Gemini 3.7 Flash vs GPT-5.6 Sol
Full profiles: Gemini 3.7 Flash (Google) and GPT-5.6 Sol (OpenAI): every benchmark, price and rank.
Updated · Data from 10 sources
As of 25 Oct 2026, Gemini 3.7 Flash (high) and GPT-5.6 Sol (max) are neck and neck on quality (63.5 vs 68.0 on the BenchLeader Index, inside the ±5.1 margin of error), and Gemini 3.7 Flash (high) costs 5.3× less, which makes it the better buy for most workloads. Pick Gemini 3.7 Flash for knowledge (68 vs 65); pick GPT-5.6 Sol for agents & tools (67 vs 48).
Gemini 3.7 Flash benchmarks high Google
GPT-5.6 Sol benchmarks max OpenAIOpen in the comparison toolWhich 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 | GPT-5.6 Sol | 66 vs 63 category score; IOI 91.2% vs 67.8% | Medium |
| Agents & tool use | GPT-5.6 Sol | 67 vs 48 category score; Terminal-Bench 37.3% vs 11.2% | High |
| Maths | GPT-5.6 Sol | 71 vs 63 category score; FrontierMath Tiers 1–3 89.1% vs 71.6% | High |
| Reasoning | GPT-5.6 Sol | 72 vs 67 category score; ARC-AGI-2 92.5% vs 84.6% | Medium |
| Knowledge | Gemini 3.7 Flash | 68 vs 65 category score; AA-Omniscience 26.5 vs 22.0 | Medium |
| Long documents | GPT-5.6 Sol | 67 vs 66 category score; AA-LCR 84.0% vs 81.7% | Medium |
| Images and other media | Gemini 3.7 Flash | 68 vs 67 category score; MMMU-Pro 85.5% vs 83.4% | 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 $8.00 per 1M blended; agentic coding session $0.060 vs $0.320 | – |
| Fastest first answer | Gemini 3.7 Flash | 2.5 s (Gemini 3.7 Flash (high)) vs 69.5 s (GPT-5.6 Sol (max)) 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-5.6 Sol (max) are neck and neck on quality (63.5 vs 68.0, within the ±5.1 margin).
- Gemini 3.7 Flash (high) is stronger in human preference, knowledge, multimodal.
- GPT-5.6 Sol (max) is stronger in agents & tools, coding, composite, long context, maths, reasoning, instruction following.
- Gemini 3.7 Flash (high) is 5.3× cheaper ($1.50 vs $8.00 per 1M blended).
At a glance
| Spec | Gemini 3.7 Flash | GPT-5.6 Sol |
|---|---|---|
| Developer | OpenAI | |
| Released | 13 Aug 2026 | 9 Jul 2026 |
| Context window (tokens) | 1,048,576 | 1,050,000 |
| Compared at | Gemini 3.7 Flash (high) | GPT-5.6 Sol (max) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $0.750 / $3.75 / $0.075 | $4.00 / $20.00 / $0.400 |
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-5.6 Sol (max) | Leader |
|---|---|---|---|
| Reasoning | 67.3 | 72.4 | GPT-5.6 Sol |
| Coding | 62.9 | 65.9 | GPT-5.6 Sol |
| Agents & tools | 47.6 | 66.5 | GPT-5.6 Sol |
| Maths | 63.3 | 71.0 | GPT-5.6 Sol |
| Knowledge | 68.0 | 65.3 | Gemini 3.7 Flash |
| Instruction following | – | 71.1 | not comparable |
| Human preference | 68.4 | – | not comparable |
| Multimodal | 67.6 | 66.5 | Gemini 3.7 Flash |
| Long context | 65.9 | 67.1 | GPT-5.6 Sol |
| Composite | 73.2 | 82.0 | GPT-5.6 Sol |
Where sources disagree
Across the 68 benchmarks both have, Gemini 3.7 Flash leads 21, GPT-5.6 Sol leads 47. Independent sources split: Gemini 3.7 Flash leads on Arena Text; GPT-5.6 Sol leads on AA Intelligence Index v4.3.2.
| Overall index | Gemini 3.7 Flash (high) | GPT-5.6 Sol (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 63.5 | 68.0 | neck and neck |
| AA Intelligence Index v4.3.2 | 39.1 | 47.0 | GPT-5.6 Sol |
| Arena Text | 1490 | 1485† | 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-5.6 Sol (max) |
|---|---|---|
| Blended price per 1M tokens | $1.50 | $8.00 |
| Input / output per 1M | $0.750 / $3.75 | $4.00 / $20.00 |
| Cached input per 1M | $0.075 | $0.400 |
| Price per task: chat reply | $0.0014 | $0.0076 |
| Price per task: summarize a 30-page report | $0.011 | $0.060 |
| Price per task: code edit | $0.010 | $0.054 |
| Price per task: agentic coding session | $0.060 | $0.320 |
| Price per task: structured extraction | $0.0023 | $0.012 |
| Cost to run a full benchmark suite | – | $1.99 |
| Output speed | 88 tok/s | 89 tok/s |
| Time to first answer | 2.5 s | 69.5 s |
| Time to a complete answer | – | 75.1 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-5.6 Sol (max) scores 68.0 and takes 69 s to its first answer token; GPT-5.6 Sol (xhigh) scores 66.6 in 17 s, GPT-5.6 Sol (high) scores 66.3 in 7 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
140 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-5.6 Sol (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 63.5 | 68.0 |
| Agents & tools score | BenchLeader | 47.6 | 66.5 |
| Coding score | BenchLeader | 62.9 | 65.9 |
| Composite score | BenchLeader | 73.2 | 82.0 |
| Human preference score | BenchLeader | 68.4 | – |
| Knowledge score | BenchLeader | 68.0 | 65.3 |
| Long context score | BenchLeader | 65.9 | 67.1 |
| Maths score | BenchLeader | 63.3 | 71.0 |
| Multimodal score | BenchLeader | 67.6 | 66.5 |
| Reasoning score | BenchLeader | 67.3 | 72.4 |
| Instruction following score | BenchLeader | – | 71.1 |
| Blended price $/M | BenchLeader | $1.50 | $8.00 |
| Output speed | BenchLeader | 88 tok/s | 89 tok/s |
| Time to first answer | BenchLeader | 2.5 s | 69.5 s |
| Context window | BenchLeader | 1,048,576 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 94.8% | 93.5% |
| SimpleBench | SimpleBench | – | 64.8%† |
| Arena Hard Prompts | Arena (LMArena) | 1509 | 1512† |
| LiveBench 2.0 Reasoning | LiveBench | 87.8%† | 91.7%† |
| LiveBench 1.0 Reasoning | LiveBench | 87.8% | 91.7% |
| GPQA Diamond (AA) | Artificial Analysis | 94.5% | 94.1% |
| Humanity's Last Exam (AA) | Artificial Analysis | 47.9% | 49.5% |
| GPQA Diamond (Vals) | Vals AI | 93.9% | 95.2% |
| EnigmaEval | Scale AI SEAL | – | 37.1%† |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 67.0%† |
| ARC-AGI-1 | ARC Prize | 95.5% | 96.5% |
| ARC-AGI-2 | ARC Prize | 84.6% | 92.5% |
| ARC-AGI-3 | ARC Prize | – | 7.8% |
| CritPt | Artificial Analysis | 14.3% | 32.3% |
| MysteryMechanism | Vals AI | – | 33.3% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 66.0% | 59.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 37.0% | 58.0% |
| LMCA | Epoch AI Benchmarking Hub | 50.4% | 58.4% |
| DTBench | Epoch AI Benchmarking Hub | 96.8% | 95.5% |
| ForecastBench | ForecastBench | 66.2% | 65.5% |
| BTF-3 | Epoch AI Benchmarking Hub | – | 13.7% |
| Coding | |||
| SciCode | SciCode | 57.2% | 57.1% |
| WeirdML | WeirdML | – | 87.0% |
| FrontierCode | Cognition | 43.6%† | 47.5%† |
| GSO-Bench | GSO-Bench | – | 76.5%† |
Show all 140 benchmarks
| Metric | Publisher | Gemini 3.7 Flash (high) | GPT-5.6 Sol (max) |
|---|---|---|---|
| Coding | |||
| Arena Coding | Arena (LMArena) | 1520 | 1534† |
| Arena WebDev | Arena (LMArena) | 1590 | 1615† |
| LiveBench 2.0 Coding | LiveBench | 80.5%† | 89.7%† |
| LiveBench 1.0 Coding | LiveBench | 78.9% | 83.9% |
| LiveCodeBench | Vals AI | 88.7% | 82.6% |
| IOI | Vals AI | 67.8% | 91.2% |
| SWE-bench (Vals) | Vals AI | 80.8% | 96.2% |
| Code Migration | Vals AI | 34.8% | 52.9% |
| ProgramBench | Vals AI | 0.0% | 1.5% |
| Vibe Code Bench 1-100 | Vals AI | – | 20.0% |
| Vibe Code Bench v1.1 | Vals AI | 70.4% | 80.5% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | – | 46.0%† |
| SWE Atlas: Test Writing | Scale AI SEAL | – | 45.9%† |
| MirrorCode | Epoch AI Benchmarking Hub | – | 20.0%† |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | – | 93.1%† |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | 65.3% | 72.7% |
| CursorBench | Cursor | – | 41.7% |
| ALE-Bench | Epoch AI Benchmarking Hub | 904.3 | 2176.9 |
| FrontierSWE | Epoch AI Benchmarking Hub | 20.3% | 32.2% |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 13.6% | 39.9% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 85.8% | 88.0% |
| Startrise Frontend | Startrise | – | 66.2%† |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 11.2% | 37.3% |
| OSWorld-Verified 2.0 | OSWorld | – | 27.3% |
| Remote Labor Index | Scale AI / CAIS | 5.0%† | – |
| APEX-Agents | Mercor | 67.8%† | – |
| Arena Agent | Arena (LMArena) | -1.3 | 6.1† |
| LiveBench Long Agentic | LiveBench | 28.3%† | 48.5%† |
| LiveBench Browser | LiveBench | 56.0%† | 88.0%† |
| LiveBench Agentic Coding | LiveBench | 58.3% | 56.2% |
| Terminal-Bench Hard | Artificial Analysis | – | 65.9% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 85.1% |
| Terminal-Bench 2.1 (Vals) | Vals AI | 77.5% | 85.8% |
| HiL-Bench | Scale AI SEAL | – | 32.3%† |
| τ³-bench Banking | τ²-bench | – | 46.9%† |
| τ³-Banking (AA) | Artificial Analysis | 32.8% | 44.3% |
| ITBench SRE (AA) | Artificial Analysis | – | 56.2% |
| Analyst Agent (AA) | Artificial Analysis | 60.0% | 47.5% |
| CUA-bench | Vals AI | – | 8.3% |
| CyberBench | Vals AI | 43.8% | 76.3% |
| Finance Agent v2 | Vals AI | 59.0% | 53.8% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 8.8% | 2.5% |
| Legal Research Bench | Vals AI | 34.6% | 48.1% |
| SkillsBench | Vals AI | 65.9% | 54.1% |
| SREBench | Vals AI | 4.6% | 30.5% |
| Tax Agent Bench | Vals AI | 22.8% | 31.1% |
| Terminal-Bench 4.0 (Vals) | Vals AI | 12.1% | 37.9% |
| Terminal-Bench Science | Vals AI | 7.1% | 20.0% |
| Time Horizon Index: KSP | Vals AI | – | 23.8% |
| Web Search Index | Vals AI | – | 43.6% |
| Arena Search | Arena (LMArena) | – | 1258† |
| BALROG | BALROG | – | 60.0% |
| PostTrainBench | Epoch AI Benchmarking Hub | – | 36.2% |
| GBAEval | Epoch AI Benchmarking Hub | – | 52.6%† |
| Vending-Bench 2 | Andon Labs | – | 9619.4† |
| AutomationBench | Artificial Analysis | 62.0% | 60.1% |
| GDP.pdf (AA) | Artificial Analysis | 23.6% | 27.2% |
| EnterpriseOps-Gym | Artificial Analysis | 50.4%† | 42.9% |
| AA-Briefcase v1.1 | Artificial Analysis | – | 1479 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | – | 22.4% |
| FACTS Search | 80.9%† | 88.5%† | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 71.6% | 89.1% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 36.6% | 82.9% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 97.2% | 100.0% |
| ProofBench | Vals AI | 58.0%† | 83.0% |
| LiveBench Mathematics | LiveBench | 93.5% | 96.2% |
| Arena Maths | Arena (LMArena) | 1505 | 1496† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 69.2% | 69.7% |
| LiveBench 2.0 Data Analysis | LiveBench | 68.0%† | 79.8%† |
| LiveBench Research | LiveBench | 76.5%† | 54.5%† |
| LiveBench 1.0 Data Analysis | LiveBench | 68.0% | 79.8% |
| AA-Omniscience | Artificial Analysis | 26.5 | 22.0 |
| MMLU-Pro | Vals AI | 90.1% | 89.1% |
| LegalBench | Vals AI | 87.3% | 87.0% |
| CorpFin | Vals AI | – | 64.4% |
| TaxEval | Vals AI | 74.7% | 74.8% |
| PRBench Finance | Scale AI SEAL | – | 50.5% |
| PRBench Legal | Scale AI SEAL | – | 50.5% |
| BioMysteryBench | Vals AI | – | 71.1% |
| Excel Modeling Benchmark | Vals AI | 71.3% | 72.3% |
| MedCode | Vals AI | 53.4% | 44.0% |
| MedScribe | Vals AI | 83.9% | 85.2% |
| Public Benefits Bench v1.1 | Vals AI | – | 66.5% |
| DrugDiscoveryBench | Scale AI SEAL | – | 56.5% |
| EBR-bench | Epoch AI Benchmarking Hub | – | 44.8% |
| GDP.pdf | Epoch AI Benchmarking Hub | 23.8% | 30.7% |
| AA-Omniscience: accuracy | Artificial Analysis | 55.3% | 59.4% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 35.5% | 7.8% |
| Instruction following | |||
| LiveBench Language | LiveBench | 85.5% | 87.7% |
| LiveBench Instruction Following | LiveBench | 79.9% | 71.8% |
| IFBench | Artificial Analysis | – | 72.7% |
| Arena Instruction Following | Arena (LMArena) | 1487 | 1491† |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1490 | 1485† |
| EQ-Bench 4 | EQ-Bench | – | 1240† |
| Arena Creative Writing | Arena (LMArena) | 1492 | 1470† |
| Arena Multi-turn | Arena (LMArena) | 1489 | 1490† |
| Arena Longer Queries | Arena (LMArena) | 1501 | 1499† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1314 | 1281† |
| LiveBench Design | LiveBench | 73.2%† | 81.6%† |
| MMMU-Pro | Artificial Analysis | 85.5% | 83.4% |
| MMMU-Pro (Vals) | Vals AI | 89.0% | 88.8% |
| MortgageTax | Vals AI | 66.7% | 67.3% |
| SAGE | Vals AI | 49.2% | 52.6% |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | – | 33.6%† |
| Long context | |||
| AA-LCR | Artificial Analysis | 81.7% | 84.0% |
| Arena Document | Arena (LMArena) | – | 1489† |
| MLCR | Artificial Analysis | 12.2% | 30.0% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 158.0† | 162.8† |
| LiveBench 2.0 | LiveBench | 67.2%† | 76.3%† |
| LiveBench 1.0 | LiveBench | 78.8% | 81.0% |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 39.1 | 47.0 |
| Vals RSI Index | Vals AI | 18.3% | 23.9% |
| Vals Science Index | Vals AI | – | 41.5% |
| Vals Multimodal Index | Vals AI | – | 72.6% |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 20.1%† |
Data as of 2026-10-25. Best configuration of each model; every score links to its source on the model pages.
What changed
- GPT-5.6 Sol (max): 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-5.6 Sol (max): 3 new results (Harvey's Legal Agent Benchmark v1, Vals RSI Index, Vals Science 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-5.6 Sol (max): 1 score revision.
- Gemini 3.7 Flash (high): 1 new result (ForecastBench).
- GPT-5.6 Sol (max): 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.
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-5.6 Sol vs Claude Opus 5.5
- GPT-5.6 Sol vs Gemini 4 Argon
- GPT-5.6 Sol vs Muse Wonder Full
- GPT-5.6 Sol vs Claude Fable 5.1
- Gemini 3.6 Flash vs GPT-5.6 Sol
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
Gemini 3.7 Flash vs GPT-5.6 Sol: questions
- Is Gemini 3.7 Flash better than GPT-5.6 Sol?
- Gemini 3.7 Flash (high) and GPT-5.6 Sol (max) are neck and neck on quality (63.5 vs 68.0, 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-5.6 Sol, and will that change?
- Gemini 3.7 Flash is cheaper today: $1.50 against $8.00 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-5.6 Sol for coding?
- GPT-5.6 Sol scores higher in coding (66 vs 63 on the category index, where 50 is average). On LiveCodeBench, the top shared coding benchmark, it is 88.7% for Gemini 3.7 Flash against 82.6% for GPT-5.6 Sol.
- Is Gemini 3.7 Flash better than GPT-5.6 Sol for agents and tool use?
- GPT-5.6 Sol scores higher in agentic tasks (67 vs 48 on the category index, where 50 is average). On Terminal-Bench, the top shared agents & tools benchmark, it is 11.2% for Gemini 3.7 Flash against 37.3% for GPT-5.6 Sol.
- Which is better for long documents?
- GPT-5.6 Sol scores higher on long-context benchmarks (67 vs 66). Both take about 1.1M tokens: Gemini 3.7 Flash 1,048,576, GPT-5.6 Sol 1,050,000.
- Which has lower latency?
- Gemini 3.7 Flash (high) answers first: 2.5 s against 69.5 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-5.6 Sol?
- GPT-5.6 Sol streams faster: 89 against 88 output tokens per second.
- Do independent benchmarks agree on Gemini 3.7 Flash vs GPT-5.6 Sol?
- Across the 68 benchmarks both have, Gemini 3.7 Flash leads 21, GPT-5.6 Sol leads 47. Independent sources split: Gemini 3.7 Flash leads on Arena Text; GPT-5.6 Sol leads on AA Intelligence Index v4.3.2.
- When was Gemini 3.7 Flash released?
- Gemini 3.7 Flash was released on 13 Aug 2026.