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BenchLM

Data as of September 24, 2026 · How the score is built

Superseded.OpenAI has newer models in this line:GPT-6 Luna
SupersededProprietaryReasoning

Released Jul 9, 20261.05M context

GPT-5.6 Luna

Decision readingGPT-5.6 Luna scores 65.6 out of 100 and ranks #25 of 194. This profile shows 30 source-displayable benchmark rows; its strongest eligible category is Coding at #9. 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

65.6/100

field median 50.2#25 of 194 ranked models

Public

#25of 194

Verified #17 of 74

Price

$0.20input / $1.20 output

input median $0.97cached $0.020 · blended $0.70

Speed

Not measured

field median 91 tok/sTime to first token not measured

Context

1.05Mtokens

field median 256,000Maximum output length is tracked separately

Strongest published evidence

Coding ranks #9. Particularly well-suited for software development and code generation tasks.

Validate before choosing

30 published rows leave some tracked benchmark slots empty. Independent runtime speed has not been measured.

Source-linked · 30 displayable benchmark rows

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Check the published record for the model and serving route you use.

Check this model

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 scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #28 of 105Percentile 74thWeight 22%9 benchmarksVerified
55.3
CodingRank #9 of 135Percentile 94thWeight 20%7 benchmarksVerified
64.5
ReasoningRank #18 of 19Percentile 6thWeight 17%2 benchmarksVerified
54.7
MultimodalRank #22 of 50Percentile 57thWeight 12%2 benchmarksVerified
67.1
KnowledgeRank #22 of 158Percentile 87thWeight 12%6 benchmarksVerified
64.6
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathRank Not rankedWeight 5%3 benchmarksVerified
94.2

30 of 483 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How 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.

  1. Agentic9/9 verified
  2. Coding7/7 verified
  3. Reasoning2/2 verified
  4. Multimodal2/2 verified
  5. Knowledge6/6 verified
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. Math3/3 verified
Verified sourceProvisionalNot measured

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

  • Agentic74th percentile
  • Coding94th percentile
  • Reasoning6th percentile
  • Multimodal57th percentile
  • Knowledge87th percentile
  • MultilingualNot eligible
  • Instruction followingNot eligible
  • MathNot eligible

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#28/105
  2. Coding#9/135
  3. Reasoning#18/19
  4. Multimodal#22/50
  5. Knowledge#22/158
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

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.

Coding7 rows
Coding benchmark values, best verified comparison, weight, and source status
SWE-bench ProScore62.7%Versus best verified row

Best verified: Claude Opus 5.5 · 89.9%

Gap27.2 behindWeight26% ref. weight
Provider exact
DeepSWEScore67.2%Versus best verified row

Best verified: Muse Spark 1.3 · 75.4%

Gap8.2 behindWeight15% ref. weight
Provider exact
CursorBench 3.2Score61.1%Versus best verified row

Best verified: Claude Fable 5.1 · 73.4%

Gap12.3 behindWeight10% ref. weight
VulcanBench v3Score85.5%Versus best verified row

Best verified: Grok 4.5 · 89.9%

Gap4.4 behindWeight3% ref. weight
Terminal-Bench 2.1Score84.7%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap8.1 behindWeightScored in Agentic
Provider exact
FrontierCode 1.1 ExtendedScore55.1%Versus best verified row

Best verified: GPT-6 Astra · 64.5%

Gap9.4 behindWeightDisplay only
SWE-bench (Vals)SWE-bench, Vals AI runScore93.0%Versus best verified row

Best verified: Claude Opus 5 · 97.0%

Gap4 behindWeightDisplay only
Agentic9 rows
Agentic benchmark values, best verified comparison, weight, and source status
OSWorld 2.0Score45.6%Versus best verified row

Best verified: GPT-6 Astra · 72.6%

Gap27 behindWeight10% ref. weight
Provider exact
Terminal-Bench 2.1Score84.7%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap8.1 behindWeight8% ref. weight
Provider exact
BrowseCompScore83.3%Versus best verified row

Best verified: Atria Dawn Preview · 92.5%

Gap9.2 behindWeight8% ref. weight
Provider exact
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI runScore79.0%Versus best verified row

Best verified: GPT-6 Astra · 87.3%

Gap8.3 behindWeight3% ref. weight
Terminal-Bench 3.0Score14.3%Versus best verified row

Best verified: Claude Opus 5 · 42.7%

Gap28.4 behindWeight3% ref. weight
ToolathlonScore53.4%Versus best verified row

Best verified: Muse Spark 1.1 · 75.6%

Gap22.2 behindWeight3% ref. weight
Provider exact
CyberGymScore77.9%Versus best verified row

Best verified: MiMo-V2.6-Flash · 95.1%

Gap17.2 behindWeightDisplay only
Provider exact
ExploitGymScore12.4%Versus best verified row

Best verified: GPT-6 Astra · 42.4%

Gap30 behindWeightDisplay only
Provider exact
ApprenticeBenchApprenticeBench: end-to-end computer use, continual learning, and long-horizon agency on a real accounts-payable jobScore7%Versus best verified row

Best verified: Claude Fable 5.1 · 72%

Gap65 behindWeightDisplay only
Reasoning2 rows
Reasoning benchmark values, best verified comparison, weight, and source status
ARC-AGI-2Abstraction and Reasoning Corpus for AGI v2Score59.5%Versus best verified row

Best verified: GPT-6 Astra · 95%

Gap35.5 behindWeightWeighted 25%
ARC-AGI-3Abstraction and Reasoning Corpus for AGI v3Score0.2%Versus best verified row

Best verified: GPT-6 Astra · 62.7%

Gap62.5 behindWeightWeighted 15%
Multimodal2 rows
Multimodal benchmark values, best verified comparison, weight, and source status
MMMU-ProMassive Multi-discipline Multimodal Understanding ProScore78.4%Versus best verified row

Best verified: Gemini 3.5 Flash · 83.6%

Gap5.2 behindWeightWeighted 40%
Provider exact
MMMU-Pro w/ PythonMMMU-Pro with PythonScore79.5%Versus best verified row

Best verified: GPT-5.6 Sol · 84.6%

Gap5.1 behindWeightDisplay only
Provider exact
Knowledge6 rows
Knowledge benchmark values, best verified comparison, weight, and source status
MMLU-Pro (Vals)MMLU-Pro, Vals AI runScore86.0%Versus best verified row

Best verified: Claude Fable 5.1 · 92.4%

Gap6.4 behindWeight6% ref. weight
GPQAGraduate-Level Google-Proof Q&AScore92.3%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap3.7 behindWeight3% ref. weight
Provider exact
GPQA Diamond (Vals)GPQA Diamond, Vals AI runScore91.7%Versus best verified row

Best verified: Gemini 3.1 Pro · 95.5%

Gap3.8 behindWeight2% ref. weight
GPQA-DGPQA DiamondScore92.3%Versus best verified row

Best verified: GPT-6 Astra · 96.0%

Gap3.7 behindWeightDisplay only
Provider exact
HealthBench ProfessionalScore55.7%Versus best verified row

Best verified: Claude Opus 5.5 · 65.6%

Gap9.9 behindWeightDisplay only
Provider exact
HealthBench HardScore32.0%Versus best verified row

Best verified: Muse Spark · 42.8%

Gap10.8 behindWeightDisplay only
Math3 rows
Math benchmark values, best verified comparison, weight, and source status
FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3Score78.600%Versus best verified row

Best verified: GPT-5.6 Sol · 89.000%

Gap10.4 behindWeightWeighted 30%
Provider exact
FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4Score58.500%Versus best verified row

Best verified: GPT-6 Astra · 97.600%

Gap39.1 behindWeightWeighted 10%
Provider exact
FrontierMath (legacy)FrontierMath legacy aggregateScore78.6%Versus best verified row

Best verified: GPT-5.6 Sol · 89%

Gap10.4 behindWeightDisplay only
Provider exact
External signals1 row
External signals benchmark values, best verified comparison, weight, and source status
ExploitBenchExploitBench v8-benchScore33%Versus best verified row

Best verified: GPT-6 Astra · 100%

Gap66.8 behindWeightDisplay only
Provider 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.

Current modelExplore all models

GPT-5.6 Luna · 65.6 score · $0.70 blended per million tokens

The chart opens on the current model. Scroll horizontally to inspect the full price axis.

30405060708090100$0.50$1$5$10$25$50$100↘ frontierGPT-5.6 Luna

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.

0482026-072026-09Output priceInput price

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.

  1. Jul 9, 2026 · you are here

    GPT-5.6 Luna

    Score 65.6 · $0.2 / $1.2

  2. Sep 22, 2026

    GPT-6 Luna

    Score 66.6 · $0.1 / $0.5

Radar

GPT-5.6 Luna release history

Full release history

Radar confirmed these at the source. Use GPT-5.6 Luna in your work? Explore Radar to follow supported changes and choose your alerts.

Radar

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 #25 of 194 on the public leaderboard with a score of 65.6/100. Its source-verified position is #17 of 74.

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. 30 of 483 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Coding at #9, while its lowest eligible position is Agentic at #28. particularly well-suited for software development and code generation tasks.

Last updated September 24, 2026. Runtime fields remain blank until a sourced snapshot exists.

Questions

How does GPT-5.6 Luna perform overall in AI benchmarks?

GPT-5.6 Luna ranks #25 out of 194 models on the public BenchAlign leaderboard, with a score of 65.6/100. Its evidence status is Supported, and this profile shows 30 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 #22 out of 158 eligible models for knowledge and understanding, with a public category score of 64.6/100. 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 #9 out of 135 eligible models for coding and programming, with a public category score of 64.5/100. That places it in the current top ten for this category. 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 #18 out of 19 eligible models for reasoning and logic, with a public category score of 54.7/100. 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 #28 out of 105 eligible models for agentic tool use and computer tasks, with a public category score of 55.3/100. 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 multimodal and grounded tasks?

GPT-5.6 Luna ranks #22 out of 50 eligible models for multimodal and grounded tasks, with a public category score of 67.1/100. 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.

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 51 source-displayable rows across 483 tracked benchmark slots. 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.

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Compare GPT-5.6 Luna with every tracked model506 comparisons