Long documents
Prompts that approach the documented context limit
Sakana Fugu
Sakana Fugu has the larger documented context window.
Confidence: documented
MAI-Thinking-1 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
Share or export
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. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 4
- MAI-Thinking-1 only
- 10
- Sakana Fugu only
- 7
- Like-for-like categories
- 0 / 8
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
MAI-Thinking-1 and Sakana Fugu are 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
MAI-Thinking-1 and Sakana Fugu are 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.
—MAI-Thinking-1—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.
10 MAI-Thinking-1 only
4 Shared
7 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.
GPQAKnowledge
Normalized gap 11.3MAI-Thinking-1:84.2%Sakana Fugu:95.5%SWE-bench ProCoding
Normalized gap 6.2MAI-Thinking-1:52.8%Sakana Fugu:59%
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- MAI-Thinking-1
- Not ranked
- Sakana Fugu
- Not ranked
- Basis
- BenchAlign v5.7 lane · 1 vs 1 public rows
- Reading
- Not comparable
Coding
Not comparable- MAI-Thinking-1
- Not ranked
- Sakana Fugu
- Not ranked
- Basis
- BenchAlign v5.7 lane · 4 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- MAI-Thinking-1
- Not ranked
- Sakana Fugu
- 70.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MAI-Thinking-1
- Not ranked
- Sakana Fugu
- 68.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- MAI-Thinking-1
- Not ranked
- Sakana Fugu
- Not ranked
- Basis
- BenchAlign v5.7 lane · 4 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- MAI-Thinking-1
- Not ranked
- Sakana Fugu
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- MAI-Thinking-1
- 95.4
- #1/124
- Sakana Fugu
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MAI-Thinking-1
- 72.8
- Unranked · 3 rankable rows
- Sakana Fugu
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | MAI-Thinking-1 | Sakana Fugu | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 1 vs 1 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 4 vs 5 public rows | Not comparable |
| Reasoning | Not ranked | 70.6Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 68.6Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 4 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 95.4#1/124 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 72.8Unranked · 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.
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
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate. Sakana Fugu has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate. Sakana Fugu has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Sakana Fugu
- API rate not published
- Fits in one request
- Cached-input rate unavailable
MAI-Thinking-1 has no comparable published API token rate. 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.
SpecificationMAI-Thinking-1Sakana Fugu
Context window
Maximum documented context; output-token limits may be lower.
MAI-Thinking-1
256K
Sakana Fugu
1M
API model ID
MAI-Thinking-1
Not sourced
Sakana Fugu
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MAI-Thinking-1
No comparable hosted API rate
Sakana Fugu
No comparable hosted API rate
Documented inputs
MAI-Thinking-1
Not sourced
Sakana Fugu
Not sourced
Documented outputs
MAI-Thinking-1
Not sourced
Sakana Fugu
Not sourced
Provider availability
MAI-Thinking-1
Not sourced
Sakana Fugu
Not sourced
Reasoning profile
MAI-Thinking-1
Reasoning
Sakana Fugu
Reasoning
Weight access
MAI-Thinking-1
Proprietary
Sakana Fugu
Proprietary
License
MAI-Thinking-1
Proprietary
Sakana Fugu
Proprietary
Release date
MAI-Thinking-1
2026-06-02
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
- Sakana Fugu has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, MAI-Thinking-1 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, MAI-Thinking-1 or Sakana Fugu?
MAI-Thinking-1 and Sakana Fugu are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, MAI-Thinking-1 or Sakana Fugu?
MAI-Thinking-1 and Sakana Fugu are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, MAI-Thinking-1 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, MAI-Thinking-1 or Sakana Fugu?
Sakana Fugu has the larger documented context window: 1M, compared with 256K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence21 rows
Agentic
Coding
LiveCodeBench v6
MAI-Thinking-187.7%SourceSakana Fugu92.9%SourceSakana Fugu leads this result
SWE-bench Verified
MAI-Thinking-173.5%SourceSakana Fugu—Not directly comparable
SWE-bench Pro
MAI-Thinking-152.8%SourceSakana Fugu59%SourceSakana Fugu leads this result
Terminal-Bench 2.0
MAI-Thinking-146.0%SourceSakana Fugu—Not directly comparable
Terminal-Bench 2.1
MAI-Thinking-1—Sakana Fugu80.2%SourceNot directly comparable
LiveCodeBench Pro
MAI-Thinking-1—Sakana Fugu87.8%SourceNot directly comparable
SciCode
MAI-Thinking-1—Sakana Fugu60.1%SourceNot directly comparable
Reasoning
Multimodal
CharXiv
MAI-Thinking-1—Sakana Fugu85.1%SourceNot directly comparable
Knowledge
GPQA
MAI-Thinking-184.2%SourceSakana Fugu95.5%SourceSakana Fugu leads this result
GPQA-D
MAI-Thinking-184.2%SourceSakana Fugu95.5%SourceSakana Fugu leads this result
MMLU-Pro
MAI-Thinking-185%SourceSakana Fugu—Not directly comparable
SimpleQA
MAI-Thinking-131%SourceSakana Fugu—Not directly comparable
HLE w/o tools
MAI-Thinking-1—Sakana Fugu47.2%SourceNot directly comparable
Instruction following
IFBench
MAI-Thinking-185%SourceSakana Fugu—Not directly comparable
Math
21 public results · 4 shared
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Last updated September 27, 2026