Agentic
Not comparable- Claude Opus 4.7 (Adaptive)
- 61.0
- Supported · #21/151
- Fugu Cyber
- Not ranked
- Basis
- BenchAlign lane · 7 vs 2 public rows
- Reading
- Not comparable
Anthropic
69.94/100
Estimated · Public rank #23
90% interval 51.6–81.5
Claude Opus 4.7 (Adaptive) vs Fugu Cyber
Updated September 4, 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 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.
Chat turn cost
1K fresh input + 500 output tokens
Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Fugu Cyber
Fugu Cyber has the lower estimated token cost for this stated workload. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
20 Claude Opus 4.7 (Adaptive) only
1 Shared
1 Fugu Cyber only
- Shared results
- 1
- Claude Opus 4.7 (Adaptive) only
- 20
- Fugu Cyber only
- 1
- 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- Claude Opus 4.7 (Adaptive)
- 59.6
- Estimated · #27/183
- Fugu Cyber
- Not ranked
- Basis
- BenchAlign lane · 3 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Opus 4.7 (Adaptive)
- 49.7
- Unranked · 3 rankable rows
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Opus 4.7 (Adaptive)
- 61.8
- Estimated · #31/181
- Fugu Cyber
- Not ranked
- Basis
- BenchAlign lane · 4 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.7 (Adaptive)
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.7 (Adaptive)
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.7 (Adaptive)
- 50.0
- #36/48
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.7 (Adaptive)
- Not ranked
- Fugu Cyber
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.7 (Adaptive) | Fugu Cyber | Basis | Reading |
|---|---|---|---|---|
| Agentic | 61.0Supported · #21/151 | Not ranked | Not comparableBenchAlign lane · 7 vs 2 public rows | Not comparable |
| Coding | 59.6Estimated · #27/183 | Not ranked | Not comparableBenchAlign lane · 3 vs 0 public rows | Not comparable |
| Reasoning | 49.7Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Knowledge | 61.8Estimated · #31/181 | Not ranked | Not comparableBenchAlign lane · 4 vs 0 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 50.0#36/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 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
- Claude Opus 4.7 (Adaptive)
- $0.0175
- Fits in one request
- Fugu Cyber
- $0.024
- Fits in one request
Claude Opus 4.7 (Adaptive) has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.7 (Adaptive)
- $0.325
- Fits in one request
- Fugu Cyber
- $0.408
- Fits in one request
Claude Opus 4.7 (Adaptive) has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Opus 4.7 (Adaptive)
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- Fugu Cyber
- $0.6
- Fits in one request
Fugu Cyber has the lower modeled cost
Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.7 (Adaptive)Fugu Cyber
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.7 (Adaptive)
1M
Fugu Cyber
1M
API model ID
Claude Opus 4.7 (Adaptive)
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.
Claude Opus 4.7 (Adaptive)
Not published
Fugu Cyber
$0.6 per 1M cached input tokens
Documented inputs
Claude Opus 4.7 (Adaptive)
Not sourced
Fugu Cyber
Not sourced
Documented outputs
Claude Opus 4.7 (Adaptive)
Not sourced
Fugu Cyber
Not sourced
Provider availability
Claude Opus 4.7 (Adaptive)
Not sourced
Fugu Cyber
Not sourced
Reasoning profile
Claude Opus 4.7 (Adaptive)
Reasoning
Fugu Cyber
Reasoning
Weight access
Claude Opus 4.7 (Adaptive)
Proprietary
Fugu Cyber
Proprietary
License
Claude Opus 4.7 (Adaptive)
Proprietary
Fugu Cyber
Proprietary
Release date
Claude Opus 4.7 (Adaptive)
2026-04-16
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
- 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
- Repository review: $0.325 vs $0.408. Cache-heavy agent loop: $1.35 vs $0.6.
- 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 evidence22 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.7 (Adaptive)69.4%SourceFugu Cyber—Not directly comparable
BrowseComp
Claude Opus 4.7 (Adaptive)79.3%SourceFugu Cyber—Not directly comparable
MCP Atlas
Claude Opus 4.7 (Adaptive)77.3%SourceFugu Cyber—Not directly comparable
OSWorld-Verified
Claude Opus 4.7 (Adaptive)78%SourceFugu Cyber—Not directly comparable
CyberGym
Claude Opus 4.7 (Adaptive)73.1%SourceFugu Cyber86.9%SourceFugu Cyber leads this result
OSWorld 2.0
Claude Opus 4.7 (Adaptive)18.2%SourceFugu Cyber—Not directly comparable
JobBench
Claude Opus 4.7 (Adaptive)45.9%SourceFugu Cyber—Not directly comparable
CTI-REALM
Claude Opus 4.7 (Adaptive)—Fugu Cyber72.1%SourceNot directly comparable
Coding
Reasoning
Knowledge
GPQA
Claude Opus 4.7 (Adaptive)94.2%SourceFugu Cyber—Not directly comparable
GPQA-D
Claude Opus 4.7 (Adaptive)94.2%SourceFugu Cyber—Not directly comparable
HLE
Claude Opus 4.7 (Adaptive)54.7%SourceFugu Cyber—Not directly comparable
HLE w/o tools
Claude Opus 4.7 (Adaptive)46.9%SourceFugu Cyber—Not directly comparable
Math
FrontierMath (legacy)
Claude Opus 4.7 (Adaptive)43.8%SourceFugu Cyber—Not directly comparable
Multimodal
Frequently asked questions
Which is better, Claude Opus 4.7 (Adaptive) or Fugu Cyber?
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, Claude Opus 4.7 (Adaptive) 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, Claude Opus 4.7 (Adaptive) 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, Claude Opus 4.7 (Adaptive) or Fugu Cyber?
For the stated presets, chat costs $0.0175 on Claude Opus 4.7 (Adaptive) and $0.024 on Fugu Cyber; repository review costs $0.325 and $0.408; the cache-heavy agent loop costs $1.35 and $0.6. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.7 (Adaptive) or Fugu Cyber?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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