Long documents
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna has the larger documented context window.
Confidence: documented
dots3-note Preview vs GPT-5.6 Luna
Updated September 28, 2026. Rank says GPT-5.6 Luna is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
GPT-5.6 Luna has the higher public score estimate, 65.55 versus 62.64, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- dots3-note Preview only
- 25
- GPT-5.6 Luna only
- 24
- Like-for-like categories
- 0 / 8
Estimated: dots3-note Preview · Supported: GPT-5.6 LunaHow the comparison works
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Dots3-note Preview is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Dots3-note Preview is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—dots3-note Preview63.1GPT-5.6 Luna
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
25 dots3-note Preview only
6 Shared
24 GPT-5.6 Luna only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
ARC-AGI-2Reasoning
Normalized gap 21.9dots3-note Preview:81.4%GPT-5.6 Luna:59.5%SWE-bench ProCoding
Normalized gap 1.7dots3-note Preview:61%GPT-5.6 Luna:62.7%MMMU-ProMultimodal
Normalized gap 0.7dots3-note Preview:79.1%GPT-5.6 Luna:78.4%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Agentic
Directional only- dots3-note Preview
- 56.2
- Estimated · #25/111
- GPT-5.6 Luna
- 55.2
- Supported · #30/111
- Basis
- BenchAlign v5.7 lane · 9 vs 9 public rows
- Reading
- Directional only
Coding
Not comparable- dots3-note Preview
- Not ranked
- GPT-5.6 Luna
- 63.1
- Supported · #10/136
- Basis
- BenchAlign v5.7 lane · 7 vs 8 public rows
- Reading
- Not comparable
Reasoning
Not comparable- dots3-note Preview
- 72.9
- Unranked · 1 rankable row
- GPT-5.6 Luna
- 55.8
- #24/27
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- dots3-note Preview
- 67.2
- Unranked · 10 rankable rows
- GPT-5.6 Luna
- 68.1
- #22/50
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- dots3-note Preview
- Not ranked
- GPT-5.6 Luna
- 64.7
- Supported · #23/160
- Basis
- BenchAlign v5.7 lane · 1 vs 6 public rows
- Reading
- Not comparable
Multilingual
Not comparable- dots3-note Preview
- Not ranked
- GPT-5.6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- dots3-note Preview
- 85.2
- #37/124
- GPT-5.6 Luna
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- dots3-note Preview
- Not ranked
- GPT-5.6 Luna
- 94.2
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | dots3-note Preview | GPT-5.6 Luna | Basis | Reading |
|---|---|---|---|---|
| Agentic | 56.2Estimated · #25/111 | 55.2Supported · #30/111 | Directional onlyBenchAlign v5.7 lane · 9 vs 9 public rows | Directional only |
| Coding | Not ranked | 63.1Supported · #10/136 | Not comparableBenchAlign v5.7 lane · 7 vs 8 public rows | Not comparable |
| Reasoning | 72.9Unranked · 1 rankable row | 55.8#24/27 | Not comparableProvisional lane · 1 vs 2 weighted rows | Not comparable |
| Multimodal | 67.2Unranked · 10 rankable rows | 68.1#22/50 | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Knowledge | Not ranked | 64.7Supported · #23/160 | Not comparableBenchAlign v5.7 lane · 1 vs 6 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 85.2#37/124 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 94.2Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- dots3-note Preview
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.6 Luna
- $0.0008
- Fits in one request
dots3-note Preview has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- dots3-note Preview
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.6 Luna
- $0.0136
- Fits in one request
dots3-note Preview has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- dots3-note Preview
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- GPT-5.6 Luna
- $0.02
- Fits in one request
dots3-note Preview has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Specificationdots3-note PreviewGPT-5.6 Luna
Context window
Maximum documented context; output-token limits may be lower.
dots3-note Preview
GPT-5.6 Luna
1.05M
OpenAI model catalogAPI model ID
dots3-note Preview
Not sourced
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
dots3-note Preview
No comparable hosted API rate
dots3-note Preview model cardGPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
dots3-note Preview
Not sourced
GPT-5.6 Luna
text, image
OpenAI model catalogDocumented outputs
dots3-note Preview
Not sourced
GPT-5.6 Luna
Provider availability
dots3-note Preview
Not sourced
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
dots3-note Preview
Reasoning
GPT-5.6 Luna
Reasoning
Weight access
dots3-note Preview
Open Weight
GPT-5.6 Luna
Proprietary
License
dots3-note Preview
Open Weight
GPT-5.6 Luna
Proprietary
Release date
dots3-note Preview
2026-08-14
GPT-5.6 Luna
2026-07-09
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- GPT-5.6 Luna has the higher public score estimate, 65.55 versus 62.64, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.6 Luna has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, dots3-note Preview or GPT-5.6 Luna?
GPT-5.6 Luna has the higher public score estimate, 65.55 versus 62.64, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, dots3-note Preview or GPT-5.6 Luna?
Dots3-note Preview is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, dots3-note Preview or GPT-5.6 Luna?
dots3-note Preview scores higher for agentic tasks on the public lane, 56.2 to 55.2. Dots3-note Preview is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, dots3-note Preview or GPT-5.6 Luna?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, dots3-note Preview or GPT-5.6 Luna?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 512K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence55 rows
Agentic
Claw-Eval
dots3-note Preview73.4%SourceGPT-5.6 Luna—Not directly comparable
Terminal-Bench 2.1
dots3-note Preview75.1%SourceGPT-5.6 Luna84.7%SourceGPT-5.6 Luna leads this result
Toolathlon-Verified
dots3-note Preview55.6%SourceGPT-5.6 Luna—Not directly comparable
skillsBench
dots3-note Preview52.8%SourceGPT-5.6 Luna—Not directly comparable
APEX-Agents
dots3-note Preview30.8%SourceGPT-5.6 Luna—Not directly comparable
BrowseComp
dots3-note Preview83.3%SourceGPT-5.6 Luna83.3%SourceTie
HLE w/ tools
dots3-note Preview52.6%SourceGPT-5.6 Luna—Not directly comparable
DeepSearchQA
dots3-note Preview92.1%SourceGPT-5.6 Luna—Not directly comparable
WideResearch
dots3-note Preview78.9%SourceGPT-5.6 Luna—Not directly comparable
Terminal-Bench 3.0
dots3-note Preview—GPT-5.6 Luna14.3%SourceNot directly comparable
OSWorld 2.0
dots3-note Preview—GPT-5.6 Luna45.6%SourceNot directly comparable
CyberGym
dots3-note Preview—GPT-5.6 Luna77.9%SourceNot directly comparable
ExploitGym
dots3-note Preview—GPT-5.6 Luna12.4%SourceNot directly comparable
Toolathlon
dots3-note Preview—GPT-5.6 Luna53.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
dots3-note Preview—GPT-5.6 Luna79.0%SourceNot directly comparable
ApprenticeBench
dots3-note Preview—GPT-5.6 Luna7%SourceNot directly comparable
Coding
Codeforces
dots3-note Preview3056.0SourceGPT-5.6 Luna—Not directly comparable
LiveCodeBench v6
dots3-note Preview91.5%SourceGPT-5.6 Luna—Not directly comparable
Terminal-Bench 2.1
dots3-note Preview75.1%SourceGPT-5.6 Luna84.7%SourceGPT-5.6 Luna leads this result
SWE-bench Verified
dots3-note Preview78.4%SourceGPT-5.6 Luna—Not directly comparable
SWE Multilingual
dots3-note Preview75.7%SourceGPT-5.6 Luna—Not directly comparable
SWE-bench Pro
dots3-note Preview61%SourceGPT-5.6 Luna62.7%SourceGPT-5.6 Luna leads this result
NL2Repo
dots3-note Preview49.8%SourceGPT-5.6 Luna—Not directly comparable
DeepSWE
dots3-note Preview—GPT-5.6 Luna67.2%SourceNot directly comparable
FrontierCode 1.1 Extended
dots3-note Preview—GPT-5.6 Luna55.1%SourceNot directly comparable
cursorBench32
dots3-note Preview—GPT-5.6 Luna61.1%SourceNot directly comparable
VulcanBench v3
dots3-note Preview—GPT-5.6 Luna85.5%SourceNot directly comparable
SWE-bench (Vals)
dots3-note Preview—GPT-5.6 Luna93.0%SourceNot directly comparable
cursorBench40
dots3-note Preview—GPT-5.6 Luna35.9%SourceNot directly comparable
Reasoning
Multimodal
SimpleVQA
dots3-note Preview72.5%SourceGPT-5.6 Luna—Not directly comparable
MMMU-Pro
dots3-note Preview79.1%SourceGPT-5.6 Luna78.4%Sourcedots3-note Preview leads this result
MathVision
dots3-note Preview87.7%SourceGPT-5.6 Luna—Not directly comparable
ZeroBench
dots3-note Preview19.0%SourceGPT-5.6 Luna—Not directly comparable
CharXiv w/o tools
dots3-note Preview83.1%SourceGPT-5.6 Luna—Not directly comparable
GDP.pdf (no tools)
dots3-note Preview60.7%SourceGPT-5.6 Luna—Not directly comparable
PerceptionBench
dots3-note Preview53.4%SourceGPT-5.6 Luna—Not directly comparable
BabyVision
dots3-note Preview50.0%SourceGPT-5.6 Luna—Not directly comparable
MMVU
dots3-note Preview79.9%SourceGPT-5.6 Luna—Not directly comparable
VideoMMMU
dots3-note Preview86.8%SourceGPT-5.6 Luna—Not directly comparable
MMMU-Pro w/ Python
dots3-note Preview—GPT-5.6 Luna79.5%SourceNot directly comparable
Knowledge
HLE
dots3-note Preview52.6%SourceGPT-5.6 Luna—Not directly comparable
GPQA
dots3-note Preview—GPT-5.6 Luna92.3%SourceNot directly comparable
GPQA-D
dots3-note Preview—GPT-5.6 Luna92.3%SourceNot directly comparable
HealthBench Professional
dots3-note Preview—GPT-5.6 Luna55.7%SourceNot directly comparable
HealthBench Hard
dots3-note Preview—GPT-5.6 Luna32.0%SourceNot directly comparable
GPQA Diamond (Vals)
dots3-note Preview—GPT-5.6 Luna91.7%SourceNot directly comparable
MMLU-Pro (Vals)
dots3-note Preview—GPT-5.6 Luna86.0%SourceNot directly comparable
Instruction following
Math
IMOAnswerBench
dots3-note Preview90.9%SourceGPT-5.6 Luna—Not directly comparable
FrontierMath (legacy)
dots3-note Preview—GPT-5.6 Luna78.6%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
dots3-note Preview—GPT-5.6 Luna78.600%SourceNot directly comparable
FrontierMath v2 (Tier 4)
dots3-note Preview—GPT-5.6 Luna58.500%SourceNot directly comparable
55 public results · 6 shared
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Last updated September 28, 2026