Gemini 3.7 Flash vs GPT-5.5
Full profiles: Gemini 3.7 Flash (Google) and GPT-5.5 (OpenAI): every benchmark, price and rank.
Updated · Data from 12 sources
As of 25 Oct 2026, Gemini 3.7 Flash (high) and GPT-5.5 (xhigh) are neck and neck on quality (63.5 vs 66.1 on the BenchLeader Index, inside the ±5.1 margin of error), and Gemini 3.7 Flash (high) costs 7.5× less, which makes it the better buy for most workloads. Pick Gemini 3.7 Flash for multimodal (68 vs 63); pick GPT-5.5 for agents & tools (62 vs 48).
Gemini 3.7 Flash benchmarks 
GPT-5.5 benchmarks Which to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Gemini 3.7 Flash | 63 vs 61 category score; Arena WebDev 1590 vs 1513 | Medium |
| Agents & tool use | GPT-5.5 | 62 vs 48 category score; τ³-Banking (AA) 39.0% vs 32.8% | High |
| Maths | GPT-5.5 | 70 vs 63 category score; FrontierMath Tiers 1–3 85.3% vs 71.6% | Medium |
| Reasoning | GPT-5.5 | 76 vs 67 category score; ARC-AGI-2 85.0% vs 84.6% | High |
| Knowledge | Gemini 3.7 Flash | 68 vs 65 category score; SimpleQA Verified 69.2% vs 63.0% | Medium |
| Long documents | GPT-5.5 | 67 vs 66 category score; AA-LCR 84.3% vs 81.7% | Medium |
| Images and other media | Gemini 3.7 Flash | 68 vs 63 category score; MMMU-Pro 85.5% vs 79.9% | Medium |
| Chat quality (human preference) | – | Only Gemini 3.7 Flash has an Arena rating (68 category score) | Not comparable |
| Lowest cost | Gemini 3.7 Flash | $1.50 vs $11.25 per 1M blended; agentic coding session $0.060 vs $0.420 | – |
| Fastest first answer | Gemini 3.7 Flash | 2.5 s (Gemini 3.7 Flash (high)) vs 39.1 s (GPT-5.5 (xhigh)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Gemini 3.7 Flash (high) and GPT-5.5 (xhigh) are neck and neck on quality (63.5 vs 66.1, within the ±5.1 margin).
- Gemini 3.7 Flash (high) is stronger in coding, composite, human preference, knowledge, multimodal.
- GPT-5.5 (xhigh) is stronger in agents & tools, long context, maths, reasoning, instruction following.
- Gemini 3.7 Flash (high) is 7.5× cheaper ($1.50 vs $11.25 per 1M blended).
At a glance
| Spec | Gemini 3.7 Flash | GPT-5.5 |
|---|---|---|
| Developer | OpenAI | |
| Released | 13 Aug 2026 | 23 Apr 2026 |
| Context window (tokens) | 1,048,576 | 1,050,000 |
| Compared at | Gemini 3.7 Flash (high) | GPT-5.5 (xhigh) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $0.750 / $3.75 / $0.075 | $5.00 / $30.00 / $0.500 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | Gemini 3.7 Flash (high) | GPT-5.5 (xhigh) | Leader |
|---|---|---|---|
| Reasoning | 67.3 | 75.5 | GPT-5.5 |
| Coding | 62.9 | 61.1 | Gemini 3.7 Flash |
| Agents & tools | 47.6 | 62.3 | GPT-5.5 |
| Maths | 63.3 | 69.7 | GPT-5.5 |
| Knowledge | 68.0 | 65.3 | Gemini 3.7 Flash |
| Instruction following | – | 73.9 | not comparable |
| Human preference | 68.4 | – | not comparable |
| Multimodal | 67.6 | 62.9 | Gemini 3.7 Flash |
| Long context | 65.9 | 67.3 | GPT-5.5 |
| Composite | 73.2 | 72.4 | level |
Where sources disagree
Across the 59 benchmarks both have, Gemini 3.7 Flash leads 31, GPT-5.5 leads 28. Every overall index that rates both puts Gemini 3.7 Flash ahead.
| Overall index | Gemini 3.7 Flash (high) | GPT-5.5 (xhigh) | Leader |
|---|---|---|---|
| BenchLeader Index | 63.5 | 66.1 | neck and neck |
| AA Intelligence Index v4.3.2 | 39.1 | 38.4 | Gemini 3.7 Flash |
| Arena Text | 1490 | 1482† | Gemini 3.7 Flash |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | Gemini 3.7 Flash (high) | GPT-5.5 (xhigh) |
|---|---|---|
| Blended price per 1M tokens | $1.50 | $11.25 |
| Input / output per 1M | $0.750 / $3.75 | $5.00 / $30.00 |
| Cached input per 1M | $0.075 | $0.500 |
| Price per task: chat reply | $0.0014 | $0.011 |
| Price per task: summarize a 30-page report | $0.011 | $0.078 |
| Price per task: code edit | $0.010 | $0.075 |
| Price per task: agentic coding session | $0.060 | $0.420 |
| Price per task: structured extraction | $0.0023 | $0.016 |
| Cost to run a full benchmark suite | – | $2.63 |
| Output speed | 88 tok/s | 86 tok/s |
| Time to first answer | 2.5 s | 39.1 s |
| Time to a complete answer | – | 45.0 s |
Each model is compared at its best-ranked reasoning-effort setting. Gemini 3.7 Flash (high) scores 63.5 and takes 2 s to its first answer token; Gemini 3.7 Flash (medium) scores 63.1 in 2 s, Gemini 3.7 Flash (low) scores 60.3 in 2 s. GPT-5.5 (xhigh) scores 66.1 and takes 39 s to its first answer token; GPT-5.5 (high) scores 65.3 in 14 s, GPT-5.5 (medium) scores 62.9 in 3 s. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
138 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | Gemini 3.7 Flash (high) | GPT-5.5 (xhigh) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 63.5 | 66.1 |
| Agents & tools score | BenchLeader | 47.6 | 62.3 |
| Coding score | BenchLeader | 62.9 | 61.1 |
| Composite score | BenchLeader | 73.2 | 72.4 |
| Human preference score | BenchLeader | 68.4 | – |
| Knowledge score | BenchLeader | 68.0 | 65.3 |
| Long context score | BenchLeader | 65.9 | 67.3 |
| Maths score | BenchLeader | 63.3 | 69.7 |
| Multimodal score | BenchLeader | 67.6 | 62.9 |
| Reasoning score | BenchLeader | 67.3 | 75.5 |
| Instruction following score | BenchLeader | – | 73.9 |
| Blended price $/M | BenchLeader | $1.50 | $11.25 |
| Output speed | BenchLeader | 88 tok/s | 86 tok/s |
| Time to first answer | BenchLeader | 2.5 s | 39.1 s |
| Context window | BenchLeader | 1,048,576 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 94.8% | 90.7%† |
| SimpleBench | SimpleBench | – | 69.0%† |
| Arena Hard Prompts | Arena (LMArena) | 1509 | 1499† |
| LiveBench 2.0 Reasoning | LiveBench | 87.8%† | 89.7%† |
| LiveBench 1.0 Reasoning | LiveBench | 87.8% | 89.7% |
| GPQA Diamond (AA) | Artificial Analysis | 94.5% | 93.5% |
| Humanity's Last Exam (AA) | Artificial Analysis | 47.9% | 45.8% |
| GPQA Diamond (Vals) | Vals AI | 93.9% | 93.2% |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 88.8%† |
| ARC-AGI-1 | ARC Prize | 95.5% | 95.0% |
| ARC-AGI-2 | ARC Prize | 84.6% | 85.0% |
| ARC-AGI-3 | ARC Prize | – | 0.4%† |
| CritPt | Artificial Analysis | 14.3% | 27.1% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 66.0% | 63.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 37.0% | 56.0% |
| CL-bench Life | Epoch AI Benchmarking Hub | – | 22.2%† |
| LMCA | Epoch AI Benchmarking Hub | 50.4% | 54.3% |
| DTBench | Epoch AI Benchmarking Hub | 96.8% | 96.0% |
| ForecastBench | ForecastBench | 66.2% | 64.4%† |
| BTF-3 | Epoch AI Benchmarking Hub | – | 14.3%† |
| Coding | |||
| SciCode | SciCode | 57.2% | 55.8% |
| WeirdML | WeirdML | – | 84.9% |
| FrontierCode | Cognition | 43.6%† | 43.0%† |
| GSO-Bench | GSO-Bench | – | 40.2% |
| Arena Coding | Arena (LMArena) | 1520 | 1511† |
Show all 138 benchmarks
| Metric | Publisher | Gemini 3.7 Flash (high) | GPT-5.5 (xhigh) |
|---|---|---|---|
| Coding | |||
| Arena WebDev | Arena (LMArena) | 1590 | 1513 |
| LiveBench 2.0 Coding | LiveBench | 80.5%† | 85.1%† |
| LiveBench 1.0 Coding | LiveBench | 78.9% | 82.2% |
| LiveCodeBench | Vals AI | 88.7% | 85.3% |
| IOI | Vals AI | 67.8% | – |
| SWE-bench (Vals) | Vals AI | 80.8% | 82.6% |
| Code Migration | Vals AI | 34.8% | 45.2% |
| ProgramBench | Vals AI | 0.0% | 0.5% |
| Vibe Code Bench v1.1 | Vals AI | 70.4% | 69.8% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | – | 45.4% |
| SWE Atlas: Refactoring | Scale AI SEAL | – | 44.8% |
| SWE Atlas: Test Writing | Scale AI SEAL | – | 42.6% |
| MirrorCode | Epoch AI Benchmarking Hub | – | 10.0%† |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | – | 88.1%† |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | 65.3% | 67.0% |
| ALE-Bench | Epoch AI Benchmarking Hub | 904.3 | 1943.0 |
| FrontierSWE | Epoch AI Benchmarking Hub | 20.3% | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 13.6% | 14.7% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 85.8% | 84.3% |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 11.2% | 84.7%† |
| OSWorld-Verified 2.0 | OSWorld | – | 13.0% |
| Remote Labor Index | Scale AI / CAIS | 5.0%† | 6.3%† |
| APEX-Agents | Mercor | 67.8%† | 55.1%† |
| Arena Agent | Arena (LMArena) | -1.3 | – |
| LiveBench Long Agentic | LiveBench | 28.3%† | 44.6%† |
| LiveBench Browser | LiveBench | 56.0%† | 48.0%† |
| LiveBench Agentic Coding | LiveBench | 58.3% | 54.0% |
| Terminal-Bench Hard | Artificial Analysis | – | 60.6% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 93.9% |
| Terminal-Bench 2.1 (Vals) | Vals AI | 77.5% | 76.4%† |
| MCP Atlas | Scale AI SEAL | – | 75.3% |
| HiL-Bench | Scale AI SEAL | – | 39.7%† |
| τ³-bench Banking | τ²-bench | – | 44.6% |
| τ³-Banking (AA) | Artificial Analysis | 32.8% | 39.0% |
| ITBench SRE (AA) | Artificial Analysis | – | 45.8% |
| Analyst Agent (AA) | Artificial Analysis | 60.0% | 50.0% |
| APEX-Agents (AA) | Artificial Analysis | – | 37.7% |
| CyberBench | Vals AI | 43.8% | – |
| Finance Agent v2 | Vals AI | 59.0% | 51.8% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 8.8% | 3.8% |
| Legal Research Bench | Vals AI | 34.6% | 40.4% |
| SkillsBench | Vals AI | 65.9% | 62.2% |
| SREBench | Vals AI | 4.6% | 3.8% |
| Tax Agent Bench | Vals AI | 22.8% | 21.8% |
| Terminal-Bench 2.0 (Vals) | Vals AI | – | 73.2%† |
| Terminal-Bench 4.0 (Vals) | Vals AI | 12.1% | – |
| Terminal-Bench Science | Vals AI | 7.1% | – |
| Time Horizon Index: KSP | Vals AI | – | 8.3% |
| DeepResearch Bench | DeepResearch Bench | – | 54.0%† |
| PostTrainBench | Epoch AI Benchmarking Hub | – | 27.2% |
| ExploitBench | Epoch AI Benchmarking Hub | – | 47.4%† |
| GBAEval | Epoch AI Benchmarking Hub | – | 53.2%† |
| Vending-Bench 2 | Andon Labs | – | 7523.8† |
| AutomationBench | Artificial Analysis | 62.0% | 47.3% |
| GDP.pdf (AA) | Artificial Analysis | 23.6% | 21.2% |
| EnterpriseOps-Gym | Artificial Analysis | 50.4%† | 46.6% |
| AA-Briefcase v1.1 | Artificial Analysis | – | 1136 |
| FACTS Search | 80.9%† | – | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 71.6% | 85.3% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 36.6% | 72.5% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 97.2% | 84.4%† |
| ProofBench | Vals AI | 58.0%† | 50.0% |
| LiveBench Mathematics | LiveBench | 93.5% | 95.9% |
| AIME 2026 | MathArena | – | 100.0% |
| HMMT February 2026 | MathArena | – | 98.5% |
| USAMO 2026 | MathArena | – | 98.2% |
| MathArena Apex | MathArena | – | 80.2% |
| Arena Maths | Arena (LMArena) | 1505 | 1501† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 69.2% | 63.0% |
| LiveBench 2.0 Data Analysis | LiveBench | 68.0%† | 81.6%† |
| LiveBench Research | LiveBench | 76.5%† | 61.6%† |
| LiveBench 1.0 Data Analysis | LiveBench | 68.0% | 81.6% |
| AA-Omniscience | Artificial Analysis | 26.5 | 20.5 |
| MMLU-Pro | Vals AI | 90.1% | 88.1% |
| LegalBench | Vals AI | 87.3% | 86.5% |
| CorpFin | Vals AI | – | 68.4% |
| TaxEval | Vals AI | 74.7% | 75.0% |
| CaseLaw v2 | Vals AI | – | 66.2% |
| Excel Modeling Benchmark | Vals AI | 71.3% | 64.5% |
| MedCode | Vals AI | 53.4% | 49.1% |
| MedScribe | Vals AI | 83.9% | 86.9% |
| Public Benefits Bench v1.1 | Vals AI | – | 60.9% |
| DrugDiscoveryBench | Scale AI SEAL | – | 51.6% |
| EBR-bench | Epoch AI Benchmarking Hub | – | 34.3% |
| GDP.pdf | Epoch AI Benchmarking Hub | 23.8% | 26.0% |
| AA-Omniscience: accuracy | Artificial Analysis | 55.3% | 58.0% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 35.5% | 11.0% |
| Instruction following | |||
| LiveBench Language | LiveBench | 85.5% | 87.4% |
| LiveBench Instruction Following | LiveBench | 79.9% | 70.7% |
| IFBench | Artificial Analysis | – | 75.8% |
| Arena Instruction Following | Arena (LMArena) | 1487 | 1475† |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1490 | 1477† |
| EQ-Bench 4 | EQ-Bench | – | 1294† |
| Arena Creative Writing | Arena (LMArena) | 1492 | 1448† |
| Arena Multi-turn | Arena (LMArena) | 1489 | 1484† |
| Arena Longer Queries | Arena (LMArena) | 1501 | 1487† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1314 | 1297† |
| LiveBench Design | LiveBench | 73.2%† | 70.3%† |
| MMMU-Pro | Artificial Analysis | 85.5% | 79.9% |
| MMMU-Pro (Vals) | Vals AI | 89.0% | 88.3% |
| MortgageTax | Vals AI | 66.7% | 68.8% |
| SAGE | Vals AI | 49.2% | 51.5% |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | – | 36.2%† |
| Long context | |||
| AA-LCR | Artificial Analysis | 81.7% | 84.3% |
| Arena Document | Arena (LMArena) | – | 1483† |
| MLCR | Artificial Analysis | 12.2% | 28.9% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 158.0† | 159.7† |
| LiveBench 2.0 | LiveBench | 67.2%† | 68.7%† |
| LiveBench 1.0 | LiveBench | 78.8% | 80.2% |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 39.1 | 38.4 |
| Vals RSI Index | Vals AI | 18.3% | 16.5% |
| Vals Multimodal Index | Vals AI | – | 68.1% |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 16.3% |
Data as of 2026-10-25. Best configuration of each model; every score links to its source on the model pages.
What changed
- GPT-5.5 (xhigh): 1 new result (AA-Briefcase v1.1).
- Gemini 3.7 Flash (high): 2 new results (Harvey's Legal Agent Benchmark v1, Vals RSI Index).
- GPT-5.5 (xhigh): 2 new results (Harvey's Legal Agent Benchmark v1, Vals RSI Index).
- Gemini 3.7 Flash (high): input price moved from $0.38/M to $0.75/M (97% up); output price moved from $1.88/M to $3.75/M (99% up).
- Gemini 3.7 Flash (high): 1 score revision; input price moved from $0.75/M to $0.38/M (49% down); output price moved from $3.75/M to $1.88/M (50% down).
- GPT-5.5 (xhigh): 1 score revision.
- Gemini 3.7 Flash (high): 1 new result (ForecastBench).
- Gemini 3.7 Flash (high): BenchLeader Index 62.9 → 63.5.
- Gemini 3.7 Flash (high): BenchLeader Index 60.8 → 62.9.
- Gemini 3.7 Flash (high): 18 new results (AA Intelligence Index v4.3.2, GPQA Diamond (AA), Humanity's Last Exam (AA)…).
Related comparisons
- Gemini 3.7 Flash vs Claude Opus 5.5
- Gemini 3.7 Flash vs Gemini 4 Argon
- Gemini 3.7 Flash vs Muse Wonder Full
- GPT-5.5 vs Claude Opus 5.5
- GPT-5.5 vs Gemini 4 Argon
- GPT-5.5 vs Muse Wonder Full
- GPT-5.5 vs Claude Fable 5.1
- Gemini 3.6 Flash vs GPT-5.5
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
Gemini 3.7 Flash vs GPT-5.5: questions
- Is Gemini 3.7 Flash better than GPT-5.5?
- Gemini 3.7 Flash (high) and GPT-5.5 (xhigh) are neck and neck on quality (63.5 vs 66.1, within the ±5.1 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-25, so check the category scores for your use.
- Which is cheaper, Gemini 3.7 Flash or GPT-5.5, and will that change?
- Gemini 3.7 Flash is cheaper today: $1.50 against $11.25 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Gemini 3.7 Flash better than GPT-5.5 for coding?
- Gemini 3.7 Flash scores higher in coding (63 vs 61 on the category index, where 50 is average). On Arena WebDev, the top shared coding benchmark, it is 1590 for Gemini 3.7 Flash against 1513 for GPT-5.5.
- Is Gemini 3.7 Flash better than GPT-5.5 for agents and tool use?
- GPT-5.5 scores higher in agentic tasks (62 vs 48 on the category index, where 50 is average). On τ³-Banking (AA), the top shared agents & tools benchmark, it is 32.8% for Gemini 3.7 Flash against 39.0% for GPT-5.5.
- Which is better for long documents?
- GPT-5.5 scores higher on long-context benchmarks (67 vs 66). Both take about 1.1M tokens: Gemini 3.7 Flash 1,048,576, GPT-5.5 1,050,000.
- Which has lower latency?
- Gemini 3.7 Flash (high) answers first: 2.5 s against 39.1 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Which is faster, Gemini 3.7 Flash or GPT-5.5?
- Gemini 3.7 Flash streams faster: 88 against 86 output tokens per second.
- Do independent benchmarks agree on Gemini 3.7 Flash vs GPT-5.5?
- Across the 59 benchmarks both have, Gemini 3.7 Flash leads 31, GPT-5.5 leads 28. Every overall index that rates both puts Gemini 3.7 Flash ahead.
- When was Gemini 3.7 Flash released?
- Gemini 3.7 Flash was released on 13 Aug 2026.