Gemini 3.6 Flash vs GPT-5.5 Pro
Gemini 3.6 Flash 
GPT-5.5 Pro Verdict
- GPT-5.5 Pro (xhigh) leads on quality: 67.1 vs 61.7.
- Gemini 3.6 Flash (high) is stronger in agents & tools, coding, composite, human preference, knowledge, long context, multimodal.
- GPT-5.5 Pro (xhigh) is stronger in maths, reasoning.
- Gemini 3.6 Flash (high) is 45× cheaper ($1.50 vs $67.50 per 1M blended).
- Gemini 3.6 Flash (high) streams 46.6× faster (186 vs 4 tokens per second).
| Metric | Gemini 3.6 Flash (high) | GPT-5.5 Pro (xhigh) |
|---|---|---|
| BenchLeader Index | 61.7 | 67.1 |
| Agents & tools score | 53.7 | – |
| Coding score | 58.7 | – |
| Composite score | 59.9 | – |
| Human preference score | 68.2 | – |
| Knowledge score | 66.7 | – |
| Long context score | 66.5 | – |
| Maths score | 61.4 | 74.5 |
| Multimodal score | 66.7 | – |
| Reasoning score | 64.5 | 80.1 |
| Blended price $/M | $1.50 | $67.50 |
| Output speed | 186 tok/s | 4 tok/s |
| Time to first answer | 17.1 s | 3.5 s |
| Context window | 1.0M | 1.1M |
| GPQA Diamond | 94.1% | – |
| FrontierMath Tiers 1–3 | 59.0% | 87.7% |
| FrontierMath Tier 4 | 21.9% | 78.0% |
| OTIS Mock AIME | 94.2% | – |
| SimpleQA Verified | 66.2% | – |
| SciCode | 52.7% | – |
| WeirdML | 56.1% | – |
| LMArena Text | 1480 | – |
| LMArena Hard Prompts | 1500 | – |
| LMArena Coding | 1517 | – |
| LMArena WebDev | 1537 | – |
| LMArena Vision | 1300 | – |
| LMArena Agent | -5.9 | – |
| LiveBench | 73.6% | – |
| LiveBench Reasoning | 85.2% | – |
| LiveBench Coding | 77.9% | – |
| LiveBench Agentic Coding | 43.4% | – |
| LiveBench Mathematics | 86.4% | – |
| LiveBench Data Analysis | 63.0% | – |
| LiveBench Language | 83.9% | – |
| LiveBench Instruction Following | 75.4% | – |
| AA Intelligence Index | 34.3 | – |
| AA-LCR | 80.0% | – |
| MMMU-Pro | 83.2% | – |
| AA-Omniscience | 22.1 | – |
| GPQA Diamond (AA) | 92.8% | – |
| Humanity's Last Exam (AA) | 40.8% | – |
| SciCode (AA) | 53.4% | – |
| LiveCodeBench | 88.1% | – |
| MMLU-Pro | 89.3% | – |
| IOI | 35.1% | – |
| LegalBench | 86.7% | – |
| CorpFin | 63.3% | – |
| TaxEval | 74.9% | – |
| Terminal-Bench 2.1 (Vals) | 73.8% | – |
| SWE-bench (Vals) | 79.6% | – |
| GPQA Diamond (Vals) | 93.4% | – |
| Vals Index | 55.4 | – |
| ARC-AGI-1 | 91.2% | 95.0% |
| ARC-AGI-2 | 60.4% | 84.2% |
| CritPt | 10.6% | 30.6% |
| GDPval (AA) | 41.6% | – |
| τ²-Bench Banking (AA) | 29.9% | – |
| BioMysteryBench | 58.5% | – |
| Code Migration | 30.9% | – |
| CyberBench | 48.5% | – |
| Excel Modeling Benchmark | 65.4% | – |
| Finance Agent v2 | 56.3% | – |
| Harvey's Legal Agent Benchmark | 3.3% | – |
| Legal Research Bench | 25.0% | – |
| MedCode | 53.1% | – |
| MedScribe | 79.7% | – |
| MMMU-Pro (Vals) | 88.4% | – |
| MortgageTax | 67.6% | – |
| ProgramBench | 0.0% | – |
| Public Benefits Bench | 56.6% | – |
| SAGE | 48.7% | – |
| Terminal-Bench 4.0 (Vals) | 4.5% | – |
| Terminal-Bench Science | 1.4% | – |
| Vals Multimodal Index | 65.1% | – |
| Vibe Code Bench v1.1 | 64.0% | – |
| LMArena Maths | 1504 | – |
| LMArena Creative Writing | 1470 | – |
| LMArena Instruction Following | 1473 | – |
| LMArena Multi-turn | 1485 | – |
| LMArena Longer Queries | 1487 | – |
| LMArena Document | 1453 | – |
| Chess Puzzles | 40.0% | – |
| Mystery Game Puzzles | 30.0% | – |
| DeepSWE | 46.7% | – |
| LMCA | 44.9% | 53.9% |
| DTBench | 95.5% | 96.0% |
| CursorBench | 53.5% | – |
| ALE-Bench | 715.5 | – |
| GDP.pdf | 14.0% | – |
Data as of 2026-09-19. Best configuration of each model; every score links to its source on the model pages.
Gemini 3.6 Flash vs GPT-5.5 Pro: questions
- Is Gemini 3.6 Flash better than GPT-5.5 Pro?
- GPT-5.5 Pro (xhigh) leads on quality: 67.1 vs 61.7. The BenchLeader Index combines every independent quality benchmark; GPT-5.5 Pro (xhigh) is ahead overall as of 2026-09-19, but check the category scores for your use.
- Which is cheaper, Gemini 3.6 Flash or GPT-5.5 Pro?
- Gemini 3.6 Flash is cheaper: $1.50 against $67.50 per million tokens, blended at three input tokens per output token.
- Which is faster, Gemini 3.6 Flash or GPT-5.5 Pro?
- Gemini 3.6 Flash streams faster: 186 against 4 output tokens per second.
- Which has the larger context window?
- GPT-5.5 Pro accepts more context: 1.1M against 1.0M tokens.