Data as of November 10, 2026 · How the score is built
GPT-5.6 Luna
Decision readingGPT-5.6 Luna scores 63.7 out of 100 and ranks #38 of 214. This profile shows 47 source-displayable benchmark rows; its strongest eligible category is Coding at #17. API pricing is $0.2 input and $1.2 output per million tokens, with cached input at $0.02.
Released Jul 9, 2026 — see all recent releases
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
63.7/100
field median 51.2#38 of 214 ranked models
Public
#38of 214
Verified #26 of 79
Price
$0.20input / $1.20 output
input median $0.60cached $0.020 · blended $0.70
Speed
Not measured
field median 92 tok/sTime to first token not measured
Context
1.05Mtokens
field median 256,000Maximum output length is tracked separately
Strongest published evidence
Coding ranks #17. Particularly well-suited for software development and code generation tasks.
Validate before choosing
47 published rows leave some tracked benchmark slots empty. Independent runtime speed has not been measured.
Source-linked · 47 displayable benchmark rows
Is GPT-5.6 Luna right for your job?
Its place on the LLM Selector’s job rankings, the same order the job pages publish. Describe your own job to see where it stands for that.
GPT-5.6 Luna has a newer version, GPT-6 Luna. Where GPT-6 Luna ranks by benchmark score:
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Category score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #33 of 132Percentile 76thWeight 22%11 benchmarksVerified | 59.1 | 11 benchmarks | Verified | |||
| CodingRank #17 of 158Percentile 90thWeight 20%8 benchmarksVerified | 61.9 | 8 benchmarks | Verified | |||
| ReasoningRank #36 of 158Percentile 78thWeight 17%3 benchmarksVerified | 58.3 | 3 benchmarks | Verified | |||
| MultimodalRank #23 of 67Percentile 67thWeight 12%4 benchmarksVerified | 65.4 | 4 benchmarks | Verified | |||
| KnowledgeRank #40 of 179Percentile 78thWeight 12%6 benchmarksVerified | 63.1 | 6 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathRank Not rankedWeight 5%3 benchmarksVerified | 78.9 | 3 benchmarks | Verified |
47 of 890 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic11/11 verified
- Coding8/8 verified
- Reasoning3/3 verified
- Multimodal4/4 verified
- Knowledge6/6 verified
- MultilingualNot measured
- Inst. FollowingNot measured
- Math3/3 verified
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
GPT-5.6 Luna category percentile values
- Agentic76th percentile
- Coding90th percentile
- Reasoning78th percentile
- Multimodal67th percentile
- Knowledge78th percentile
- MultilingualNot eligible
- Instruction followingNot eligible
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#33/132
- Coding#17/158
- Reasoning#36/158
- Multimodal#23/67
- Knowledge#40/179
- MultilingualNot ranked
- Inst. FollowingNot ranked
- MathNot ranked
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding8 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Pro | Score62.7% | Versus best verified row Best verified: Claude Fable 5.2 · 93% | Gap30.3 behind | Weight26% ref. weight | Provider exact |
| DeepSWE | Score67.2% | Versus best verified row Best verified: Muse Wonder Full · 79.0% | Gap11.8 behind | Weight15% ref. weight | Provider exact |
| CursorBench 3.2 | Score61.1% | Versus best verified row Best verified: Claude Fable 5.1 · 73.4% | Gap12.3 behind | Weight10% ref. weight | Benchmark exact |
| VulcanBench v3 | Score85.5% | Versus best verified row Best verified: Grok 4.5 · 89.9% | Gap4.4 behind | Weight3% ref. weight | Benchmark exact |
| Terminal-Bench 2.1 | Score84.7% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap8.1 behind | WeightScored in Agentic | Provider exact |
| FrontierCode 1.1 Extended | Score55.1% | Versus best verified row Best verified: GPT-6 Astra · 64.5% | Gap9.4 behind | WeightDisplay only | Provider exact |
| SWE-bench (Vals)SWE-bench, Vals AI run | Score93.0% | Versus best verified row Best verified: Claude Opus 5 · 97.0% | Gap4 behind | WeightDisplay only | Verified |
| CursorBench 4.0 | Score35.9% | Versus best verified row Best verified: Gemini 4 Carbon · 60.4% | Gap24.5 behind | WeightDisplay only | Benchmark exact |
Agentic11 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.1 | Score84.7% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap8.1 behind | Weight8% ref. weight | Provider exact |
| BrowseComp | Score83.3% | Versus best verified row Best verified: Atria Dawn Preview · 92.5% | Gap9.2 behind | Weight8% ref. weight | Provider exact |
| Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run | Score79.0% | Versus best verified row Best verified: GPT-6 Astra · 87.3% | Gap8.3 behind | Weight4% ref. weight | |
| Terminal-Bench 3.0 | Score14.3% | Versus best verified row Best verified: Claude Opus 5 · 42.7% | Gap28.4 behind | Weight3% ref. weight | Benchmark exact |
| Toolathlon | Score53.4% | Versus best verified row Best verified: Muse Spark 1.1 · 75.6% | Gap22.2 behind | Weight3% ref. weight | Provider exact |
| Terminal-Bench 4.1 | Score17.60% | Versus best verified row Best verified: Claude Opus 5.5 · 65.80% | Gap48.2 behind | WeightDisplay only | Benchmark exact |
| OSWorld 2.0 | Score45.6% | Versus best verified row Best verified: Kimi K3.1 Pro · 79.6% | Gap34 behind | WeightDisplay only | Provider exact |
| CyberGym | Score77.9% | Versus best verified row Best verified: MiMo-V2.6-Flash · 95.1% | Gap17.2 behind | WeightDisplay only | Provider exact |
| ExploitGym | Score12.4% | Versus best verified row Best verified: Gemini 4 Carbon · 53.5% | Gap41.1 behind | WeightDisplay only | Provider exact |
| ApprenticeBenchApprenticeBench: end-to-end computer use, continual learning, and long-horizon agency on a real accounts-payable job | Score7% | Versus best verified row Best verified: Claude Fable 5.1 · 72% | Gap65 behind | WeightDisplay only | Benchmark exact |
| MazeBenchMazeBench: visual spatial reasoning in a 3D open world | Score0% | Versus best verified row Best verified: GPT-6 Astra · 23% | Gap23 behind | WeightDisplay only | Benchmark exact |
Reasoning3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| ARC-AGI-2Abstraction and Reasoning Corpus for AGI v2 | Score59.5% | Versus best verified row Best verified: GPT-6 Astra · 95% | Gap35.5 behind | Weight15% ref. weight | Benchmark exact |
| ARC-AGI-3Abstraction and Reasoning Corpus for AGI v3 | Score0.2% | Versus best verified row Best verified: Claude Fable 5.2 · 65.7% | Gap65.5 behind | Weight15% ref. weight | Benchmark exact |
| ARC-AGI-1ARC-AGI-1 Semi-Private Evaluation | Score88.00% | Versus best verified row Best verified: Claude Fable 5 · 98.50% | Gap10.5 behind | WeightDisplay only | Benchmark exact |
Multimodal4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMMU-ProMassive Multi-discipline Multimodal Understanding Pro | Score78.4% | Versus best verified row Best verified: GPT-6.1 Terra · 85.5% | Gap7.1 behind | Weight30% ref. weight | Provider exact |
| ParseBenchParseBench: document parsing for AI agents (LlamaIndex leaderboard) | Score66.86% | Versus best verified row Best verified: Claude Opus 5.5 · 78.80% | Gap11.9 behind | Weight7% ref. weight | |
| ExtractBench (LlamaIndex)ExtractBench: schema-guided enterprise document extraction (LlamaIndex leaderboard) | Score91.16% | Versus best verified row Best verified: GPT-6.1 Sol · 95.31% | Gap4.2 behind | WeightDisplay only | |
| MMMU-Pro w/ PythonMMMU-Pro with Python | Score79.5% | Versus best verified row Best verified: Kimi K3.1 Pro · 90% | Gap10.5 behind | WeightDisplay only | Provider exact |
Knowledge6 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-Pro (Vals)MMLU-Pro, Vals AI run | Score86.0% | Versus best verified row Best verified: Claude Fable 5.1 · 92.4% | Gap6.4 behind | Weight6% ref. weight | Verified |
| GPQAGraduate-Level Google-Proof Q&A | Score92.3% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap3.7 behind | Weight3% ref. weight | Provider exact |
| GPQA Diamond (Vals)GPQA Diamond, Vals AI run | Score91.7% | Versus best verified row Best verified: Gemini 3.1 Pro · 95.5% | Gap3.8 behind | Weight2% ref. weight | |
| GPQA-DGPQA Diamond | Score92.3% | Versus best verified row Best verified: GPT-6 Astra · 96.0% | Gap3.7 behind | WeightDisplay only | Provider exact |
| HealthBench Professional | Score55.7% | Versus best verified row Best verified: Claude Sonnet 5.5 · 69.2% | Gap13.5 behind | WeightDisplay only | Provider exact |
| HealthBench Hard | Score32.0% | Versus best verified row Best verified: Muse Spark · 42.8% | Gap10.8 behind | WeightDisplay only | Provider exact |
Math3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | Score78.600% | Versus best verified row Best verified: Claude Fable 5.2 · 95.088% | Gap16.5 behind | WeightWeighted 30% | Provider exact |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | Score58.500% | Versus best verified row Best verified: Claude Fable 5.2 · 100.000% | Gap41.5 behind | WeightWeighted 10% | Provider exact |
| FrontierMath (legacy)FrontierMath legacy aggregate | Score78.6% | Versus best verified row Best verified: GPT-5.6 Sol · 89% | Gap10.4 behind | WeightDisplay only | Provider exact |
External signals12 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| NYT ConnectionsNYT Connections LLM Benchmark (extended version) | Score69.4% | Versus best verified row Best verified: Gemini 4 Argon · 98.6% | Gap29.2 behind | WeightDisplay only | Benchmark exact |
| Chart Arena | Score1373 | Versus best verified row Best verified: Claude Opus 5.5 · 1628 | Gap255 behind | WeightDisplay only | Benchmark exact |
| DAYJOB Healthcare | Score1.2% | Versus best verified row Best verified: Claude Opus 5.5 · 25.2% | Gap24 behind | WeightDisplay only | Benchmark exact |
| DAYJOB | Score1.1% | Versus best verified row Best verified: Claude Opus 5.5 · 24.5% | Gap23.4 behind | WeightDisplay only | Benchmark exact |
| StealthMarkStealthMark Intelligence Benchmarks (overall score) | Score37.8 | Versus best verified row Best verified: GPT-6 Astra · 74.6 | Gap36.8 behind | WeightDisplay only | Benchmark exact |
| BabaIsBenchBabaIsBench (Baba Is You intro levels, pass@1) | Score63% | Versus best verified row Best verified: Claude Opus 5 · 100% | Gap37 behind | WeightDisplay only | Benchmark exact |
| MaxxBenchMaxxBench (mutated DeepSWE tasks) | Score46.1% | Versus best verified row Best verified: Claude Opus 5.5 · 76.4% | Gap30.3 behind | WeightDisplay only | Benchmark exact |
| ExploitBenchExploitBench v8-bench | Score33% | Versus best verified row Best verified: GPT-6 Astra · 100% | Gap66.8 behind | WeightDisplay only | Provider exact |
| ExploitGym HoneypotExploitGym Honeypot out-of-scope exploitation rate | Score9.90% | Versus best verified row Best verified: GPT-6 Astra · 0.00% | Gap9.9 behind | WeightDisplay only | Provider exact |
| Chess PuzzlesChess Puzzles (Epoch AI) | Score43.0% | Versus best verified row Best verified: Claude Fable 5.2 · 79.0% | Gap36 behind | WeightDisplay only | Benchmark exact |
| Mystery Game PuzzlesMystery Game Puzzles (Epoch AI) | Score21.0% | Versus best verified row Best verified: Claude Fable 5.2 · 90.0% | Gap69 behind | WeightDisplay only | Benchmark exact |
| Furniture AssemblyFurniture Assembly Benchmark (Epoch AI) | Score42.5% | Versus best verified row Best verified: Claude Fable 5.2 · 90.0% | Gap47.5 behind | WeightDisplay only | Benchmark exact |
What it costs to get this score
Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.
GPT-5.6 Luna · 63.7 score · $0.70 blended per million tokens
The chart opens on the current model. Scroll horizontally to inspect the full price axis.
Horizontal: blended price per million tokens, log scale · Vertical: public score
Published price history
This chart appears only when at least two dated first-party price records exist for the exact model. It shows rate changes, not an inferred cost trend.
2 dated first-party price records. The newest record lists $1.2 per million tokens.
Lineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- gpt-5.6-lunaOpenAI model catalog
- Context window
- 1.05MOpenAI model catalog
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- text, imageOpenAI model catalog
- Output modalities
- textOpenAI model catalog
- Parameters
- Not disclosed by the provider
- Availability
- OpenAI Responses APIOpenAI model catalog
- Cloud regions
- Not tracked yet
- Lifecycle
- activeOpenAI model catalog
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Published at $0.020 per million cached input tokensOpenAI pricing
- Self-host
- Weights are not published
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
GPT-5.6 Luna ranks #38 of 214 on the public leaderboard with a score of 63.72/100. Its source-verified position is #26 of 79.
GPT-5.6 Luna is a proprietary model with a 1.05M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
GPT-5.6 Luna sits in the GPT-5.6 family with GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Cyber. 47 of 890 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #17, while its lowest eligible position is Knowledge at #40. particularly well-suited for software development and code generation tasks.
Last updated November 10, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Where to run GPT-5.6 Luna by API
OpenRouter is the one host we track that publishes a per-token price for it. Prices read from each host’s own pages on October 20, 2026.
| Host | Input /1M | Output /1M | Cached /1M | Context |
|---|---|---|---|---|
| OpenRouter | $0.20 | $1.20 | $0.02 | 1.05M |
Ask your AI, from current BenchLM data
Where does GPT-5.6 Luna rank right now, and what does it cost?
Questions
How does GPT-5.6 Luna perform overall in AI benchmarks?
GPT-5.6 Luna ranks #38 out of 214 models on the public BenchAlign leaderboard, with a score of 63.72/100. Its evidence status is Supported, and this profile shows 47 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is GPT-5.6 Luna good for knowledge and understanding?
GPT-5.6 Luna ranks #40 out of 179 eligible models for knowledge and understanding, with a public category score of 63.1/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is GPT-5.6 Luna good for coding and programming?
GPT-5.6 Luna ranks #17 out of 158 eligible models for coding and programming, with a public category score of 61.9/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is GPT-5.6 Luna good for mathematics?
GPT-5.6 Luna has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is GPT-5.6 Luna good for reasoning and logic?
GPT-5.6 Luna ranks #36 out of 158 eligible models for reasoning and logic, with a public category score of 58.3/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is GPT-5.6 Luna good for agentic tool use and computer tasks?
GPT-5.6 Luna ranks #33 out of 132 eligible models for agentic tool use and computer tasks, with a public category score of 59.1/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is GPT-5.6 Luna good for multimodal and grounded tasks?
GPT-5.6 Luna ranks #23 out of 67 eligible models for multimodal and grounded tasks, with a public category score of 65.4/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Which sibling models are related to GPT-5.6 Luna?
GPT-5.6 Luna belongs to the GPT-5.6 family. Related tracked variants include GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Cyber. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does GPT-5.6 Luna have full benchmark coverage on BenchLM?
No. GPT-5.6 Luna currently has 65 source-displayable rows. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of GPT-5.6 Luna?
GPT-5.6 Luna has a documented context window of 1.05M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.