OpenAI, proprietary

# GPT-5.6 Terra

> GPT-5.6 Terra by OpenAI, released July 2026. Ranked #17 of 354 with a Noometry Index of 59.2. API: $2 in / $12 out per M tokens. 1.05M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-5-6-terra
- Last updated: 2026-10-10
- Title: GPT-5.6 Terra Benchmarks, Price & Rank (October 2026)

GPT-5.6 Terra by OpenAI ranks 17th of 354 ranked models on the Noometry Index as of October 2026, with a score of 59.2. Its strongest category is multimodal, where it ranks 11th. API pricing starts at $2 per million input tokens and $12 per million output tokens, with a 1.05M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #17 of 354
- **Index score:** 59.2
- **Evidence:** Confirmed 52 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** July 9, 2026
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 1.05M
- **Max output:** 128K
- **Input price:** $2 / M
- **Output price:** $12 / M
- **Blended price:** $4.50 / M
- **Output speed:** 11 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #170 of 219
- **Knowledge cutoff:** February 2026
- **Input:** text, image, pdf

## Category scores

Each category score combines every public result we have in that category.

GPT-5.6 Terra category scores

1.  Coding 57.7
2.  Agentic & Tool Use 40.1
3.  Reasoning 60.7
4.  Math 81.6
5.  Knowledge 61.2
6.  Multimodal 47.3
7.  Multilingual 54.4
8.  Instruction Following 76.4
9.  Long Context 44.4
10.  Writing & Preference 70.2
11.  050100

GPT-5.6 Terra category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 57.7 | #19 | 7 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 40.1 | #25 | 3 |
| [Reasoning](https://noometry.com/best/reasoning) | 60.7 | #21 | 12 |
| [Math](https://noometry.com/best/math) | 81.6 | #12 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 61.2 | #30 | 3 |
| [Multimodal](https://noometry.com/best/multimodal) | 47.3 | #11 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 54.4 | #44 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 76.4 | #40 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 44.4 | #68 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 70.2 | #23 | 5 |

## Strengths and weaknesses

Categories where GPT-5.6 Terra places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-5.6 Terra: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Math](https://noometry.com/best/math) | 81.6 | +45.0 | #12 of 327, top 4% |
| [Coding](https://noometry.com/best/coding) | 57.7 | +19.0 | #19 of 340, top 6% |
| [Reasoning](https://noometry.com/best/reasoning) | 60.7 | +37.1 | #21 of 350, top 6% |

### Weakest categories

GPT-5.6 Terra: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 44.4 | +3.5 | #68 of 296, top 23% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 40.1 | +9.8 | #25 of 154, top 17% |
| [Multilingual](https://noometry.com/best/multilingual) | 54.4 | +7.0 | #44 of 297, top 15% |

## Closest competitors

The models ranked just above and below GPT-5.6 Terra. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-5.6 Terra
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | #13 | 60.7 | $10 | 34 | [Compare](https://noometry.com/compare/claude-opus-4-8-vs-gpt-5-6-terra) |
| [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) | #14 | 59.8 | $1.50 | — | [Compare](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-5-6-terra) |
| [Kimi K3](https://noometry.com/models/kimi-k3) | #15 | 59.5 | $6 | — | [Compare](https://noometry.com/compare/gpt-5-6-terra-vs-kimi-k3) |
| [GPT-5.4](https://noometry.com/models/gpt-5-4) | #16 | 59.4 | $5.63 | 12 | [Compare](https://noometry.com/compare/gpt-5-4-vs-gpt-5-6-terra) |
| [GPT-5.4 Pro](https://noometry.com/models/gpt-5-4-pro) | #18 | 58.9 | $67.50 | — | [Compare](https://noometry.com/compare/gpt-5-4-pro-vs-gpt-5-6-terra) |
| [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | #19 | 58.3 | $10 | 33 | [Compare](https://noometry.com/compare/claude-opus-4-7-vs-gpt-5-6-terra) |
| [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | #20 | 58.2 | $10 | 19 | [Compare](https://noometry.com/compare/claude-opus-4-6-vs-gpt-5-6-terra) |
| [Grok 4.6](https://noometry.com/models/grok-4-6) | #21 | 56.9 | $3 | — | [Compare](https://noometry.com/compare/gpt-5-6-terra-vs-grok-4-6) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

GPT-5.6 Terra Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 53.8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 24.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 69.6% | #7 of 29, top 25% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 35.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 60.2% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 41.3% | #19 of 37, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 30.7% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 25.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 41.3% | #10 of 14, top 72% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 27.6% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 33.6% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1522 | #41 of 113, top 37% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 50.1% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 49.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 55% | #25 of 121, top 21% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 49.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 44.6% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 51.6% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 78.3% | #11 of 119, top 10% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1484 | #40 of 294, top 14% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,951 | #8 of 105, top 8% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GPT-5.6 Terra Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 58.2% | #14 of 49, top 29% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 53.2% | #6 of 35, top 18% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 24.7% | #10 of 36, top 28% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 24.7% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 7,343 | #14 of 60, top 24% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GPT-5.6 Terra Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 67.1% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 18.8% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 83.9% | #14 of 83, top 17% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 37.5% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 74.2% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 48.9% | #41 of 77, top 54% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 48.9% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 51.3% | #62 of 99, top 63% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 78.4% | #39 of 91, top 43% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 92% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 60.2% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 96.5% | #11 of 83, top 14% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 77% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 94% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 22.9% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 9.4% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 30% | #10 of 134, top 8% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 17.4% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 2% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 27.1% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 22% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 54% | #9 of 129, top 7% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 5% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1468 | #39 of 297, top 14% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 19% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 35% | #19 of 74, top 26% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-28 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 10% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 14% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 91.2% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 90.1% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 93.3% | #26 of 151, top 18% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 90.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 78.7% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 92.5% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 51.4% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 49.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 55% | #11 of 125, top 9% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 49.6% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 46.5% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 52.2% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 83.8% | #6 of 25, top 24% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 159.62 | #11 of 213, top 6% |  | [Epoch AI](https://epoch.ai/eci) | 2026-07-09 |

### Math

GPT-5.6 Terra Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 86% | #10 of 81, top 13% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 70.7% | #13 of 63, top 21% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 88.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 99.7% | #12 of 173, top 7% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 53.3% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 74% | #14 of 77, top 19% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1466 | #45 of 285, top 16% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

GPT-5.6 Terra Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 87.4% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 93.3% | #16 of 186, top 9% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-09 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 77.3% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 43.2% | #38 of 77, top 50% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1492 | #28 of 273, top 11% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-5.6 Terra Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1271 | #36 of 122, top 30% | xhigh | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 30.8% | #15 of 31, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 54.2% | #9 of 31, top 30% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1472 | #10 of 38, top 27% | xhigh | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

GPT-5.6 Terra Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1439 | #44 of 297, top 15% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1513 | #31 of 285, top 11% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1471 | #39 of 223, top 18% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1460 | #35 of 231, top 16% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1457 | #21 of 211, top 10% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1425 | #33 of 213, top 16% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1450 | #43 of 283, top 16% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1448 | #53 of 226, top 24% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-5.6 Terra Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1454 | #38 of 298, top 13% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-5.6 Terra Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1451 | #51 of 291, top 18% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-5.6 Terra Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1447 | #49 of 297, top 17% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1410 | #65 of 295, top 23% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1855 | #15 of 115, top 14% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1234 | #11 of 28, top 40% |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1449 | #55 of 295, top 19% | xhigh | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-5.6 Terra API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $2 | $12 | $0.20 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $2 | $12 | $0.20 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $2 | $12 | $0.20 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-5.6-terra) | $2 | $12 | $0.20 | 2026-10-10 |

[All OpenAI API prices →](https://noometry.com/llm-pricing/openai) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare GPT-5.6 Terra

-   [GPT-5.6 Terra vs GPT-5.4](https://noometry.com/compare/gpt-5-4-vs-gpt-5-6-terra)
-   [GPT-5.6 Terra vs GPT-5.4 Pro](https://noometry.com/compare/gpt-5-4-pro-vs-gpt-5-6-terra)
-   [GPT-5.6 Terra vs Kimi K3](https://noometry.com/compare/gpt-5-6-terra-vs-kimi-k3)
-   [GPT-5.6 Terra vs Claude Opus 4.7](https://noometry.com/compare/claude-opus-4-7-vs-gpt-5-6-terra)
-   [GPT-5.6 Terra vs Gemini 3.7 Flash](https://noometry.com/compare/gemini-3-7-flash-vs-gpt-5-6-terra)
-   [GPT-5.6 Terra vs Claude Opus 4.6](https://noometry.com/compare/claude-opus-4-6-vs-gpt-5-6-terra)
-   [GPT-5.6 Terra vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-5-6-terra)
-   [GPT-5.6 Terra vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-5-6-terra)
-   [GPT-5.6 Terra vs Grok 4.6](https://noometry.com/compare/gpt-5-6-terra-vs-grok-4-6)
-   [GPT-5.6 Terra vs Qwen3.8 Max](https://noometry.com/compare/gpt-5-6-terra-vs-qwen3-8-max)
-   [GPT-5.6 Terra vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-5-6-terra)
-   [GPT-5.6 Terra vs Muse Spark 1.3](https://noometry.com/compare/gpt-5-6-terra-vs-muse-spark-1-3)
-   [GPT-5.6 Terra vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-gpt-5-6-terra)
-   [GPT-5.6 Terra vs MiMo-V2.6-Pro](https://noometry.com/compare/gpt-5-6-terra-vs-mimo-v2-6-pro)

## Other OpenAI models

-   [GPT-6 Astra](https://noometry.com/models/gpt-6-astra)70.8
-   [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol)65.6
-   [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol)65.0
-   [GPT-5.5 Pro](https://noometry.com/models/gpt-5-5-pro)64.3
-   [GPT-5.5](https://noometry.com/models/gpt-5-5)63.4
-   [GPT-6 Sol](https://noometry.com/models/gpt-6-sol)61.8
-   [GPT-5.4](https://noometry.com/models/gpt-5-4)59.4
-   [GPT-5.4 Pro](https://noometry.com/models/gpt-5-4-pro)58.9

## Frequently asked questions

### How good is GPT-5.6 Terra?

GPT-5.6 Terra by OpenAI ranks 17th of 354 ranked models on the Noometry Index as of October 2026, with a score of 59.2. Its strongest category is multimodal, where it ranks 11th. API pricing starts at $2 per million input tokens and $12 per million output tokens, with a 1.05M-token context window.

### How much does GPT-5.6 Terra cost?

GPT-5.6 Terra costs $2 per million input tokens and $12 per million output tokens on OpenAI's own API, with cached input at $0.20.

### What is GPT-5.6 Terra's context window?

GPT-5.6 Terra accepts up to 1.05M tokens of input and can write up to 128K tokens in one response.

### Is GPT-5.6 Terra open source?

No. GPT-5.6 Terra is proprietary and available only through OpenAI's API and partner platforms.

### How fast is GPT-5.6 Terra?

GPT-5.6 Terra generated about 11 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are GPT-5.6 Terra's strengths and weaknesses?

Relative to other ranked models, GPT-5.6 Terra places best in math, coding, reasoning and lowest in long context, agentic & tool use, multilingual.

### What is GPT-5.6 Terra best at?

Its best category is multimodal, where it ranks 11th on Noometry.

### Cite this page

Noometry. (2026). GPT-5.6 Terra benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gpt-5-6-terra

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gpt-5-6-terra.md).