Knowledge
Like-for-like- GPT-4.1
- 66.3
- Sakana Fugu
- 95.5
- Weighted basis
- 1 vs 1 rows
- Reading
- Sakana Fugu leads
GPT-4.1 vs Sakana Fugu
Updated August 13, 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.
1 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
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: rate-fallback
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 GPT-4.1 only
1 Shared
10 Sakana Fugu only
- Shared results
- 1
- GPT-4.1 only
- 6
- Sakana Fugu only
- 10
- Like-for-like categories
- 1 / 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.
Agentic
Not comparable- GPT-4.1
- Not measured
- Sakana Fugu
- 80.2
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Coding
Not comparable- GPT-4.1
- 54.6
- Sakana Fugu
- 59.7
- Weighted basis
- 1 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1
- Not measured
- Sakana Fugu
- 86.6
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1
- 4.1
- Sakana Fugu
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1
- Not measured
- Sakana Fugu
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1
- Not measured
- Sakana Fugu
- 85.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-4.1
- 87.4
- Sakana Fugu
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | GPT-4.1 | Sakana Fugu | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 66.3 | 95.5 | Like-for-like1 vs 1 rows | Sakana Fugu leads |
| Agentic | Not measured | 80.2 | Not comparable0 vs 1 rows | Not comparable |
| Coding | 54.6 | 59.7 | Not comparable1 vs 2 rows | Not comparable |
| Reasoning | Not measured | 86.6 | Not comparable0 vs 1 rows | Not comparable |
| Math | 4.1 | Not measured | Not comparable2 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 | 87.4 | 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.
GPQA
Knowledge
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
- GPT-4.1
- $0.006
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
Sakana Fugu has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-4.1
- $0.124
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
Sakana Fugu has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-4.1
- $0.52
- Fits in one request
- Cached input priced at the published list-input rate
- Sakana Fugu
- API rate not published
- Fits in one request
- Cached-input rate unavailable
GPT-4.1 has no published cached-input rate, so cached tokens use its listed input 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.
SpecificationGPT-4.1Sakana Fugu
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1
1M
Sakana Fugu
1M
API model ID
GPT-4.1
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.
GPT-4.1
Not published
Sakana Fugu
No comparable hosted API rate
Documented inputs
GPT-4.1
Not sourced
Sakana Fugu
Not sourced
Documented outputs
GPT-4.1
Not sourced
Sakana Fugu
Not sourced
Provider availability
GPT-4.1
Not sourced
Sakana Fugu
Not sourced
Reasoning profile
GPT-4.1
Non-Reasoning
Sakana Fugu
Reasoning
Weight access
GPT-4.1
Proprietary
Sakana Fugu
Proprietary
License
GPT-4.1
Proprietary
Sakana Fugu
Proprietary
Release date
GPT-4.1
2025-04-14
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
- 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
Coding
SWE-bench Verified
GPT-4.154.6%SourceSakana Fugu—Not directly comparable
SWE-bench Pro
GPT-4.1—Sakana Fugu59%SourceNot directly comparable
Terminal-Bench 2.0
GPT-4.1—Sakana Fugu80.2%SourceNot directly comparable
LiveCodeBench v6
GPT-4.1—Sakana Fugu92.9%SourceNot directly comparable
LiveCodeBench Pro
GPT-4.1—Sakana Fugu87.8%SourceNot directly comparable
SciCode
GPT-4.1—Sakana Fugu60.1%SourceNot directly comparable
Reasoning
MRCRv2
GPT-4.1—Sakana Fugu86.6%SourceNot directly comparable
Knowledge
Math
Multimodal
CharXiv
GPT-4.1—Sakana Fugu85.1%SourceNot directly comparable
Instruction following
IFEval
GPT-4.187.4%SourceSakana Fugu—Not directly comparable
Frequently asked questions
Which is better, GPT-4.1 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, GPT-4.1 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, GPT-4.1 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, GPT-4.1 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, GPT-4.1 or Sakana Fugu?
Both models list the same context window, 1M.
Related comparisons
Last updated August 13, 2026
Watch GPT-4.1 vs Sakana Fugu
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.