OpenAI, proprietary
GPT-5.6 Luna
GPT-5.6 Luna by OpenAI ranks 30th of 354 ranked models on the Noometry Index as of October 2026, with a score of 54.6. Its strongest category is math, where it ranks 14th. API pricing starts at $0.20 per million input tokens and $1.20 per million output tokens, with a 1.05M-token context window.
Last verified
Specifications
- Noometry rank
- #30 of 354
- Index score
- 54.6
- Evidence
- Confirmed 52 results
- Provider
- OpenAI
- Released
- July 9, 2026
- Weights
- Proprietary
- Reasoning
- Yes
- Context window
- 1.05M
- Max output
- 128K
- Input price
- $0.20 / M
- Output price
- $1.20 / M
- Blended price
- $0.45 / M
- Output speed
- 12 tokens/s Kagi
- Value
- #52 of 219
- Knowledge cutoff
- February 2026
- Input
- text, image, pdf
Category scores
Each category score combines every public result we have in that category.
- Coding 54.5
- Agentic & Tool Use 34.4
- Reasoning 47.6
- Math 77.7
- Knowledge 58.5
- Multimodal 42.7
- Multilingual 52.8
- Instruction Following 75.6
- Long Context 43.9
- Writing & Preference 68.0
| Category | Score | Rank | Results |
|---|---|---|---|
| Coding | 54.5 | #28 | 7 |
| Agentic & Tool Use | 34.4 | #45 | 3 |
| Reasoning | 47.6 | #43 | 12 |
| Math | 77.7 | #14 | 5 |
| Knowledge | 58.5 | #34 | 3 |
| Multimodal | 42.7 | #28 | 3 |
| Multilingual | 52.8 | #78 | 1 |
| Instruction Following | 75.6 | #57 | 1 |
| Long Context | 43.9 | #82 | 1 |
| Writing & Preference | 68.0 | #29 | 5 |
Strengths and weaknesses
Categories where GPT-5.6 Luna places highest and lowest among the models ranked in each, with its score against that category's median.
Strongest categories
| Category | Score | vs median | Rank |
|---|---|---|---|
| Math | 77.7 | +41.1 | #14 of 327, top 5% |
| Coding | 54.5 | +15.8 | #28 of 340, top 9% |
| Writing & Preference | 68.0 | +14.2 | #29 of 312, top 10% |
Weakest categories
| Category | Score | vs median | Rank |
|---|---|---|---|
| Agentic & Tool Use | 34.4 | +4.1 | #45 of 154, top 30% |
| Long Context | 43.9 | +3.0 | #82 of 296, top 28% |
| Multilingual | 52.8 | +5.4 | #78 of 297, top 27% |
Closest competitors
The models ranked just above and below GPT-5.6 Luna. When scores are this close, price and speed are often the better way to choose.
| Model | Rank | Score | Blended $/M | Speed | |
|---|---|---|---|---|---|
| GLM-5.3 | #26 | 54.8 | $2.15 | — | Compare |
| Muse Spark 1.3 | #27 | 54.8 | $2 | — | Compare |
| Gemini 3 Pro | #28 | 54.8 | — | 1 | Compare |
| Claude Sonnet 5 | #29 | 54.6 | $4 | — | Compare |
| DeepSeek V4 Pro | #31 | 54.3 | $0.99 | 16 | Compare |
| Gemini 3.5 Flash | #32 | 54.2 | $3.38 | — | Compare |
| Gemini 3.6 Flash | #33 | 54.1 | $1.50 | — | Compare |
| GPT-5.2 | #34 | 54.1 | $4.81 | 15 | Compare |
Sponsored placements are available on pages like this one. Advertise on Noometry
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
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| DeepSWE | 44.2% | high | Epoch AI | ||
| DeepSWE | 1.5% | low | Epoch AI | ||
| DeepSWE | 67.2% | #12 of 29, top 42% | max | Epoch AI | |
| DeepSWE | 11.3% | medium | Epoch AI | ||
| DeepSWE | 56.9% | xhigh | Epoch AI | ||
| FrontierCode | 39.8% | #22 of 37, top 60% | Epoch AI | ||
| CursorBench | 29.4% | high | Epoch AI | ||
| CursorBench | 16% | low | Epoch AI | ||
| CursorBench | 35.9% | #13 of 14, top 93% | max | Epoch AI | |
| CursorBench | 22.2% | medium | Epoch AI | ||
| CursorBench | 33% | xhigh | Epoch AI | ||
| LMArena WebDev | 1519 | #42 of 113, top 38% | LMArena | 2026-10-08 | |
| SciCode | 50.7% | high | Epoch AI | ||
| SciCode | 45.6% | low | Epoch AI | ||
| SciCode | 53.6% | #30 of 121, top 25% | max | Epoch AI | |
| SciCode | 45.8% | medium | Epoch AI | ||
| SciCode | 39.9% | none | Epoch AI | ||
| SciCode | 50% | xhigh | Epoch AI | ||
| WeirdML | 60.9% | #28 of 119, top 24% | high | Epoch AI | |
| LMArena Coding | 1466 | #64 of 294, top 22% | xhigh | LMArena | 2026-10-08 |
| ALE-Bench | 1,667 | #11 of 105, top 11% | max | Epoch AI |
Agentic & Tool Use
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| APEX-Agents | 43% | #35 of 49, top 72% | Epoch AI | ||
| BALROG | 45.6% | #8 of 35, top 23% | max | Epoch AI | |
| GDP.pdf | 22.7% | #18 of 36, top 50% | Epoch AI | ||
| GDP.pdf | 22.7% | medium | Epoch AI | ||
| Vending-Bench 2 | 4,095 | #35 of 60, top 59% | Epoch AI |
Reasoning
Math
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| FrontierMath (Tiers 1-3) | 41.4% | low | Epoch AI | 2026-08-29 | |
| FrontierMath (Tiers 1-3) | 82.1% | #14 of 81, top 18% | max | Epoch AI | 2026-07-09 |
| FrontierMath (Tiers 1-3) | 39.6% | none | Epoch AI | 2026-08-29 | |
| FrontierMath Tier 4 | 61% | #14 of 63, top 23% | max | Epoch AI | 2026-07-09 |
| OTIS Mock AIME 2024-2025 | 66.7% | low | Epoch AI | 2026-08-07 | |
| OTIS Mock AIME 2024-2025 | 98.3% | #22 of 173, top 13% | max | Epoch AI | 2026-07-09 |
| OTIS Mock AIME 2024-2025 | 40% | none | Epoch AI | 2026-08-07 | |
| ProofBench | 60% | #19 of 77, top 25% | max | Epoch AI | |
| LMArena Math | 1458 | #54 of 285, top 19% | xhigh | LMArena | 2026-10-08 |
Knowledge
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| GPQA Diamond | 82.3% | low | Epoch AI | 2026-08-07 | |
| GPQA Diamond | 91.6% | #25 of 186, top 14% | max | Epoch AI | 2026-07-09 |
| GPQA Diamond | 63.6% | none | Epoch AI | 2026-08-07 | |
| SimpleQA Verified | 41% | #42 of 77, top 55% | max | Epoch AI | 2026-08-10 |
| LMArena Expert | 1478 | #42 of 273, top 16% | xhigh | LMArena | 2026-10-08 |
Multimodal
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Vision | 1258 | #50 of 122, top 41% | xhigh | LMArena | 2026-10-09 |
| Blueprint-Bench 2 | 22.6% | #23 of 31, top 75% | Epoch AI | ||
| Furniture Assembly | 42.5% | #14 of 31, top 46% | max | Epoch AI | 2026-09-10 |
| LMArena Document | 1457 | #18 of 38, top 48% | xhigh | LMArena | 2026-09-13 |
Multilingual
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Non-English | 1417 | #78 of 297, top 27% | xhigh | LMArena | 2026-10-08 |
| LMArena Chinese | 1470 | #73 of 285, top 26% | xhigh | LMArena | 2026-10-08 |
| LMArena French | 1456 | #58 of 223, top 27% | xhigh | LMArena | 2026-10-08 |
| LMArena German | 1454 | #41 of 231, top 18% | xhigh | LMArena | 2026-10-08 |
| LMArena Japanese | 1411 | #50 of 211, top 24% | xhigh | LMArena | 2026-10-08 |
| LMArena Korean | 1415 | #40 of 213, top 19% | xhigh | LMArena | 2026-10-08 |
| LMArena Russian | 1428 | #72 of 283, top 26% | xhigh | LMArena | 2026-10-08 |
| LMArena Spanish | 1448 | #55 of 226, top 25% | xhigh | LMArena | 2026-10-08 |
Instruction Following
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Instruction Following | 1437 | #53 of 298, top 18% | xhigh | LMArena | 2026-10-08 |
Long Context
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Longer Query | 1436 | #71 of 291, top 25% | xhigh | LMArena | 2026-10-08 |
Writing & Preference
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Text | 1431 | #77 of 297, top 26% | xhigh | LMArena | 2026-10-08 |
| LMArena Creative Writing | 1396 | #86 of 295, top 30% | xhigh | LMArena | 2026-10-08 |
| EQ-Bench Creative Writing | 1829 | #21 of 115, top 19% | EQ-Bench | ||
| EQ-Bench 4 | 1156 | #19 of 28, top 68% | EQ-Bench | ||
| LMArena Multi-Turn | 1434 | #78 of 295, top 27% | xhigh | LMArena | 2026-10-08 |
API pricing by provider
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
|---|---|---|---|---|
| azure | $0.20 | $1.20 | $0.02 | 2026-10-10 |
| bedrock | $0.20 | $1.20 | $0.02 | 2026-10-10 |
| openai | $0.20 | $1.20 | $0.02 | 2026-10-10 |
| openrouter | $0.20 | $1.20 | $0.02 | 2026-10-10 |
Compare GPT-5.6 Luna
- GPT-5.6 Luna vs Claude Sonnet 5
- GPT-5.6 Luna vs DeepSeek V4 Pro
- GPT-5.6 Luna vs Gemini 3 Pro
- GPT-5.6 Luna vs Gemini 3.5 Flash
- GPT-5.6 Luna vs Muse Spark 1.3
- GPT-5.6 Luna vs Gemini 3.6 Flash
- GPT-5.6 Luna vs Claude Fable 5.1
- GPT-5.6 Luna vs Gemini 3.8 Flash
- GPT-5.6 Luna vs Kimi K3
- GPT-5.6 Luna vs Grok 4.6
- GPT-5.6 Luna vs Qwen3.8 Max
- GPT-5.6 Luna vs GLM-5.3
- GPT-5.6 Luna vs MiMo-V2.6-Pro
Other OpenAI models
- GPT-6 Astra70.8
- GPT-6.1 Sol65.6
- GPT-5.6 Sol65.0
- GPT-5.5 Pro64.3
- GPT-5.563.4
- GPT-6 Sol61.8
- GPT-5.459.4
- GPT-5.6 Terra59.2
Frequently asked questions
How good is GPT-5.6 Luna?
GPT-5.6 Luna by OpenAI ranks 30th of 354 ranked models on the Noometry Index as of October 2026, with a score of 54.6. Its strongest category is math, where it ranks 14th. API pricing starts at $0.20 per million input tokens and $1.20 per million output tokens, with a 1.05M-token context window.
How much does GPT-5.6 Luna cost?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens on OpenAI's own API, with cached input at $0.02.
What is GPT-5.6 Luna's context window?
GPT-5.6 Luna accepts up to 1.05M tokens of input and can write up to 128K tokens in one response.
Is GPT-5.6 Luna open source?
No. GPT-5.6 Luna is proprietary and available only through OpenAI's API and partner platforms.
How fast is GPT-5.6 Luna?
GPT-5.6 Luna generated about 12 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 Luna's strengths and weaknesses?
Relative to other ranked models, GPT-5.6 Luna places best in math, coding, writing & preference and lowest in agentic & tool use, long context, multilingual.
What is GPT-5.6 Luna best at?
Its best category is math, where it ranks 14th on Noometry.