Agentic
Not comparable- Gemini 3.5 Flash-Lite
- 43.4
- Estimated · #104/151
- Sakana Fugu-Ultra
- Not ranked
- Basis
- BenchAlign lane · 3 vs 1 public rows
- Reading
- Not comparable
Gemini 3.5 Flash-Lite vs Sakana Fugu-Ultra
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
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-Ultra is 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-Ultra is 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.
6 Gemini 3.5 Flash-Lite only
4 Shared
7 Sakana Fugu-Ultra only
- Shared results
- 4
- Gemini 3.5 Flash-Lite only
- 6
- Sakana Fugu-Ultra only
- 7
- 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- Gemini 3.5 Flash-Lite
- 43.6
- Supported · #121/183
- Sakana Fugu-Ultra
- Not ranked
- Basis
- BenchAlign lane · 4 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3.5 Flash-Lite
- 60.8
- Unranked · 3 rankable rows
- Sakana Fugu-Ultra
- 77.9
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 3.5 Flash-Lite
- 53.0
- Supported · #71/181
- Sakana Fugu-Ultra
- Not ranked
- Basis
- BenchAlign lane · 2 vs 3 public rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- Sakana Fugu-Ultra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- Sakana Fugu-Ultra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.5 Flash-Lite
- 76.1
- #16/48
- Sakana Fugu-Ultra
- 72.5
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- Sakana Fugu-Ultra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.5 Flash-Lite | Sakana Fugu-Ultra | Basis | Reading |
|---|---|---|---|---|
| Agentic | 43.4Estimated · #104/151 | Not ranked | Not comparableBenchAlign lane · 3 vs 1 public rows | Not comparable |
| Coding | 43.6Supported · #121/183 | Not ranked | Not comparableBenchAlign lane · 4 vs 5 public rows | Not comparable |
| Reasoning | 60.8Unranked · 3 rankable rows | 77.9Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Knowledge | 53.0Supported · #71/181 | Not ranked | Not comparableBenchAlign lane · 2 vs 3 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 | 76.1#16/48 | 72.5Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 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.
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.
Terminal-Bench 2.0
Agentic
Gemini 3.5 Flash-Lite: 54%Sakana Fugu-Ultra: 82.1%Normalized gap 28.1Gemini 3.5 Flash-Lite source Sakana Fugu-Ultra sourceMRCRv2
Reasoning
Gemini 3.5 Flash-Lite: 72.2%Sakana Fugu-Ultra: 93.6%Normalized gap 21.4Gemini 3.5 Flash-Lite source Sakana Fugu-Ultra sourceSWE-bench Pro
Coding
Gemini 3.5 Flash-Lite: 54.2%Sakana Fugu-Ultra: 73.7%Normalized gap 19.5Gemini 3.5 Flash-Lite source Sakana Fugu-Ultra source
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
- Gemini 3.5 Flash-Lite
- $0.00155
- Fits in one request
- Sakana Fugu-Ultra
- API rate not published
- Fits in one request
Sakana Fugu-Ultra has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.5 Flash-Lite
- $0.0225
- Fits in one request
- Sakana Fugu-Ultra
- API rate not published
- Fits in one request
Sakana Fugu-Ultra has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.5 Flash-Lite
- $0.037
- Fits in one request
- Sakana Fugu-Ultra
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Sakana Fugu-Ultra 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.
SpecificationGemini 3.5 Flash-LiteSakana Fugu-Ultra
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash-Lite
Sakana Fugu-Ultra
1M
API model ID
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationSakana Fugu-Ultra
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingSakana Fugu-Ultra
No comparable hosted API rate
Documented inputs
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationSakana Fugu-Ultra
Not sourced
Documented outputs
Gemini 3.5 Flash-Lite
Sakana Fugu-Ultra
Not sourced
Provider availability
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideSakana Fugu-Ultra
Not sourced
Reasoning profile
Gemini 3.5 Flash-Lite
Reasoning
Sakana Fugu-Ultra
Reasoning
Weight access
Gemini 3.5 Flash-Lite
Proprietary
Sakana Fugu-Ultra
Proprietary
License
Gemini 3.5 Flash-Lite
Proprietary
Sakana Fugu-Ultra
Proprietary
Release date
Gemini 3.5 Flash-Lite
2026-07-21
Sakana Fugu-Ultra
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
- 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 evidence17 rows
Agentic
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54%SourceSakana Fugu-Ultra82.1%SourceSakana Fugu-Ultra leads this result
OSWorld-Verified
Gemini 3.5 Flash-Lite74%SourceSakana Fugu-Ultra—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite50.2%SourceSakana Fugu-Ultra—Not directly comparable
Coding
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54.0%SourceSakana Fugu-Ultra82.1%SourceSakana Fugu-Ultra leads this result
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%SourceSakana Fugu-Ultra73.7%SourceSakana Fugu-Ultra leads this result
LiveCodeBench (Vals)
Gemini 3.5 Flash-Lite79.0%SourceSakana Fugu-Ultra—Not directly comparable
SWE-bench (Vals)
Gemini 3.5 Flash-Lite75.0%SourceSakana Fugu-Ultra—Not directly comparable
LiveCodeBench v6
Gemini 3.5 Flash-Lite—Sakana Fugu-Ultra93.2%SourceNot directly comparable
LiveCodeBench Pro
Gemini 3.5 Flash-Lite—Sakana Fugu-Ultra90.8%SourceNot directly comparable
SciCode
Gemini 3.5 Flash-Lite—Sakana Fugu-Ultra58.7%SourceNot directly comparable
Reasoning
Knowledge
GPQA Diamond (Vals)
Gemini 3.5 Flash-Lite83.8%SourceSakana Fugu-Ultra—Not directly comparable
MMLU-Pro (Vals)
Gemini 3.5 Flash-Lite85.8%SourceSakana Fugu-Ultra—Not directly comparable
GPQA
Gemini 3.5 Flash-Lite—Sakana Fugu-Ultra95.5%SourceNot directly comparable
GPQA-D
Gemini 3.5 Flash-Lite—Sakana Fugu-Ultra95.5%SourceNot directly comparable
HLE w/o tools
Gemini 3.5 Flash-Lite—Sakana Fugu-Ultra50%SourceNot directly comparable
Multimodal
CharXiv
Gemini 3.5 Flash-Lite—Sakana Fugu-Ultra86.6%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.5 Flash-Lite or Sakana Fugu-Ultra?
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, Gemini 3.5 Flash-Lite or Sakana Fugu-Ultra?
Sakana Fugu-Ultra is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Gemini 3.5 Flash-Lite or Sakana Fugu-Ultra?
Sakana Fugu-Ultra is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 3.5 Flash-Lite or Sakana Fugu-Ultra?
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, Gemini 3.5 Flash-Lite or Sakana Fugu-Ultra?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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