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
GPT-5.6 Luna vs Sakana Fugu
Updated September 27, 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
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Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 5
- GPT-5.6 Luna only
- 24
- Sakana Fugu only
- 6
- Like-for-like categories
- 0 / 8
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
Sakana Fugu 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
Sakana Fugu is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
64.5GPT-5.6 Luna—Sakana Fugu
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.
24 GPT-5.6 Luna only
5 Shared
6 Sakana Fugu 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.
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.
SWE-bench ProCoding
Normalized gap 3.7GPT-5.6 Luna:62.7%Sakana Fugu:59%GPQAKnowledge
Normalized gap 3.2GPT-5.6 Luna:92.3%Sakana Fugu:95.5%
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
Not comparable- GPT-5.6 Luna
- 55.3
- Supported · #28/105
- Sakana Fugu
- Not ranked
- Basis
- BenchAlign v5.7 lane · 9 vs 1 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.6 Luna
- 64.5
- Supported · #9/135
- Sakana Fugu
- Not ranked
- Basis
- BenchAlign v5.7 lane · 7 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Luna
- 54.7
- #18/19
- Sakana Fugu
- 70.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Luna
- 67.1
- #22/50
- Sakana Fugu
- 68.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.6 Luna
- 64.6
- Supported · #22/158
- Sakana Fugu
- Not ranked
- Basis
- BenchAlign v5.7 lane · 6 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Luna
- Not ranked
- Sakana Fugu
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Luna
- Not ranked
- Sakana Fugu
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Luna
- 94.2
- Unranked · 3 rankable rows
- Sakana Fugu
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Luna | Sakana Fugu | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.3Supported · #28/105 | Not ranked | Not comparableBenchAlign v5.7 lane · 9 vs 1 public rows | Not comparable |
| Coding | 64.5Supported · #9/135 | Not ranked | Not comparableBenchAlign v5.7 lane · 7 vs 5 public rows | Not comparable |
| Reasoning | 54.7#18/19 | 70.6Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Multimodal | 67.1#22/50 | 68.6Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Knowledge | 64.6Supported · #22/158 | Not ranked | Not comparableBenchAlign v5.7 lane · 6 vs 3 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 | 94.2Unranked · 3 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 v5.7) 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
- GPT-5.6 Luna
- $0.0008
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
Sakana Fugu has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Luna
- $0.0136
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
Sakana Fugu has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.6 Luna
- $0.02
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Sakana Fugu 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.
SpecificationGPT-5.6 LunaSakana Fugu
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Luna
1.05M
OpenAI model catalogSakana Fugu
1M
API model ID
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogSakana Fugu
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingSakana Fugu
No comparable hosted API rate
Documented inputs
GPT-5.6 Luna
text, image
OpenAI model catalogSakana Fugu
Not sourced
Documented outputs
GPT-5.6 Luna
Sakana Fugu
Not sourced
Provider availability
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogSakana Fugu
Not sourced
Reasoning profile
GPT-5.6 Luna
Reasoning
Sakana Fugu
Reasoning
Weight access
GPT-5.6 Luna
Proprietary
Sakana Fugu
Proprietary
License
GPT-5.6 Luna
Proprietary
Sakana Fugu
Proprietary
Release date
GPT-5.6 Luna
2026-07-09
Sakana Fugu
2026-06-22
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- 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, GPT-5.6 Luna or Sakana Fugu?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.6 Luna or Sakana Fugu?
Sakana Fugu is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.6 Luna or Sakana Fugu?
Sakana Fugu is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.6 Luna or Sakana Fugu?
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, GPT-5.6 Luna or Sakana Fugu?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence35 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Luna14.3%SourceSakana Fugu—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna84.7%SourceSakana Fugu80.2%SourceGPT-5.6 Luna leads this result
BrowseComp
GPT-5.6 Luna83.3%SourceSakana Fugu—Not directly comparable
OSWorld 2.0
GPT-5.6 Luna45.6%SourceSakana Fugu—Not directly comparable
CyberGym
GPT-5.6 Luna77.9%SourceSakana Fugu—Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%SourceSakana Fugu—Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%SourceSakana Fugu—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna79.0%SourceSakana Fugu—Not directly comparable
ApprenticeBench
GPT-5.6 Luna7%SourceSakana Fugu—Not directly comparable
Coding
SWE-bench Pro
GPT-5.6 Luna62.7%SourceSakana Fugu59%SourceGPT-5.6 Luna leads this result
Terminal-Bench 2.1
GPT-5.6 Luna84.7%SourceSakana Fugu80.2%SourceGPT-5.6 Luna leads this result
DeepSWE
GPT-5.6 Luna67.2%SourceSakana Fugu—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%SourceSakana Fugu—Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%SourceSakana Fugu—Not directly comparable
VulcanBench v3
GPT-5.6 Luna85.5%SourceSakana Fugu—Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna93.0%SourceSakana Fugu—Not directly comparable
LiveCodeBench v6
GPT-5.6 Luna—Sakana Fugu92.9%SourceNot directly comparable
LiveCodeBench Pro
GPT-5.6 Luna—Sakana Fugu87.8%SourceNot directly comparable
SciCode
GPT-5.6 Luna—Sakana Fugu60.1%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.6 Luna92.3%SourceSakana Fugu95.5%SourceSakana Fugu leads this result
GPQA-D
GPT-5.6 Luna92.3%SourceSakana Fugu95.5%SourceSakana Fugu leads this result
HealthBench Professional
GPT-5.6 Luna55.7%SourceSakana Fugu—Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%SourceSakana Fugu—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Luna91.7%SourceSakana Fugu—Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Luna86.0%SourceSakana Fugu—Not directly comparable
HLE w/o tools
GPT-5.6 Luna—Sakana Fugu47.2%SourceNot directly comparable
Math
35 public results · 5 shared
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Last updated September 27, 2026