OpenAI, proprietary

# GPT-6 Luna

> GPT-6 Luna by OpenAI, released September 2026. Ranked #36 of 354 with a Noometry Index of 53.3. API: $0.10 in / $0.50 out per M tokens. 1.05M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-6-luna
- Last updated: 2026-10-10
- Title: GPT-6 Luna Benchmarks, Price & Rank (October 2026)

GPT-6 Luna by OpenAI ranks 36th of 354 ranked models on the Noometry Index as of October 2026, with a score of 53.3. Its strongest category is math, where it ranks 15th. API pricing starts at $0.10 per million input tokens and $0.50 per million output tokens, with a 1.05M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #36 of 354
- **Index score:** 53.3
- **Evidence:** Confirmed 42 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** September 22, 2026
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 1.05M
- **Max output:** 128K
- **Input price:** $0.10 / M
- **Output price:** $0.50 / M
- **Blended price:** $0.20 / M
- **Output speed:** Not measured
- **Value:** #26 of 219
- **Knowledge cutoff:** May 2026
- **Input:** text, image, pdf

## Category scores

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

GPT-6 Luna category scores

1.  Coding 55.5
2.  Agentic & Tool Use 33.3
3.  Reasoning 48.2
4.  Math 76.1
5.  Knowledge 57.0
6.  Multimodal 42.4
7.  Multilingual 50.5
8.  Instruction Following 74.3
9.  Long Context 43.0
10.  Writing & Preference 58.3
11.  020406080

GPT-6 Luna category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 55.5 | #25 | 5 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 33.3 | #54 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 48.2 | #41 | 9 |
| [Math](https://noometry.com/best/math) | 76.1 | #15 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 57.0 | #41 | 3 |
| [Multimodal](https://noometry.com/best/multimodal) | 42.4 | #30 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 50.5 | #117 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 74.3 | #99 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 43.0 | #111 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 58.3 | #119 | 3 |

## Strengths and weaknesses

Categories where GPT-6 Luna places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-6 Luna: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Math](https://noometry.com/best/math) | 76.1 | +39.5 | #15 of 327, top 5% |
| [Coding](https://noometry.com/best/coding) | 55.5 | +16.8 | #25 of 340, top 8% |
| [Reasoning](https://noometry.com/best/reasoning) | 48.2 | +24.6 | #41 of 350, top 12% |

### Weakest categories

GPT-6 Luna: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multilingual](https://noometry.com/best/multilingual) | 50.5 | +3.1 | #117 of 297, top 40% |
| [Writing & Preference](https://noometry.com/best/writing) | 58.3 | +4.5 | #119 of 312, top 39% |
| [Long Context](https://noometry.com/best/long-context) | 43.0 | +2.1 | #111 of 296, top 38% |

## Closest competitors

The models ranked just above and below GPT-6 Luna. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-6 Luna
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) | #32 | 54.2 | $3.38 | — | [Compare](https://noometry.com/compare/gemini-3-5-flash-vs-gpt-6-luna) |
| [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash) | #33 | 54.1 | $1.50 | — | [Compare](https://noometry.com/compare/gemini-3-6-flash-vs-gpt-6-luna) |
| [GPT-5.2](https://noometry.com/models/gpt-5-2) | #34 | 54.1 | $4.81 | 15 | [Compare](https://noometry.com/compare/gpt-5-2-vs-gpt-6-luna) |
| [DeepSeek V4 Flash](https://noometry.com/models/deepseek-v4-flash) | #35 | 53.6 | $0.26 | 6 | [Compare](https://noometry.com/compare/deepseek-v4-flash-vs-gpt-6-luna) |
| [Grok 4.7](https://noometry.com/models/grok-4-7) | #37 | 53.1 | $3 | — | [Compare](https://noometry.com/compare/gpt-6-luna-vs-grok-4-7) |
| [DeepSeek V4.1 Flash](https://noometry.com/models/deepseek-v4-1-flash) | #38 | 52.8 | $0.26 | — | [Compare](https://noometry.com/compare/deepseek-v4-1-flash-vs-gpt-6-luna) |
| [GPT-5.2 Pro](https://noometry.com/models/gpt-5-2-pro) | #39 | 52.3 | $57.75 | — | [Compare](https://noometry.com/compare/gpt-5-2-pro-vs-gpt-6-luna) |
| [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview) | #40 | 52.3 | $1.13 | — | [Compare](https://noometry.com/compare/gemini-3-flash-preview-vs-gpt-6-luna) |

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-6 Luna Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 59.3% |  | high | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 2.4% |  | low | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 66.6% | #14 of 29, top 49% | max | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 44.5% |  | medium | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 61.3% |  | xhigh | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 42.4% | #18 of 37, top 49% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 37.3% |  | high | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 25.7% |  | low | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 42.4% |  | max | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 35.5% |  | medium | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 37.1% |  | xhigh | [Model card](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (self-reported) | 2026-09-22 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1581 | #29 of 113, top 26% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 50.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 46.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 54.6% | #26 of 121, top 22% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 50.9% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 43.1% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 51.7% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1439 | #98 of 294, top 34% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,577 | #14 of 105, top 14% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GPT-6 Luna Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 44.3% | #33 of 49, top 68% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 23% | #16 of 36, top 45% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GPT-6 Luna Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 31.4% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 4.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 59.3% | #31 of 83, top 38% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 18.1% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 0% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 41.9% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 68.7% | #51 of 91, top 57% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 70.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 37.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 86.7% | #33 of 83, top 40% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 61% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 8.8% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 73% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 15.4% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 2.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 19.4% | #26 of 134, top 20% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 10.6% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 1.1% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 17.4% |  | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 31% | #34 of 129, top 27% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1411 | #116 of 297, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 7% | #67 of 74, top 91% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 90.1% | #38 of 151, top 26% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 44.5% | #35 of 125, top 29% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 156.28 | #24 of 213, top 12% |  | [Epoch AI](https://epoch.ai/eci) | 2026-09-22 |

### Math

GPT-6 Luna Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 78.9% | #16 of 81, top 20% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 56.1% | #17 of 63, top 27% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 98.9% | #18 of 173, top 11% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 64% | #17 of 77, top 23% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1416 | #108 of 285, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

GPT-6 Luna Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 90.5% | #36 of 186, top 20% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 41.4% | #39 of 77, top 51% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1444 | #76 of 273, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GPT-6 Luna Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1217 | #72 of 122, top 60% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 31.2% | #14 of 31, top 46% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) | 44.2% | #12 of 31, top 39% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-28 |

### Multilingual

GPT-6 Luna Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1386 | #117 of 297, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1433 | #115 of 285, top 41% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1420 | #100 of 223, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1369 | #117 of 231, top 51% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1369 | #87 of 211, top 42% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1360 | #93 of 213, top 44% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1394 | #113 of 283, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1393 | #117 of 226, top 52% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-6 Luna Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1409 | #88 of 298, top 30% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-6 Luna Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1409 | #108 of 291, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-6 Luna Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1391 | #128 of 297, top 44% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1363 | #118 of 295, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1396 | #124 of 295, top 43% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-6 Luna API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $0.10 | $0.50 | $0.01 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.10 | $0.50 | $0.01 | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $0.10 | $0.50 | $0.01 | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-6-luna) | $0.10 | $0.50 | $0.01 | 2026-10-10 |

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

## Compare GPT-6 Luna

-   [GPT-6 Luna vs GPT-5.6 Luna](https://noometry.com/compare/gpt-5-6-luna-vs-gpt-6-luna)
-   [GPT-6 Luna vs DeepSeek V4 Flash](https://noometry.com/compare/deepseek-v4-flash-vs-gpt-6-luna)
-   [GPT-6 Luna vs Grok 4.7](https://noometry.com/compare/gpt-6-luna-vs-grok-4-7)
-   [GPT-6 Luna vs GPT-5.2](https://noometry.com/compare/gpt-5-2-vs-gpt-6-luna)
-   [GPT-6 Luna vs DeepSeek V4.1 Flash](https://noometry.com/compare/deepseek-v4-1-flash-vs-gpt-6-luna)
-   [GPT-6 Luna vs Gemini 3.6 Flash](https://noometry.com/compare/gemini-3-6-flash-vs-gpt-6-luna)
-   [GPT-6 Luna vs GPT-5.2 Pro](https://noometry.com/compare/gpt-5-2-pro-vs-gpt-6-luna)
-   [GPT-6 Luna vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-6-luna)
-   [GPT-6 Luna vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-6-luna)
-   [GPT-6 Luna vs Kimi K3](https://noometry.com/compare/gpt-6-luna-vs-kimi-k3)
-   [GPT-6 Luna vs Grok 4.6](https://noometry.com/compare/gpt-6-luna-vs-grok-4-6)
-   [GPT-6 Luna vs Qwen3.8 Max](https://noometry.com/compare/gpt-6-luna-vs-qwen3-8-max)
-   [GPT-6 Luna vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-6-luna)
-   [GPT-6 Luna vs Muse Spark 1.3](https://noometry.com/compare/gpt-6-luna-vs-muse-spark-1-3)

## 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-5.6 Terra](https://noometry.com/models/gpt-5-6-terra)59.2

## Frequently asked questions

### How good is GPT-6 Luna?

GPT-6 Luna by OpenAI ranks 36th of 354 ranked models on the Noometry Index as of October 2026, with a score of 53.3. Its strongest category is math, where it ranks 15th. API pricing starts at $0.10 per million input tokens and $0.50 per million output tokens, with a 1.05M-token context window.

### How much does GPT-6 Luna cost?

GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens on OpenAI's own API, with cached input at $0.01.

### What is GPT-6 Luna's context window?

GPT-6 Luna accepts up to 1.05M tokens of input and can write up to 128K tokens in one response.

### Is GPT-6 Luna open source?

No. GPT-6 Luna is proprietary and available only through OpenAI's API and partner platforms.

### What are GPT-6 Luna's strengths and weaknesses?

Relative to other ranked models, GPT-6 Luna places best in math, coding, reasoning and lowest in multilingual, writing & preference, long context.

### What is GPT-6 Luna best at?

Its best category is math, where it ranks 15th on Noometry.

### Cite this page

Noometry. (2026). GPT-6 Luna benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gpt-6-luna

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