | — | 45.8% |
| Kagi LLM Benchmark | — | 73.3% |
| CritPt | 10.6% | — |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| ForecastBench | — | 60.1 |
Math GPT-5.2 leads
Gemini 3.6 Flash: 57.3 (#40), GPT-5.2: 60.0 (#38)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 58.9% | 67.4% |
| FrontierMath Tier 4 | 22% | 31.7% |
| MathArena Final-Answer Competitions | 70.8% | 72% |
| OTIS Mock AIME 2024-2025 | 94.2% | 96.1% |
| ProofBench | 36% | 15% |
| LMArena Math | 1505 | 1440 |
| FrontierMath (Feb 2025 set) | — | 40.7% |
| FrontierMath Tier 4 (v1) | — | 18.8% |
Knowledge Gemini 3.6 Flash leads
Gemini 3.6 Flash: 67.8 (#8), GPT-5.2: 59.3 (#32)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| GPQA Diamond | 94.1% | 91.4% |
| SimpleQA Verified | 66.2% | 37.1% |
| LMArena Expert | 1488 | 1445 |
| Humanity's Last Exam | — | 27.8% |
| Vectara Hallucination Rate | — | 8.4% |
Multimodal GPT-5.2 leads
Gemini 3.6 Flash: 38.5 (#64), GPT-5.2: 51.3 (#7)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| LMArena Vision | 1298 | 1268 |
| Furniture Assembly | 23.3% | 38.3% |
| LMArena Document | 1456 | 1405 |
| VPCT | — | 84% |
| Blueprint-Bench 2 | 31.2% | — |
Multilingual Gemini 3.6 Flash leads
Gemini 3.6 Flash: 56.5 (#19), GPT-5.2: 53.4 (#67)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1469 | 1425 |
| LMArena Chinese | 1531 | 1460 |
| LMArena French | 1504 | 1455 |
| LMArena German | 1478 | 1448 |
| LMArena Japanese | 1476 | 1420 |
| LMArena Korean | 1431 | 1392 |
| LMArena Russian | 1487 | 1440 |
| LMArena Spanish | 1475 | 1433 |
Instruction Following Gemini 3.6 Flash leads
Gemini 3.6 Flash: 77.0 (#33), GPT-5.2: 74.7 (#89)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1466 | 1417 |
Long Context Gemini 3.6 Flash leads
Gemini 3.6 Flash: 45.1 (#50), GPT-5.2: 44.0 (#78)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| LMArena Longer Query | 1474 | 1428 |
| CL-bench | — | 18.2% |
Writing & Preference Gemini 3.6 Flash leads
Gemini 3.6 Flash: 68.2 (#27), GPT-5.2: 66.8 (#32)
| Benchmark | Gemini 3.6 Flash | GPT-5.2 |
|---|---|---|
| LMArena Text | 1479 | 1439 |
| LMArena Creative Writing | 1465 | 1401 |
| EQ-Bench Creative Writing | 1604 | 1703 |
| LMArena Multi-Turn | 1481 | 1458 |
Frequently asked questions
Is Gemini 3.6 Flash better than GPT-5.2?
Gemini 3.6 Flash and GPT-5.2 score almost the same on the Noometry Index (54.1 vs 54.1), so choose on price, context window or the category you care about most.
Which is cheaper, Gemini 3.6 Flash or GPT-5.2?
Gemini 3.6 Flash is cheaper. It lists at $0.75 per million input tokens and $3.75 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is Gemini 3.6 Flash or GPT-5.2 better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 50.0 in the Noometry coding category.
Which has the bigger context window?
Gemini 3.6 Flash does, with 1.05M tokens against 400K.
How many benchmarks do Gemini 3.6 Flash and GPT-5.2 share?
39 benchmarks have published results for both models. Gemini 3.6 Flash has 46 scored results on Noometry and GPT-5.2 has 67.