Agentic
Like-for-like- Qwen3.7 Max
- 69.7
- Sakana Fugu
- 80.2
- Weighted basis
- 1 vs 1 rows
- Reading
- Sakana Fugu leads
Qwen3.7 Max 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.
7 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.
Agentic work
Tool use, computer use, and multi-step task completion
Sakana Fugu
Sakana Fugu leads on the same 1 weighted benchmark row.
Confidence: limited
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
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.
28 Qwen3.7 Max only
7 Shared
4 Sakana Fugu only
- Shared results
- 7
- Qwen3.7 Max only
- 28
- Sakana Fugu only
- 4
- Like-for-like categories
- 2 / 8
2 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.
Reasoning
Like-for-like- Qwen3.7 Max
- 90.4
- Sakana Fugu
- 86.6
- Weighted basis
- 1 vs 1 rows
- Reading
- Qwen3.7 Max leads
Coding
Directional only- Qwen3.7 Max
- 77.9
- Sakana Fugu
- 59.7
- Weighted basis
- 4 vs 2 rows
- Reading
- Directional only
Knowledge
Directional only- Qwen3.7 Max
- 64.2
- Sakana Fugu
- 95.5
- Weighted basis
- 4 vs 1 rows
- Reading
- Directional only
Math
Not comparable- Qwen3.7 Max
- 97.1
- Sakana Fugu
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Qwen3.7 Max
- 87.0
- Sakana Fugu
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Qwen3.7 Max
- Not measured
- Sakana Fugu
- 85.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- Qwen3.7 Max
- 84.4
- Sakana Fugu
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
| Category | Qwen3.7 Max | Sakana Fugu | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 69.7 | 80.2 | Like-for-like1 vs 1 rows | Sakana Fugu leads |
| Reasoning | 90.4 | 86.6 | Like-for-like1 vs 1 rows | Qwen3.7 Max leads |
| Coding | 77.9 | 59.7 | Directional only4 vs 2 rows | Directional only |
| Knowledge | 64.2 | 95.5 | Directional only4 vs 1 rows | Directional only |
| Math | 97.1 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | 87.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multimodal | Not measured | 85.1 | Not comparable0 vs 1 rows | Not comparable |
| Instruction following | 84.4 | Not measured | Not comparable2 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.
Terminal-Bench 2.0
Agentic
SciCode
Coding
MRCRv2
Reasoning
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
- Qwen3.7 Max
- API rate not published
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
Qwen3.7 Max has no comparable published API token rate. Sakana Fugu has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Qwen3.7 Max
- API rate not published
- Fits in one request
- Sakana Fugu
- API rate not published
- Fits in one request
Qwen3.7 Max 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
- Qwen3.7 Max
- 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
Qwen3.7 Max 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.
SpecificationQwen3.7 MaxSakana Fugu
Context window
Maximum documented context; output-token limits may be lower.
Qwen3.7 Max
1M
Sakana Fugu
1M
API model ID
Qwen3.7 Max
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.
Qwen3.7 Max
No comparable hosted API rate
Sakana Fugu
No comparable hosted API rate
Documented inputs
Qwen3.7 Max
Not sourced
Sakana Fugu
Not sourced
Documented outputs
Qwen3.7 Max
Not sourced
Sakana Fugu
Not sourced
Provider availability
Qwen3.7 Max
Not sourced
Sakana Fugu
Not sourced
Reasoning profile
Qwen3.7 Max
Reasoning
Sakana Fugu
Reasoning
Weight access
Qwen3.7 Max
Proprietary
Sakana Fugu
Proprietary
License
Qwen3.7 Max
Proprietary
Sakana Fugu
Proprietary
Release date
Qwen3.7 Max
2026-05-16
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 evidence39 rows
Agentic
Terminal-Bench 2.0
Qwen3.7 Max69.7%SourceSakana Fugu80.2%SourceSakana Fugu leads this result
QwenClawBench
Qwen3.7 Max64.3%SourceSakana Fugu—Not directly comparable
Claw-Eval
Qwen3.7 Max65.2%SourceSakana Fugu—Not directly comparable
BFCL v4
Qwen3.7 Max75.0%SourceSakana Fugu—Not directly comparable
MCP Atlas
Qwen3.7 Max76.4%SourceSakana Fugu—Not directly comparable
VITA-Bench
Qwen3.7 Max47.9%SourceSakana Fugu—Not directly comparable
HLE w/ tools
Qwen3.7 Max53.5%SourceSakana Fugu—Not directly comparable
Gert Labs
Qwen3.7 Max64.27%SourceSakana Fugu—Not directly comparable
ResearchClawBench
Qwen3.7 Max18.7%SourceSakana Fugu—Not directly comparable
Coding
SWE-bench Verified
Qwen3.7 Max80.4%SourceSakana Fugu—Not directly comparable
SWE-bench Pro
Qwen3.7 Max60.6%SourceSakana Fugu59%SourceQwen3.7 Max leads this result
SWE Multilingual
Qwen3.7 Max78.3%SourceSakana Fugu—Not directly comparable
NL2Repo
Qwen3.7 Max47.2%SourceSakana Fugu—Not directly comparable
SciCode
Qwen3.7 Max53.5%SourceSakana Fugu60.1%SourceSakana Fugu leads this result
LiveCodeBench
Qwen3.7 Max91.6%SourceSakana Fugu—Not directly comparable
Terminal-Bench 2.0
Qwen3.7 Max69.7%SourceSakana Fugu80.2%SourceSakana Fugu leads this result
LiveCodeBench v6
Qwen3.7 Max—Sakana Fugu92.9%SourceNot directly comparable
LiveCodeBench Pro
Qwen3.7 Max—Sakana Fugu87.8%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Qwen3.7 Max92.4%SourceSakana Fugu95.5%SourceSakana Fugu leads this result
GPQA-D
Qwen3.7 Max92.4%SourceSakana Fugu95.5%SourceSakana Fugu leads this result
HLE
Qwen3.7 Max41.4%SourceSakana Fugu—Not directly comparable
MMLU-Pro
Qwen3.7 Max89.6%SourceSakana Fugu—Not directly comparable
MMLU-Redux
Qwen3.7 Max95%SourceSakana Fugu—Not directly comparable
SuperGPQA
Qwen3.7 Max73.6%SourceSakana Fugu—Not directly comparable
MMMLU
Qwen3.7 Max90.3%SourceSakana Fugu—Not directly comparable
HLE w/o tools
Qwen3.7 Max—Sakana Fugu47.2%SourceNot directly comparable
Math
Multilingual
MMLU-ProX
Qwen3.7 Max87%SourceSakana Fugu—Not directly comparable
NOVA-63
Qwen3.7 Max59.0%SourceSakana Fugu—Not directly comparable
INCLUDE
Qwen3.7 Max86.2%SourceSakana Fugu—Not directly comparable
MAXIFE
Qwen3.7 Max89.2%SourceSakana Fugu—Not directly comparable
PolyMath
Qwen3.7 Max86.5%SourceSakana Fugu—Not directly comparable
Multimodal
CharXiv
Qwen3.7 Max—Sakana Fugu85.1%SourceNot directly comparable
Instruction following
Frequently asked questions
Which is better, Qwen3.7 Max 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, Qwen3.7 Max or Sakana Fugu?
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, Qwen3.7 Max or Sakana Fugu?
Sakana Fugu leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.
Which costs less, Qwen3.7 Max 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, Qwen3.7 Max or Sakana Fugu?
Both models list the same context window, 1M.
Related comparisons
Last updated August 13, 2026
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