Agentic
Not comparable- Sakana Fugu
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- BenchAlign lane · 1 vs 2 public rows
- Reading
- Not comparable
Sakana Fugu vs Fugu Cyber
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.
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.
Share or export
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Sakana Fugu and Fugu Cyber are not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Sakana Fugu and Fugu Cyber are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat 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-rates
Cache-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-rates
Repository 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
11 Sakana Fugu only
2 Fugu Cyber only
- Shared results
- 0
- Sakana Fugu only
- 11
- Fugu Cyber only
- 2
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Coding
Not comparable- Sakana Fugu
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- BenchAlign lane · 5 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Sakana Fugu
- 69.6
- Unranked · 1 rankable row
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Sakana Fugu
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- BenchAlign lane · 3 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- Sakana Fugu
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Sakana Fugu
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Sakana Fugu
- 68.6
- Unranked · 1 rankable row
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Sakana Fugu
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Sakana Fugu | Fugu Cyber | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign lane · 1 vs 2 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign lane · 5 vs 0 public rows | Not comparable |
| Reasoning | 69.6Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign lane · 3 vs 0 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 68.6Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
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
- Sakana Fugu
- API rate not published
- Fits in one request
- Fugu Cyber
- $0.024
- Fits in one request
Sakana Fugu has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Sakana Fugu
- API rate not published
- Fits in one request
- Fugu Cyber
- $0.408
- 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
- Sakana Fugu
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Fugu Cyber
- $0.6
- Fits in one request
Sakana Fugu has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationSakana FuguFugu Cyber
Context window
Maximum documented context; output-token limits may be lower.
Sakana Fugu
1M
Fugu Cyber
1M
API model ID
Sakana Fugu
Not sourced
Fugu Cyber
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Sakana Fugu
No comparable hosted API rate
Fugu Cyber
$0.6 per 1M cached input tokens
Documented inputs
Sakana Fugu
Not sourced
Fugu Cyber
Not sourced
Documented outputs
Sakana Fugu
Not sourced
Fugu Cyber
Not sourced
Provider availability
Sakana Fugu
Not sourced
Fugu Cyber
Not sourced
Reasoning profile
Sakana Fugu
Reasoning
Fugu Cyber
Reasoning
Weight access
Sakana Fugu
Proprietary
Fugu Cyber
Proprietary
License
Sakana Fugu
Proprietary
Fugu Cyber
Proprietary
Release date
Sakana Fugu
2026-06-22
Fugu Cyber
2026-07-21
If you are choosing between sibling variants
- Deployment change
- Both entries list Sakana AI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
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
Coding
SWE-bench Pro
Sakana Fugu59%SourceFugu Cyber—Not directly comparable
Terminal-Bench 2.0
Sakana Fugu80.2%SourceFugu Cyber—Not directly comparable
LiveCodeBench v6
Sakana Fugu92.9%SourceFugu Cyber—Not directly comparable
LiveCodeBench Pro
Sakana Fugu87.8%SourceFugu Cyber—Not directly comparable
SciCode
Sakana Fugu60.1%SourceFugu Cyber—Not directly comparable
Reasoning
MRCRv2
Sakana Fugu86.6%SourceFugu Cyber—Not directly comparable
Knowledge
Multimodal
CharXiv
Sakana Fugu85.1%SourceFugu Cyber—Not directly comparable
Frequently asked questions
Which is better, Sakana Fugu or Fugu Cyber?
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, Sakana Fugu or Fugu Cyber?
Sakana Fugu and Fugu Cyber are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Sakana Fugu or Fugu Cyber?
Sakana Fugu and Fugu Cyber are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Sakana Fugu or Fugu Cyber?
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, Sakana Fugu or Fugu Cyber?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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