Long documents
Prompts that approach the documented context limit
GPT-6 Luna
GPT-6 Luna has the larger documented context window.
Confidence: documented
GPT-6 Luna vs ScaleDown Summarize
Updated October 13, 2026. We do not rank this pair: at least one has no public score. 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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- GPT-6 Luna only
- 13
- ScaleDown Summarize only
- 0
- Like-for-like categories
- 0 / 8
Supported: GPT-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.
Chat turn cost
1K fresh input + 500 output tokens
ScaleDown Summarize
ScaleDown Summarize has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-6 Luna
GPT-6 Luna has the lower estimated token cost for this stated workload. ScaleDown Summarize has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
ScaleDown Summarize
ScaleDown Summarize has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
ScaleDown Summarize 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
ScaleDown Summarize is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.
53.6GPT-6 Luna—ScaleDown Summarize
Not comparable · BenchAlign v5.8
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.
13 GPT-6 Luna only
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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
Not comparable- GPT-6 Luna
- 55.0
- Estimated · #36/123
- ScaleDown Summarize
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-6 Luna
- 53.6
- Supported · #36/148
- ScaleDown Summarize
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-6 Luna
- 59.6
- #27/29
- ScaleDown Summarize
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-6 Luna
- 81.9
- Unranked · 1 rankable row
- ScaleDown Summarize
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-6 Luna
- 66.8
- Supported · #28/180
- ScaleDown Summarize
- Not ranked
- Basis
- BenchAlign v5.8 lane · 5 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-6 Luna
- Not ranked
- ScaleDown Summarize
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-6 Luna
- Not ranked
- ScaleDown Summarize
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-6 Luna
- 83.7
- Unranked · 2 rankable rows
- ScaleDown Summarize
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-6 Luna | ScaleDown Summarize | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.0Estimated · #36/123 | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 0 public rows | Not comparable |
| Coding | 53.6Supported · #36/148 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 0 public rows | Not comparable |
| Reasoning | 59.6#27/29 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 81.9Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 66.8Supported · #28/180 | Not ranked | Not comparableBenchAlign v5.8 lane · 5 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 83.7Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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
- GPT-6 Luna
- $0.00035
- Fits in one request
- ScaleDown Summarize
- $0.00005
- Fits in one request
ScaleDown Summarize has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-6 Luna
- $0.0065
- Fits in one request
- ScaleDown Summarize
- $0.0025
- Fits in one request
ScaleDown Summarize has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-6 Luna
- $0.009
- Fits in one request
- ScaleDown Summarize
- $0.011
- Fits in one request
- Cached input priced at the published list-input rate
GPT-6 Luna has the lower modeled cost
ScaleDown Summarize has no published cached-input rate, so cached tokens use its listed input 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.
SpecificationGPT-6 LunaScaleDown Summarize
Context window
Maximum documented context; output-token limits may be lower.
GPT-6 Luna
ScaleDown Summarize
1M tokens per request (provider-listed)
ScaleDown model limits and pricing FAQAPI model ID
GPT-6 Luna
gpt-6-luna
OpenAI GPT-6 Luna model documentationScaleDown Summarize
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-6 Luna
$0.01 per 1M cached input tokens
OpenAI GPT-6 Luna model documentationScaleDown Summarize
Not published
ScaleDown API pricing and FAQDocumented inputs
GPT-6 Luna
text, image
OpenAI GPT-6 Luna model documentationScaleDown Summarize
Documented outputs
GPT-6 Luna
ScaleDown Summarize
Provider availability
GPT-6 Luna
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchScaleDown Summarize
Generally Available · ScaleDown API, Enterprise licensing
ScaleDown model limits and pricing FAQReasoning profile
GPT-6 Luna
Reasoning
ScaleDown Summarize
Unspecified
Weight access
GPT-6 Luna
Proprietary
ScaleDown Summarize
Proprietary
License
GPT-6 Luna
Proprietary
ScaleDown Summarize
Proprietary
Release date
GPT-6 Luna
2026-09-22
ScaleDown Summarize
Not sourced
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.0065 vs $0.0025. Cache-heavy agent loop: $0.009 vs $0.011.
- Context tradeoff
- GPT-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, GPT-6 Luna or ScaleDown Summarize?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-6 Luna or ScaleDown Summarize?
ScaleDown Summarize is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-6 Luna or ScaleDown Summarize?
ScaleDown Summarize is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-6 Luna or ScaleDown Summarize?
For the stated presets, chat costs $0.00035 on GPT-6 Luna and $0.00005 on ScaleDown Summarize; repository review costs $0.0065 and $0.0025; the cache-heavy agent loop costs $0.009 and $0.011. ScaleDown Summarize has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-6 Luna or ScaleDown Summarize?
GPT-6 Luna has the larger documented context window: 1.05M, compared with 1M tokens per request (provider-listed).
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence13 rows
Agentic
ExploitGym
GPT-6 Luna11.6%SourceScaleDown Summarize—Not directly comparable
Coding
Reasoning
Knowledge
HealthBench (raw)
GPT-6 Luna50.0%SourceScaleDown Summarize—Not directly comparable
HealthBench (length-adjusted)
GPT-6 Luna54.5%SourceScaleDown Summarize—Not directly comparable
HealthBench Professional
GPT-6 Luna60.8%SourceScaleDown Summarize—Not directly comparable
HealthBench Professional (raw)
GPT-6 Luna61.2%SourceScaleDown Summarize—Not directly comparable
HealthBench Hard
GPT-6 Luna31.4%SourceScaleDown Summarize—Not directly comparable
Math
13 public results · 0 shared
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Last updated October 13, 2026