Agentic
Not comparable- Qwen3.7 Max
- 42.8
- Supported · #110/151
- Fugu Cyber
- Not ranked
- Basis
- BenchAlign lane · 10 vs 2 public rows
- Reading
- Not comparable
Qwen3.7 Max vs Fugu Cyber
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
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 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.
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
Fugu Cyber 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
Fugu Cyber is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
41 Qwen3.7 Max only
2 Fugu Cyber only
- Shared results
- 0
- Qwen3.7 Max only
- 41
- Fugu Cyber only
- 2
- 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- Qwen3.7 Max
- 49.5
- Supported · #77/183
- Fugu Cyber
- Not ranked
- Basis
- BenchAlign lane · 10 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Qwen3.7 Max
- 74.8
- Unranked · 3 rankable rows
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Qwen3.7 Max
- 62.4
- Supported · #28/181
- Fugu Cyber
- Not ranked
- Basis
- BenchAlign lane · 9 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- Qwen3.7 Max
- 82.1
- Unranked · 3 rankable rows
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Qwen3.7 Max
- 100.0
- #1/12
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Qwen3.7 Max
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Qwen3.7 Max
- 91.1
- #16/120
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | Qwen3.7 Max | Fugu Cyber | Basis | Reading |
|---|---|---|---|---|
| Agentic | 42.8Supported · #110/151 | Not ranked | Not comparableBenchAlign lane · 10 vs 2 public rows | Not comparable |
| Coding | 49.5Supported · #77/183 | Not ranked | Not comparableBenchAlign lane · 10 vs 0 public rows | Not comparable |
| Reasoning | 74.8Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 62.4Supported · #28/181 | Not ranked | Not comparableBenchAlign lane · 9 vs 0 public rows | Not comparable |
| Math | 82.1Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | 100.0#1/12 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 91.1#16/120 | 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.
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
- Qwen3.7 Max
- API rate not published
- Fits in one request
- Fugu Cyber
- $0.024
- Fits in one request
Qwen3.7 Max 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
- Fugu Cyber
- $0.408
- Fits in one request
Qwen3.7 Max 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
- Fugu Cyber
- $0.6
- Fits in one request
Qwen3.7 Max 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 MaxFugu Cyber
Context window
Maximum documented context; output-token limits may be lower.
Qwen3.7 Max
1M
Fugu Cyber
1M
API model ID
Qwen3.7 Max
Not sourced
Fugu Cyber
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
Fugu Cyber
$0.6 per 1M cached input tokens
Documented inputs
Qwen3.7 Max
Not sourced
Fugu Cyber
Not sourced
Documented outputs
Qwen3.7 Max
Not sourced
Fugu Cyber
Not sourced
Provider availability
Qwen3.7 Max
Not sourced
Fugu Cyber
Not sourced
Reasoning profile
Qwen3.7 Max
Reasoning
Fugu Cyber
Reasoning
Weight access
Qwen3.7 Max
Proprietary
Fugu Cyber
Proprietary
License
Qwen3.7 Max
Proprietary
Fugu Cyber
Proprietary
Release date
Qwen3.7 Max
2026-05-16
Fugu Cyber
2026-07-21
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
- 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 evidence43 rows
Agentic
Terminal-Bench 2.0
Qwen3.7 Max69.7%SourceFugu Cyber—Not directly comparable
QwenClawBench
Qwen3.7 Max64.3%SourceFugu Cyber—Not directly comparable
Claw-Eval
Qwen3.7 Max65.2%SourceFugu Cyber—Not directly comparable
BFCL v4
Qwen3.7 Max75.0%SourceFugu Cyber—Not directly comparable
MCP Atlas
Qwen3.7 Max76.4%SourceFugu Cyber—Not directly comparable
VITA-Bench
Qwen3.7 Max47.9%SourceFugu Cyber—Not directly comparable
HLE w/ tools
Qwen3.7 Max53.5%SourceFugu Cyber—Not directly comparable
Gert Labs
Qwen3.7 Max64.27%SourceFugu Cyber—Not directly comparable
ResearchClawBench
Qwen3.7 Max18.7%SourceFugu Cyber—Not directly comparable
Terminal-Bench 2.1 (Vals)
Qwen3.7 Max61.0%SourceFugu Cyber—Not directly comparable
CyberGym
Qwen3.7 Max—Fugu Cyber86.9%SourceNot directly comparable
CTI-REALM
Qwen3.7 Max—Fugu Cyber72.1%SourceNot directly comparable
Coding
SWE-bench Verified
Qwen3.7 Max80.4%SourceFugu Cyber—Not directly comparable
SWE-bench Pro
Qwen3.7 Max60.6%SourceFugu Cyber—Not directly comparable
SWE Multilingual
Qwen3.7 Max78.3%SourceFugu Cyber—Not directly comparable
NL2Repo
Qwen3.7 Max47.2%SourceFugu Cyber—Not directly comparable
SciCode
Qwen3.7 Max53.5%SourceFugu Cyber—Not directly comparable
LiveCodeBench
Qwen3.7 Max91.6%SourceFugu Cyber—Not directly comparable
Terminal-Bench 2.0
Qwen3.7 Max69.7%SourceFugu Cyber—Not directly comparable
OpenHarmony Bench
Qwen3.7 Max53.4%SourceFugu Cyber—Not directly comparable
LiveCodeBench (Vals)
Qwen3.7 Max87.1%SourceFugu Cyber—Not directly comparable
SWE-bench (Vals)
Qwen3.7 Max68.8%SourceFugu Cyber—Not directly comparable
Reasoning
Knowledge
GPQA
Qwen3.7 Max92.4%SourceFugu Cyber—Not directly comparable
GPQA-D
Qwen3.7 Max92.4%SourceFugu Cyber—Not directly comparable
HLE
Qwen3.7 Max41.4%SourceFugu Cyber—Not directly comparable
MMLU-Pro
Qwen3.7 Max89.6%SourceFugu Cyber—Not directly comparable
MMLU-Redux
Qwen3.7 Max95%SourceFugu Cyber—Not directly comparable
SuperGPQA
Qwen3.7 Max73.6%SourceFugu Cyber—Not directly comparable
MMMLU
Qwen3.7 Max90.3%SourceFugu Cyber—Not directly comparable
GPQA Diamond (Vals)
Qwen3.7 Max90.2%SourceFugu Cyber—Not directly comparable
MMLU-Pro (Vals)
Qwen3.7 Max89.3%SourceFugu Cyber—Not directly comparable
Math
Multilingual
MMLU-ProX
Qwen3.7 Max87%SourceFugu Cyber—Not directly comparable
NOVA-63
Qwen3.7 Max59.0%SourceFugu Cyber—Not directly comparable
INCLUDE
Qwen3.7 Max86.2%SourceFugu Cyber—Not directly comparable
MAXIFE
Qwen3.7 Max89.2%SourceFugu Cyber—Not directly comparable
PolyMath
Qwen3.7 Max86.5%SourceFugu Cyber—Not directly comparable
Instruction following
Frequently asked questions
Which is better, Qwen3.7 Max or Fugu Cyber?
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, Qwen3.7 Max or Fugu Cyber?
Fugu Cyber is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Qwen3.7 Max or Fugu Cyber?
Fugu Cyber is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Qwen3.7 Max or Fugu Cyber?
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 Fugu Cyber?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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