Agentic
Not comparable- Ichigo
- Not measured
- Sakana Fugu
- 80.2
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Model comparison
Ichigo vs Sakana Fugu
Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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.
11 Sakana Fugu only
- Shared results
- 0
- Ichigo only
- 0
- Sakana Fugu only
- 11
- Like-for-like categories
- 0 / 8
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
Not comparable- Ichigo
- Not measured
- Sakana Fugu
- 59.7
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Ichigo
- Not measured
- Sakana Fugu
- 86.6
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Ichigo
- Not measured
- Sakana Fugu
- 95.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- Ichigo
- Not measured
- Sakana Fugu
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Ichigo
- Not measured
- Sakana Fugu
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Ichigo
- Not measured
- Sakana Fugu
- 85.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- Ichigo
- Not measured
- Sakana Fugu
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Ichigo | Sakana Fugu | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 80.2 | Not comparable0 vs 1 rows | Not comparable |
| Coding | Not measured | 59.7 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | 86.6 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 95.5 | Not comparable0 vs 1 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 85.1 | Not comparable0 vs 1 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 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.
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
- Ichigo
- API rate not published
- Fit state unavailable
- Sakana Fugu
- API rate not published
- Fits in one request
Ichigo has no comparable published API token rate. Sakana Fugu has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Ichigo
- API rate not published
- Fit state unavailable
- Sakana Fugu
- API rate not published
- Fits in one request
Ichigo 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
- Ichigo
- API rate not published
- Fit state unavailable
- Cached-input rate unavailable
- Sakana Fugu
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Ichigo 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.
SpecificationIchigoSakana Fugu
Context window
Maximum documented context; output-token limits may be lower.
Ichigo
N/A
Sakana Fugu
1M
API model ID
Ichigo
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.
Ichigo
No comparable hosted API rate
Jan model documentationSakana Fugu
No comparable hosted API rate
Documented inputs
Ichigo
Not sourced
Sakana Fugu
Not sourced
Documented outputs
Ichigo
Not sourced
Sakana Fugu
Not sourced
Provider availability
Ichigo
Not sourced
Sakana Fugu
Not sourced
Reasoning profile
Ichigo
Non-Reasoning
Sakana Fugu
Reasoning
Weight access
Ichigo
Open Weight
Sakana Fugu
Proprietary
License
Ichigo
Open Weight
Sakana Fugu
Proprietary
Release date
Ichigo
2024-10-20
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
- 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
- A complete documented context comparison is not available.
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 evidence11 rows
Agentic
Terminal-Bench 2.0
Ichigo—Sakana Fugu80.2%SourceNot directly comparable
Coding
SWE-bench Pro
Ichigo—Sakana Fugu59%SourceNot directly comparable
Terminal-Bench 2.0
Ichigo—Sakana Fugu80.2%SourceNot directly comparable
LiveCodeBench v6
Ichigo—Sakana Fugu92.9%SourceNot directly comparable
LiveCodeBench Pro
Ichigo—Sakana Fugu87.8%SourceNot directly comparable
SciCode
Ichigo—Sakana Fugu60.1%SourceNot directly comparable
Reasoning
MRCRv2
Ichigo—Sakana Fugu86.6%SourceNot directly comparable
Knowledge
Multimodal
CharXiv
Ichigo—Sakana Fugu85.1%SourceNot directly comparable
Frequently asked questions
Which is better, Ichigo or Sakana Fugu?
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, Ichigo or Sakana Fugu?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Ichigo or Sakana Fugu?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Ichigo 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, Ichigo or Sakana Fugu?
A complete documented context-window comparison is not available.
Related comparisons
Last updated August 4, 2026
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