Agentic
Not comparable- Ling 3.0 Flash
- 40.0
- Supported · #121/151
- Sakana Fugu
- Not ranked
- Basis
- BenchAlign lane · 7 vs 1 public rows
- Reading
- Not comparable
Ling 3.0 Flash vs Sakana Fugu
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.
Long documents
Prompts that approach the documented context limit
Sakana Fugu
Sakana Fugu has the larger documented context window.
Confidence: documented
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: limited
Agentic 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: limited
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.
18 Ling 3.0 Flash only
4 Shared
7 Sakana Fugu only
- Shared results
- 4
- Ling 3.0 Flash only
- 18
- Sakana Fugu 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- Ling 3.0 Flash
- 42.8
- Estimated · #126/183
- Sakana Fugu
- Not ranked
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Ling 3.0 Flash
- 69.2
- Unranked · 2 rankable rows
- Sakana Fugu
- 69.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Ling 3.0 Flash
- 45.9
- Supported · #112/181
- Sakana Fugu
- Not ranked
- Basis
- BenchAlign lane · 5 vs 3 public rows
- Reading
- Not comparable
Math
Not comparable- Ling 3.0 Flash
- 73.7
- Unranked · 3 rankable rows
- Sakana Fugu
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.0 Flash
- Not ranked
- Sakana Fugu
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.0 Flash
- Not ranked
- Sakana Fugu
- 68.8
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Ling 3.0 Flash
- 75.6
- #58/120
- Sakana Fugu
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | Ling 3.0 Flash | Sakana Fugu | Basis | Reading |
|---|---|---|---|---|
| Agentic | 40.0Supported · #121/151 | Not ranked | Not comparableBenchAlign lane · 7 vs 1 public rows | Not comparable |
| Coding | 42.8Estimated · #126/183 | Not ranked | Not comparableBenchAlign lane · 6 vs 5 public rows | Not comparable |
| Reasoning | 69.2Unranked · 2 rankable rows | 69.6Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | 45.9Supported · #112/181 | Not ranked | Not comparableBenchAlign lane · 5 vs 3 public rows | Not comparable |
| Math | 73.7Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 68.8Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | 75.6#58/120 | Not ranked | Not comparableProvisional lane · 1 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.
SciCode
Coding
GPQA
Knowledge
SWE-bench Pro
Coding
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
- Ling 3.0 Flash
- API rate not published
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
Ling 3.0 Flash has no comparable published API token rate. Sakana Fugu has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Ling 3.0 Flash
- API rate not published
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
Ling 3.0 Flash 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
- Ling 3.0 Flash
- 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
Ling 3.0 Flash has no comparable published API token rate. 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.
SpecificationLing 3.0 FlashSakana Fugu
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.0 Flash
Sakana Fugu
1M
API model ID
Ling 3.0 Flash
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.
Ling 3.0 Flash
No comparable hosted API rate
InclusionAI Ling 3.0 Flash model cardSakana Fugu
No comparable hosted API rate
Documented inputs
Ling 3.0 Flash
Not sourced
Sakana Fugu
Not sourced
Documented outputs
Ling 3.0 Flash
Not sourced
Sakana Fugu
Not sourced
Provider availability
Ling 3.0 Flash
Not sourced
Sakana Fugu
Not sourced
Reasoning profile
Ling 3.0 Flash
Reasoning
Sakana Fugu
Reasoning
Weight access
Ling 3.0 Flash
Open Weight
Sakana Fugu
Proprietary
License
Ling 3.0 Flash
Open Weight
Sakana Fugu
Proprietary
Release date
Ling 3.0 Flash
2026-07-23
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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence29 rows
Agentic
MCP Atlas
Ling 3.0 Flash65.5%SourceSakana Fugu—Not directly comparable
skillsBench
Ling 3.0 Flash44.8%SourceSakana Fugu—Not directly comparable
BFCL v4
Ling 3.0 Flash73.0%SourceSakana Fugu—Not directly comparable
WideResearch
Ling 3.0 Flash73.6%SourceSakana Fugu—Not directly comparable
BrowseComp
Ling 3.0 Flash72.2%SourceSakana Fugu—Not directly comparable
DRACO
Ling 3.0 Flash70.4%SourceSakana Fugu—Not directly comparable
Terminal-Bench 2.1 (Vals)
Ling 3.0 Flash50.2%SourceSakana Fugu—Not directly comparable
Terminal-Bench 2.0
Ling 3.0 Flash—Sakana Fugu80.2%SourceNot directly comparable
Coding
SWE-bench Pro
Ling 3.0 Flash56.6%SourceSakana Fugu59%SourceSakana Fugu leads this result
SWE Multilingual
Ling 3.0 Flash72.4%SourceSakana Fugu—Not directly comparable
LiveCodeBench v5
Ling 3.0 Flash82.8%SourceSakana Fugu—Not directly comparable
SciCode
Ling 3.0 Flash41.2%SourceSakana Fugu60.1%SourceSakana Fugu leads this result
LiveCodeBench (Vals)
Ling 3.0 Flash84.0%SourceSakana Fugu—Not directly comparable
SWE-bench (Vals)
Ling 3.0 Flash65.2%SourceSakana Fugu—Not directly comparable
Terminal-Bench 2.0
Ling 3.0 Flash—Sakana Fugu80.2%SourceNot directly comparable
LiveCodeBench v6
Ling 3.0 Flash—Sakana Fugu92.9%SourceNot directly comparable
LiveCodeBench Pro
Ling 3.0 Flash—Sakana Fugu87.8%SourceNot directly comparable
Reasoning
MRCRv2
Ling 3.0 Flash—Sakana Fugu86.6%SourceNot directly comparable
Knowledge
GPQA
Ling 3.0 Flash85.0%SourceSakana Fugu95.5%SourceSakana Fugu leads this result
GPQA-D
Ling 3.0 Flash85.0%SourceSakana Fugu95.5%SourceSakana Fugu leads this result
HLE
Ling 3.0 Flash22.7%SourceSakana Fugu—Not directly comparable
GPQA Diamond (Vals)
Ling 3.0 Flash84.8%SourceSakana Fugu—Not directly comparable
MMLU-Pro (Vals)
Ling 3.0 Flash82.0%SourceSakana Fugu—Not directly comparable
HLE w/o tools
Ling 3.0 Flash—Sakana Fugu47.2%SourceNot directly comparable
Math
Multimodal
CharXiv
Ling 3.0 Flash—Sakana Fugu85.1%SourceNot directly comparable
Instruction following
IFBench
Ling 3.0 Flash74.5%SourceSakana Fugu—Not directly comparable
Frequently asked questions
Which is better, Ling 3.0 Flash 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, Ling 3.0 Flash 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, Ling 3.0 Flash 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, Ling 3.0 Flash 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, Ling 3.0 Flash or Sakana Fugu?
Sakana Fugu has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 4, 2026
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