Gemini 4 Carbon vs GPT-6.1 Terra
Full profiles: Gemini 4 Carbon (Google) and GPT-6.1 Terra (OpenAI): every benchmark, price and rank.
Updated · Data from 6 sources
As of 29 Oct 2026, Gemini 4 Carbon and GPT-6.1 Terra are neck and neck on quality (65.1 vs 64.8 on the BenchLeader Index, inside the ±6.5 margin), and GPT-6.1 Terra is 4.0× cheaper at $2.00 vs $8.00 per 1M tokens blended. Choose Gemini 4 Carbon for knowledge (76 vs 62), reasoning (79 vs 69), and coding (72 vs 69) and fast first answers (103.1 s vs 189.3 s); choose GPT-6.1 Terra for long context (66 vs 65).
Key facts (as of 29 Oct 2026)
- Gemini 4 Carbon scores 65.1 on the BenchLeader Index and GPT-6.1 Terra scores 64.8 (BenchLeader, 29 Oct 2026).
- Gemini 4 Carbon costs $8.00 per 1M tokens blended (list price, 29 Oct 2026).
- GPT-6.1 Terra costs $2.00 per 1M tokens blended (list price, 29 Oct 2026).
- Of the 25 benchmarks both models have, Gemini 4 Carbon leads 21 (BenchLeader, 29 Oct 2026).
Gemini 4 Carbon benchmarks high Google
GPT-6.1 Terra 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 | Gemini 4 Carbon | 72 vs 69 category score; SciCode 64.2% vs 55.1% | Medium |
| Reasoning | Gemini 4 Carbon | 79 vs 69 category score; HLE-Diamond (AA) 69.1% vs 47.3% | High |
| Knowledge | Gemini 4 Carbon | 76 vs 62 category score; AA-Omniscience v1.1 53.5 vs 29.6 | High |
| Long documents | GPT-6.1 Terra | 66 vs 65 category score; AA-LCR v1.1 82.7% vs 79.3% | Medium |
| Lowest cost | GPT-6.1 Terra | $2.00 vs $8.00 per 1M blended; agentic coding session $0.080 vs $0.320 | – |
| Fastest first answer | Gemini 4 Carbon | 103.1 s (Gemini 4 Carbon (high)) vs 189.3 s (GPT-6.1 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 4 Carbon (high) and GPT-6.1 Terra (max) are neck and neck on quality (65.1 vs 64.8, within the ±6.5 margin).
- Gemini 4 Carbon (high) is stronger in coding, knowledge, reasoning.
- GPT-6.1 Terra (max) is stronger in long context, agents & tools, maths, multimodal.
- GPT-6.1 Terra (max) is 4.0× cheaper ($2.00 vs $8.00 per 1M blended).
At a glance
| Spec | Gemini 4 Carbon | GPT-6.1 Terra |
|---|---|---|
| Developer | OpenAI | |
| Released | 28 Oct 2026 | 26 Oct 2026 |
| Context window (tokens) | 1,000,000 | 1,050,000 |
| Compared at | Gemini 4 Carbon (high) | GPT-6.1 Terra (max) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $4.00 / $20.00 / – | $1.00 / $5.00 / $0.050 |
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 4 Carbon (high) | GPT-6.1 Terra (max) | Leader |
|---|---|---|---|
| Reasoning | 78.9 | 69.3 | Gemini 4 Carbon |
| Coding | 71.7 | 69.4 | Gemini 4 Carbon |
| Agents & tools | – | 68.5 | not comparable |
| Maths | – | 72.7 | not comparable |
| Knowledge | 75.8 | 62.3 | Gemini 4 Carbon |
| Multimodal | – | 67.5 | not comparable |
| Long context | 64.5 | 66.2 | GPT-6.1 Terra |
Where sources disagree
Across the 25 benchmarks both have, Gemini 4 Carbon leads 21, GPT-6.1 Terra leads 4. Every overall index that rates both puts Gemini 4 Carbon ahead.
| Overall index | Gemini 4 Carbon (high) | GPT-6.1 Terra (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 65.1 | 64.8 | neck and neck |
| AA Intelligence Index v5.0 | 59.4 | 51.0 | Gemini 4 Carbon |
| Vals Index | 70.4 | 60.5 | Gemini 4 Carbon |
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 4 Carbon (high) | GPT-6.1 Terra (max) |
|---|---|---|
| Blended price per 1M tokens | $8.00 | $2.00 |
| Input / output per 1M | $4.00 / $20.00 | $1.00 / $5.00 |
| Cached input per 1M | – | $0.050 |
| Price per task: chat reply | $0.0076 | $0.0019 |
| Price per task: summarize a 30-page report | $0.060 | $0.015 |
| Price per task: code edit | $0.054 | $0.013 |
| Price per task: agentic coding session | $0.320 | $0.080 |
| Price per task: structured extraction | $0.012 | $0.0030 |
| Cost to run a full benchmark suite | $7.76 | $0.961 |
| Output speed | 100 tok/s | 91 tok/s |
| Time to first answer | 103.1 s | 189.3 s |
| Time to a complete answer | 108.1 s | 194.8 s |
Per token is not per task: on Artificial Analysis' full suite Gemini 4 Carbon (high) cost $7.76 and GPT-6.1 Terra (max) $0.961, because Gemini 4 Carbon (high) used about 2.0× as many tokens for the same work, which a per-token price hides.
Each model is compared at its best-ranked reasoning-effort setting. Gemini 4 Carbon (high) scores 65.1 and takes 103 s to its first answer token. GPT-6.1 Terra (max) scores 64.8 and takes 189 s to its first answer token; GPT-6.1 Terra (xhigh) scores 60.2 in 26 s, GPT-6.1 Terra (high) scores 59.9 in 6 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
53 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 4 Carbon (high) | GPT-6.1 Terra (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 65.1 | 64.8 |
| Coding score | BenchLeader | 71.7 | 69.4 |
| Knowledge score | BenchLeader | 75.8 | 62.3 |
| Long context score | BenchLeader | 64.5 | 66.2 |
| Reasoning score | BenchLeader | 78.9 | 69.3 |
| Agents & tools score | BenchLeader | – | 68.5 |
| Maths score | BenchLeader | – | 72.7 |
| Multimodal score | BenchLeader | – | 67.5 |
| Blended price $/M | BenchLeader | $8.00 | $2.00 |
| Output speed | BenchLeader | 100 tok/s | 91 tok/s |
| Time to first answer | BenchLeader | 103.1 s | 189.3 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | – | 94.3% |
| LiveBench 2.0 Reasoning | LiveBench | – | 88.7%† |
| Humanity's Last Exam (AA) | Artificial Analysis | 60.4% | 46.3% |
| HLE-Diamond (AA) | Artificial Analysis | 69.1% | 47.3% |
| CritPt | Artificial Analysis | 36.0% | 28.0% |
| CritPt v2 hard subset (AA) | Artificial Analysis | 63.0% | 64.5% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | – | 74.0% |
| Coding | |||
| SciCode | SciCode | 64.2% | 55.1% |
| Arena WebDev | Arena (LMArena) | – | 1671 |
| LiveBench 2.0 Coding | LiveBench | – | 94.3%† |
| Vibe Code Bench v1.1 | Vals AI | 89.1% | 84.0% |
| ALE-Bench | Epoch AI Benchmarking Hub | – | 2608.8 |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 65.2% | 55.6% |
| Startrise Frontend | Startrise | – | 82.0%† |
| FrontierCode 1.1 (AA) | Artificial Analysis | 48.5% | 36.0% |
| FrontierCode 2.0 (AA) | Artificial Analysis | 40.6% | 42.3% |
| Agents & tools | |||
| APEX-Agents | Mercor | – | 68.3% |
| LiveBench Long Agentic | LiveBench | – | 48.6%† |
| LiveBench Browser | LiveBench | – | 93.1%† |
| Finance Agent v2.1 | Vals AI | 67.2% | 56.0% |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 17.5% | 1.7% |
| Legal Research Bench | Vals AI | 55.8% | 26.0% |
| Tax Agent Bench | Vals AI | 48.9% | 15.2% |
| Terminal-Bench 4.0 (Vals) | Vals AI | 63.1% | 49.5% |
| Vending-Bench 2 | Andon Labs | 16484.2† | – |
Show all 53 benchmarks
| Metric | Publisher | Gemini 4 Carbon (high) | GPT-6.1 Terra (max) |
|---|---|---|---|
| Agents & tools | |||
| AutomationBench | Artificial Analysis | 77.8% | 60.2% |
| GDP.pdf (AA) | Artificial Analysis | 28.4% | 28.6% |
| AA-Briefcase v1.1 | Artificial Analysis | 1609 | 1402 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 64.8% | 57.6% |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 90.9% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 92.7% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | – | 100.0% |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | – | 57.3% |
| LiveBench 2.0 Data Analysis | LiveBench | – | 82.3%† |
| LiveBench Research | LiveBench | – | 73.9%† |
| AA-Omniscience v1.1 | Artificial Analysis | 53.5 | 29.6 |
| Excel Modeling Benchmark v1.1 | Vals AI | 76.5% | 73.3% |
| EBR-bench | Epoch AI Benchmarking Hub | – | 78.1% |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 58.3% | 47.6% |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 88.6% | 65.5% |
| Instruction following | |||
| MultiChallenge | Scale AI SEAL | – | 74.2%† |
| Multimodal | |||
| LiveBench Design | LiveBench | – | 82.3%† |
| MMMU-Pro | Artificial Analysis | – | 84.7% |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 58.6%† | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 79.3% | 82.7% |
| MLCR | Artificial Analysis | – | 30.6% |
| Composite | |||
| LiveBench 2.0 | LiveBench | – | 77.8%† |
| AA Intelligence Index v5.0 | Artificial Analysis | 59.4 | 51.0 |
| AA Intelligence Index v4.3.2 | Artificial Analysis | – | 48.1 |
| AA Progress Index v1 | Artificial Analysis | 351.6 | 345.2 |
| Vals Index | Vals AI | 70.4 | 60.5 |
| Vals Finance Index | Vals AI | 66.3% | 52.9% |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 18.2%† |
Data as of 2026-10-29. Best configuration of each model; every score links to its source on the model pages.
What changed
- GPT-6.1 Terra (max): 3 new results (SciCode, APEX-Agents, ALE-Bench).
- GPT-6.1 Terra (max): BenchLeader Index 63.5 → 64.7.
- GPT-6.1 Terra (max): 7 new results (AA Intelligence Index v5.0, AA Progress Index v1, HLE-Diamond (AA)…); 2 score revisions; cost per task moved from $0.40/M to $0.96/M (138% up).
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- GPT-6.1 Terra vs GPT-6.1 Sol
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- Gemini 4 Argon vs GPT-6.1 Terra
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
Gemini 4 Carbon vs GPT-6.1 Terra: questions
- Is Gemini 4 Carbon better than GPT-6.1 Terra?
- Gemini 4 Carbon (high) and GPT-6.1 Terra (max) are neck and neck on quality (65.1 vs 64.8, within the ±6.5 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 29 Oct 2026, so check the category scores for your use.
- Gemini 4 Carbon vs GPT-6.1 Terra: which is cheaper, and will that change?
- GPT-6.1 Terra is cheaper today: $2.00 against $8.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Gemini 4 Carbon or GPT-6.1 Terra better for coding?
- Gemini 4 Carbon scores higher in coding (72 vs 69 on the category index, where 50 is average). On FrontierCode 2.0 (AA), the top shared coding benchmark, it is 40.6% for Gemini 4 Carbon against 42.3% for GPT-6.1 Terra.
- Which is faster, Gemini 4 Carbon or GPT-6.1 Terra?
- Gemini 4 Carbon streams faster (100 vs 91 output tokens per second, Artificial Analysis 7-day median).
- Which is better for long documents?
- GPT-6.1 Terra scores higher on long-context benchmarks (66 vs 65). Both take about 1.1M tokens: Gemini 4 Carbon 1,000,000, GPT-6.1 Terra 1,050,000.
More questions (3)
- Which has lower latency?
- Gemini 4 Carbon answers first: 103.1 s against 189.3 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 4 Carbon vs GPT-6.1 Terra?
- Across the 25 benchmarks both have, Gemini 4 Carbon leads 21, GPT-6.1 Terra leads 4. Every overall index that rates both puts Gemini 4 Carbon ahead.
- When was Gemini 4 Carbon released?
- Gemini 4 Carbon was released on 28 Oct 2026.