Gemini 3.6 Flash vs GPT-5.6 Terra
Full profiles: Gemini 3.6 Flash (Google) and GPT-5.6 Terra (OpenAI): every benchmark, price and rank.
Updated · Data from 11 sources
As of 2 Nov 2026, Gemini 3.6 Flash and GPT-5.6 Terra are neck and neck on quality (61.3 vs 61.1 on the BenchLeader Index, inside the ±5.8 margin), and Gemini 3.6 Flash is 3.0× cheaper at $1.50 vs $4.50 per 1M tokens blended. Choose Gemini 3.6 Flash for knowledge (64 vs 49), reasoning (64 vs 61), and multimodal (65 vs 63) and fast first answers (16.4 s vs 148.7 s), where it also streams ~1.5× faster; choose GPT-5.6 Terra for maths (69 vs 59), coding (65 vs 57), and long context (66 vs 65). Gemini 3.6 Flash's current price runs until 31 Dec 2026, then $3.00.
Key facts (as of 2 Nov 2026)
- Gemini 3.6 Flash scores 61.3 on the BenchLeader Index and GPT-5.6 Terra scores 61.1 (BenchLeader, 2 Nov 2026).
- Gemini 3.6 Flash costs $1.50 per 1M tokens blended until 31 Dec 2026, then $3.00 (https://ai.google.dev/gemini-api/docs/pricing, checked 20 Oct 2026).
- GPT-5.6 Terra costs $4.50 per 1M tokens blended (list price, 2 Nov 2026).
- Gemini 3.6 Flash streams ~1.5× faster than GPT-5.6 Terra (Artificial Analysis, 7-day median, 2 Nov 2026).
- Of the 50 benchmarks both models have, GPT-5.6 Terra leads 39 (BenchLeader, 2 Nov 2026).
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 | GPT-5.6 Terra | 65 vs 57 category score; SciCode 55.0% vs 53.4% | High |
| Agents & tool use | Either | 59 vs 59 category score; τ³-Banking (AA) 29.9% vs 40.2% | Either |
| Maths | GPT-5.6 Terra | 69 vs 59 category score; FrontierMath Tiers 1–3 86.0% vs 59.0% | High |
| Reasoning | Gemini 3.6 Flash | 64 vs 61 category score; GPQA Diamond 94.1% vs 93.3% | Medium |
| Knowledge | Gemini 3.6 Flash | 64 vs 49 category score; SimpleQA Verified 66.2% vs 43.2% | High |
| Long documents | GPT-5.6 Terra | 66 vs 65 category score; AA-LCR v1.1 83.0% vs 80.0% | Medium |
| Images and other media | Gemini 3.6 Flash | 65 vs 63 category score; MMMU-Pro 83.2% vs 80.7% | Medium |
| Chat quality (human preference) | – | Only Gemini 3.6 Flash has an Arena rating (67 category score) | Not comparable |
| Lowest cost | Gemini 3.6 Flash | $1.50 vs $4.50 per 1M blended; agentic coding session $0.060 vs $0.168; from 1 Jan 2027: $3.00 vs $4.50 | – |
| Fastest first answer | Gemini 3.6 Flash | 16.4 s (Gemini 3.6 Flash (high)) vs 148.7 s (GPT-5.6 Terra (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 3.6 Flash (high) and GPT-5.6 Terra (max) are neck and neck on quality (61.3 vs 61.1, within the ±5.8 margin).
- Gemini 3.6 Flash (high) is stronger in agents & tools, human preference, knowledge, multimodal, reasoning.
- GPT-5.6 Terra (max) is stronger in coding, long context, maths, instruction following.
- Gemini 3.6 Flash (high) is 3.0× cheaper ($1.50 vs $4.50 per 1M blended) until 31 Dec 2026, then $3.00 vs $4.50 from 1 Jan 2027.
- Gemini 3.6 Flash (high) streams 1.5× faster (197 vs 130 tokens per second).
At a glance
| Spec | Gemini 3.6 Flash | GPT-5.6 Terra |
|---|---|---|
| Developer | OpenAI | |
| Released | 21 Jul 2026 | 9 Jul 2026 |
| Context window (tokens) | 1,048,576 | 1,050,000 |
| Compared at | Gemini 3.6 Flash (high) | GPT-5.6 Terra (max) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $0.750 / $3.75 / $0.075 | $2.00 / $12.00 / $0.200 |
| Announced price change | $1.50 / $7.50 / $0.150 from 1 Jan 2027 | None announced |
Limits, availability and announced prices from the vendors' own pages: ai.google.dev pricing (checked 20 Oct 2026). Everything else is from the day's data.
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 3.6 Flash (high) | GPT-5.6 Terra (max) | Leader |
|---|---|---|---|
| Reasoning | 64.0 | 61.4 | Gemini 3.6 Flash |
| Coding | 56.9 | 64.9 | GPT-5.6 Terra |
| Agents & tools | 59.3 | 58.8 | level |
| Maths | 59.4 | 68.6 | GPT-5.6 Terra |
| Knowledge | 63.8 | 48.6 | Gemini 3.6 Flash |
| Instruction following | – | 69.9 | not comparable |
| Human preference | 67.4 | – | not comparable |
| Multimodal | 65.2 | 63.3 | Gemini 3.6 Flash |
| Long context | 64.8 | 66.4 | GPT-5.6 Terra |
On one chart
Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.
Not drawn, measured for fewer than two of these models: Instruction following, Human preference.
Where sources disagree
Across the 50 benchmarks both have, Gemini 3.6 Flash leads 11, GPT-5.6 Terra leads 39. Every overall index that rates both puts Gemini 3.6 Flash ahead.
| Overall index | Gemini 3.6 Flash (high) | GPT-5.6 Terra (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 61.3 | 61.1 | neck and neck |
| Arena Text | 1483 | 1467† | Gemini 3.6 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. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.
| Measure | Gemini 3.6 Flash (high) | GPT-5.6 Terra (max) |
|---|---|---|
| Blended price per 1M tokens, until 31 Dec 2026 | $1.50 | $4.50 |
| Input / output per 1M | $0.750 / $3.75 | $2.00 / $12.00 |
| Blended price per 1M from 1 Jan 2027 | $3.00 | $4.50 |
| Input / output per 1M from 1 Jan 2027 | $1.50 / $7.50 | $2.00 / $12.00 |
| Cached input per 1M | $0.075 | $0.200 |
| Price per task: chat reply | $0.0014 | $0.0044 |
| Price per task: summarize a 30-page report | $0.011 | $0.031 |
| Price per task: code edit | $0.010 | $0.030 |
| Price per task: agentic coding session | $0.060 | $0.168 |
| Price per task: structured extraction | $0.0023 | $0.0064 |
| Agentic coding session, input mostly cached | $0.030 | $0.087 |
| Output speed | 197 tok/s | 130 tok/s |
| Time to first answer | 16.4 s | 148.7 s |
| Time to a complete answer | 18.9 s | 152.5 s |
Each model is compared at its best-ranked reasoning-effort setting. Gemini 3.6 Flash (high) scores 61.3 and takes 16 s to its first answer token; Gemini 3.6 Flash scores 49.5 in 1 s. GPT-5.6 Terra (max) scores 61.1 and takes 149 s to its first answer token; GPT-5.6 Terra (xhigh) scores 60.3 in 22 s, GPT-5.6 Terra (high) scores 57.0 in 5 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
122 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 3.6 Flash (high) | GPT-5.6 Terra (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 61.3 | 61.1 |
| Agents & tools score | BenchLeader | 59.3 | 58.8 |
| Coding score | BenchLeader | 56.9 | 64.9 |
| Human preference score | BenchLeader | 67.4 | – |
| Knowledge score | BenchLeader | 63.8 | 48.6 |
| Long context score | BenchLeader | 64.8 | 66.4 |
| Maths score | BenchLeader | 59.4 | 68.6 |
| Multimodal score | BenchLeader | 65.2 | 63.3 |
| Reasoning score | BenchLeader | 64.0 | 61.4 |
| Instruction following score | BenchLeader | – | 69.9 |
| Blended price $/M | BenchLeader | $1.50 | $4.50 |
| Output speed | BenchLeader | 197 tok/s | 130 tok/s |
| Time to first answer | BenchLeader | 16.4 s | 148.7 s |
| Context window | BenchLeader | 1,048,576 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 94.1% | 93.3% |
| SimpleBench | SimpleBench | – | 48.9%† |
| Arena Hard Prompts | Arena (LMArena) | 1501 | 1490† |
| LiveBench 2.0 Reasoning | LiveBench | – | 90.6%† |
| LiveBench 1.0 Reasoning | LiveBench | 85.2% | 90.6% |
| GPQA Diamond (AA) | Artificial Analysis | 92.8% | 92.5% |
| Humanity's Last Exam (AA) | Artificial Analysis | 40.8% | 42.9% |
| HLE-Diamond (AA) | Artificial Analysis | – | 28.8% |
| GPQA Diamond (Vals) | Vals AI | 93.4% | 90.9%† |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 51.3%† |
| ARC-AGI-1 | ARC Prize | 91.2% | 96.5% |
| ARC-AGI-2 | ARC Prize | 60.4% | 83.9% |
| ARC-AGI-3 | ARC Prize | – | 0.8% |
| CritPt | Artificial Analysis | 10.6% | 30.0% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 40.0% | 47.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 30.0% | 35.0% |
| LMCA | Epoch AI Benchmarking Hub | 44.9% | 55.0% |
| DTBench | Epoch AI Benchmarking Hub | 95.5% | 93.3% |
| ForecastBench | ForecastBench | 65.9%† | – |
| Coding | |||
| SciCode | SciCode | 53.4% | 55.0% |
| WeirdML | WeirdML | 56.1% | 78.3%† |
| FrontierCode | Cognition | 34.4%† | 41.3%† |
| Arena Coding | Arena (LMArena) | 1518 | 1517† |
| Arena WebDev | Arena (LMArena) | 1538 | 1520† |
| LiveBench 2.0 Coding | LiveBench | – | 86.2%† |
Show all 122 benchmarks
| Metric | Publisher | Gemini 3.6 Flash (high) | GPT-5.6 Terra (max) |
|---|---|---|---|
| Coding | |||
| LiveBench 1.0 Coding | LiveBench | 77.9% | 78.3% |
| LiveCodeBench | Vals AI | 88.1% | 85.9%† |
| IOI | Vals AI | 35.1% | 87.6% |
| SWE-bench (Vals) | Vals AI | 79.6% | 95.4% |
| Code Migration | Vals AI | 30.9% | 47.8% |
| ProgramBench | Vals AI | 0.0% | 0.5% |
| Vibe Code Bench 1-100 | Vals AI | – | 14.8% |
| Vibe Code Bench v1.1 | Vals AI | 64.0% | 74.6% |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | – | 83.8%† |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | 46.7% | 69.6% |
| CursorBench | Cursor | – | 41.3% |
| ALE-Bench | Epoch AI Benchmarking Hub | 715.5 | 1951.4 |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 7.1% | 35.4% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 77.5% | 88.0% |
| Startrise Frontend | Startrise | – | 66.9%† |
| GameBench 2 Overall | DuelLab GameBench 2 | 52.5† | 47.2† |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | 64.6 | 54.8† |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | – | 21.5% |
| APEX-Agents | Mercor | 46.9%† | 58.2% |
| Arena Agent | Arena (LMArena) | – | -1.4† |
| LiveBench Long Agentic | LiveBench | – | 33.1%† |
| LiveBench Browser | LiveBench | – | 70.3%† |
| LiveBench Agentic Coding | LiveBench | 43.4% | 55.0% |
| Terminal-Bench Hard | Artificial Analysis | – | 57.6% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 86.3% |
| Terminal-Bench 2.1 (Vals) | Vals AI | 73.8% | 77.5% |
| GDPval-AA v2.1, normalised score | Artificial Analysis | – | 47.6% |
| τ³-Banking (AA) | Artificial Analysis | 29.9% | 40.2% |
| ITBench SRE (AA) | Artificial Analysis | – | 51.0% |
| APEX-Agents (AA) | Artificial Analysis | – | 38.9% |
| CyberBench | Vals AI | 45.4% | 72.1% |
| Finance Agent v2 | Vals AI | 56.3% | 54.4% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 3.3% | 0.8% |
| Legal Research Bench | Vals AI | 25.0% | 41.4% |
| SkillsBench | Vals AI | – | 58.9% |
| SREBench | Vals AI | – | 9.5% |
| Tax Agent Bench | Vals AI | 18.0% | 30.0% |
| Terminal-Bench 4.0 (Vals) | Vals AI | – | 22.7% |
| Terminal-Bench Science | Vals AI | 4.3% | 10.0% |
| BALROG | BALROG | – | 53.2% |
| Vending-Bench 2 | Andon Labs | – | 7343.2† |
| AutomationBench | Artificial Analysis | 53.0% | 59.6% |
| GDP.pdf (AA) | Artificial Analysis | 17.4% | 24.0% |
| EnterpriseOps-Gym | Artificial Analysis | – | 38.5% |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | – | 13.8% |
| FACTS Search | 68.2%† | 81.7%† | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 59.0% | 86.0% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 21.9% | 70.7% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 94.2% | 99.7% |
| ProofBench | Vals AI | 36.0%† | 74.0%† |
| LiveBench Mathematics | LiveBench | 86.4% | 94.9% |
| AIME 2026 | MathArena | 96.7%† | – |
| HMMT February 2026 | MathArena | 89.4%† | – |
| MathArena Apex | MathArena | 26.0%† | – |
| Arena Maths | Arena (LMArena) | 1504 | 1480† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 66.2% | 43.2% |
| LiveBench 2.0 Data Analysis | LiveBench | – | 79.3%† |
| LiveBench Research | LiveBench | – | 60.4%† |
| LiveBench 1.0 Data Analysis | LiveBench | 63.0% | 79.3% |
| AA-Omniscience v1.1 | Artificial Analysis | – | -9.3 |
| MMLU-Pro | Vals AI | 89.3% | 86.7%† |
| LegalBench | Vals AI | 86.7% | 85.1% |
| CorpFin | Vals AI | 63.3% | 65.3%† |
| TaxEval | Vals AI | 74.9% | 76.2%† |
| BioMysteryBench | Vals AI | 58.5% | – |
| Excel Modeling Benchmark | Vals AI | 65.4% | 66.2% |
| MedCode | Vals AI | 53.1% | 43.4%† |
| MedScribe | Vals AI | 79.7% | 82.9%† |
| Public Benefits Bench v1.1 | Vals AI | 56.6% | 62.4% |
| GDP.pdf | Epoch AI Benchmarking Hub | 14.0% | 24.7%† |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | – | 42.3% |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | – | 10.6% |
| Instruction following | |||
| LiveBench Language | LiveBench | 83.9% | 82.9% |
| LiveBench Instruction Following | LiveBench | 75.4% | 64.6% |
| IFBench | Artificial Analysis | – | 71.2% |
| Arena Instruction Following | Arena (LMArena) | 1473 | 1463† |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1483 | 1467† |
| EQ-Bench 4 | EQ-Bench | – | 1220† |
| Arena Creative Writing | Arena (LMArena) | 1471 | 1429† |
| Arena Multi-turn | Arena (LMArena) | 1488 | 1474† |
| Arena Longer Queries | Arena (LMArena) | 1485 | 1468† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1299 | 1271† |
| LiveBench Design | LiveBench | – | 76.0%† |
| MMMU-Pro | Artificial Analysis | 83.2% | 80.7% |
| MMMU-Pro (Vals) | Vals AI | 88.4% | 86.5% |
| MortgageTax | Vals AI | 67.6% | 67.3%† |
| SAGE | Vals AI | – | 87.0% |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 31.2%† | 30.8%† |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 80.0% | 83.0% |
| Arena Document | Arena (LMArena) | 1453 | 1477† |
| MLCR | Artificial Analysis | 10.0% | 27.2% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 154.7† | 160.1† |
| LiveBench 2.0 | LiveBench | – | 67.8%† |
| LiveBench 1.0 | LiveBench | 73.6% | 77.9% |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 34.0 | 42.1 |
| AA Progress Index v1 | Artificial Analysis | 305.7 | 323.9 |
| Vals Multimodal Index | Vals AI | 65.1% | 65.1%† |
Data as of 2026-11-02. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261026-cb69502a12d9 (https://benchmarks.duellab.org/releases/gb2-20261026-cb69502a12d9/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- Gemini 3.6 Flash: list price moves to $1.50 / $7.50 per 1M input / output tokens (announced).
- GPT-5.6 Terra (max): 1 new result (MirrorCode).
- Gemini 3.6 Flash (high): BenchLeader Index 61.8 → 61.2.
- GPT-5.6 Terra (max): BenchLeader Index 62.5 → 61.2.
- Gemini 3.6 Flash (high): 1 new result (AA Progress Index v1).
- GPT-5.6 Terra (max): 2 new results (AA Progress Index v1, HLE-Diamond (AA)); 4 score revisions.
- Gemini 3.6 Flash (high): 1 new result (Harvey's Legal Agent Benchmark v1).
- GPT-5.6 Terra (max): 1 new result (Harvey's Legal Agent Benchmark v1).
- Gemini 3.6 Flash (high): 1 score revision.
- GPT-5.6 Terra (max): 1 score revision.
Related comparisons
- Gemini 3.6 Flash vs Claude Opus 5.5
- Gemini 3.6 Flash vs Gemini 4 Carbon
- Gemini 3.6 Flash vs GPT-6 Astra
- GPT-5.6 Terra vs Claude Opus 5.5
- GPT-5.6 Terra vs Gemini 4 Carbon
- GPT-5.6 Terra vs GPT-6 Astra
- GPT-5.6 Terra vs GPT-6.1 Sol
- Gemini 3.6 Flash vs Claude Fable 5.1
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
Gemini 3.6 Flash vs GPT-5.6 Terra: questions
- Is Gemini 3.6 Flash better than GPT-5.6 Terra?
- Gemini 3.6 Flash (high) and GPT-5.6 Terra (max) are neck and neck on quality (61.3 vs 61.1, within the ±5.8 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2 Nov 2026, so check the category scores for your use.
- Gemini 3.6 Flash vs GPT-5.6 Terra: which is cheaper, and will that change?
- Gemini 3.6 Flash is cheaper today: $1.50 against $4.50 per million tokens, blended at three input tokens per output token. Gemini 3.6 Flash's current price runs until 31 Dec 2026, then $3.00.
- Is Gemini 3.6 Flash or GPT-5.6 Terra better for coding?
- GPT-5.6 Terra scores higher in coding (65 vs 57 on the category index, where 50 is average). On SciCode, the top shared coding benchmark, it is 53.4% for Gemini 3.6 Flash against 55.0% for GPT-5.6 Terra.
- Which is faster, Gemini 3.6 Flash or GPT-5.6 Terra?
- Gemini 3.6 Flash streams ~1.5× faster (197 vs 130 output tokens per second, Artificial Analysis 7-day median).
- Is Gemini 3.6 Flash or GPT-5.6 Terra better for agents and tool use?
- They are level in agentic tasks (59 vs 59 on the category index). On τ³-Banking (AA), the top shared agents & tools benchmark, it is 29.9% for Gemini 3.6 Flash against 40.2% for GPT-5.6 Terra.
More questions (4)
- Which is better for long documents?
- GPT-5.6 Terra scores higher on long-context benchmarks (66 vs 65). Both take about 1.1M tokens: Gemini 3.6 Flash 1,048,576, GPT-5.6 Terra 1,050,000.
- Which has lower latency?
- Gemini 3.6 Flash answers first: 16.4 s against 148.7 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.
- Do independent benchmarks agree on Gemini 3.6 Flash vs GPT-5.6 Terra?
- Across the 50 benchmarks both have, Gemini 3.6 Flash leads 11, GPT-5.6 Terra leads 39. Every overall index that rates both puts Gemini 3.6 Flash ahead.
- When was Gemini 3.6 Flash released?
- Gemini 3.6 Flash was released on 21 Jul 2026.