Agentic
Directional only- Inkling-Small
- 70.1
- Sakana Fugu-Ultra
- 82.1
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Model comparison
Inkling-Small vs Sakana Fugu-Ultra
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
8 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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.
12 Inkling-Small only
8 Shared
3 Sakana Fugu-Ultra only
- Shared results
- 8
- Inkling-Small only
- 12
- Sakana Fugu-Ultra only
- 3
- Like-for-like categories
- 0 / 8
4 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Directional only- Inkling-Small
- 62.4
- Sakana Fugu-Ultra
- 64.5
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Knowledge
Directional only- Inkling-Small
- 53.4
- Sakana Fugu-Ultra
- 95.5
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Multimodal
Directional only- Inkling-Small
- 76.6
- Sakana Fugu-Ultra
- 86.6
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Reasoning
Not comparable- Inkling-Small
- 40.1
- Sakana Fugu-Ultra
- 93.6
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- Inkling-Small
- 92.9
- Sakana Fugu-Ultra
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Inkling-Small
- Not measured
- Sakana Fugu-Ultra
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Inkling-Small
- 82.2
- Sakana Fugu-Ultra
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | Inkling-Small | Sakana Fugu-Ultra | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 70.1 | 82.1 | Directional only2 vs 1 rows | Directional only |
| Coding | 62.4 | 64.5 | Directional only3 vs 2 rows | Directional only |
| Knowledge | 53.4 | 95.5 | Directional only2 vs 1 rows | Directional only |
| Multimodal | 76.6 | 86.6 | Directional only2 vs 1 rows | Directional only |
| Reasoning | 40.1 | 93.6 | Not comparable1 vs 1 rows | Not comparable |
| Math | 92.9 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | 82.2 | Not measured | Not comparable1 vs 0 rows | Not comparable |
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 Pro
Coding
Inkling-Small: 55.9%Sakana Fugu-Ultra: 73.7%Normalized gap 17.8Inkling-Small source Sakana Fugu-Ultra sourceTerminal-Bench 2.0
Agentic
Inkling-Small: 64.7%Sakana Fugu-Ultra: 82.1%Normalized gap 17.4Inkling-Small source Sakana Fugu-Ultra sourceSciCode
Coding
Inkling-Small: 48.7%Sakana Fugu-Ultra: 58.7%Normalized gap 10.0Inkling-Small source Sakana Fugu-Ultra sourceGPQA
Knowledge
Inkling-Small: 89.5%Sakana Fugu-Ultra: 95.5%Normalized gap 6.0Inkling-Small source Sakana Fugu-Ultra sourceCharXiv
Multimodal
Inkling-Small: 81.3%Sakana Fugu-Ultra: 86.6%Normalized gap 5.3Inkling-Small 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
- Inkling-Small
- $0.0013
- 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
- Inkling-Small
- $0.03332
- 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
- Inkling-Small
- $0.0492
- 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.
SpecificationInkling-SmallSakana Fugu-Ultra
Context window
Maximum documented context; output-token limits may be lower.
Inkling-Small
1M
Sakana Fugu-Ultra
1M
API model ID
Inkling-Small
Not sourced
Sakana 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.
Inkling-Small
$0.116 per 1M cached input tokens
Sakana Fugu-Ultra
No comparable hosted API rate
Documented inputs
Inkling-Small
Not sourced
Sakana Fugu-Ultra
Not sourced
Documented outputs
Inkling-Small
Not sourced
Sakana Fugu-Ultra
Not sourced
Provider availability
Inkling-Small
Not sourced
Sakana Fugu-Ultra
Not sourced
Reasoning profile
Inkling-Small
Hybrid
Sakana Fugu-Ultra
Reasoning
Weight access
Inkling-Small
Open Weight
Sakana Fugu-Ultra
Proprietary
License
Inkling-Small
Open Weight
Sakana Fugu-Ultra
Proprietary
Release date
Inkling-Small
2026-07-30
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 evidence23 rows
Agentic
Terminal-Bench 2.0
Inkling-Small64.7%SourceSakana Fugu-Ultra82.1%SourceSakana Fugu-Ultra leads this result
BrowseComp
Inkling-Small77.4%SourceSakana Fugu-Ultra—Not directly comparable
MCP Atlas
Inkling-Small79.6%SourceSakana Fugu-Ultra—Not directly comparable
Toolathlon-Verified
Inkling-Small54.4%SourceSakana Fugu-Ultra—Not directly comparable
Coding
SWE-bench Verified
Inkling-Small80.2%SourceSakana Fugu-Ultra—Not directly comparable
SWE-bench Pro
Inkling-Small55.9%SourceSakana Fugu-Ultra73.7%SourceSakana Fugu-Ultra leads this result
Terminal-Bench 2.0
Inkling-Small64.7%SourceSakana Fugu-Ultra82.1%SourceSakana Fugu-Ultra leads this result
SciCode
Inkling-Small48.7%SourceSakana Fugu-Ultra58.7%SourceSakana Fugu-Ultra leads this result
LiveCodeBench v6
Inkling-Small—Sakana Fugu-Ultra93.2%SourceNot directly comparable
LiveCodeBench Pro
Inkling-Small—Sakana Fugu-Ultra90.8%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Inkling-Small89.5%SourceSakana Fugu-Ultra95.5%SourceSakana Fugu-Ultra leads this result
GPQA-D
Inkling-Small89.5%SourceSakana Fugu-Ultra95.5%SourceSakana Fugu-Ultra leads this result
HLE
Inkling-Small47.8%SourceSakana Fugu-Ultra—Not directly comparable
HLE w/o tools
Inkling-Small31.6%SourceSakana Fugu-Ultra50%SourceSakana Fugu-Ultra leads this result
Math
Multimodal
Instruction following
IFBench
Inkling-Small82.2%SourceSakana Fugu-Ultra—Not directly comparable
Frequently asked questions
Which is better, Inkling-Small 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, Inkling-Small or Sakana Fugu-Ultra?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Inkling-Small or Sakana Fugu-Ultra?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Inkling-Small 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, Inkling-Small or Sakana Fugu-Ultra?
Both models list the same context window, 1M.
Related comparisons
Last updated July 30, 2026
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