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Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) has the larger documented context window.
Confidence: documented
Claude Opus 4.7 (Adaptive) vs dots3-note Preview
Updated September 28, 2026. Rank says Claude Opus 4.7 (Adaptive) is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
Claude Opus 4.7 (Adaptive) has the higher public score estimate, 68.53 versus 62.64, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Model A
Anthropic
68.53/100
Estimated · Public rank #19
90% interval 57.0–80.0
- Shared results
- 6
- Claude Opus 4.7 (Adaptive) only
- 15
- dots3-note Preview only
- 25
- Like-for-like categories
- 0 / 8
Estimated: Claude Opus 4.7 (Adaptive) and dots3-note PreviewHow the comparison works
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Dots3-note Preview is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Dots3-note Preview is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat 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-ratesCache-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-fallbackRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
57.2Claude Opus 4.7 (Adaptive)—dots3-note Preview
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
15 Claude Opus 4.7 (Adaptive) only
6 Shared
25 dots3-note Preview only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
SWE-bench VerifiedCoding
Normalized gap 9.2Claude Opus 4.7 (Adaptive):87.6%dots3-note Preview:78.4%ARC-AGI-2Reasoning
Normalized gap 5.6Claude Opus 4.7 (Adaptive):75.8%dots3-note Preview:81.4%BrowseCompAgentic
Normalized gap 4.0Claude Opus 4.7 (Adaptive):79.3%dots3-note Preview:83.3%SWE-bench ProCoding
Normalized gap 3.3Claude Opus 4.7 (Adaptive):64.3%dots3-note Preview:61%HLEKnowledge
Normalized gap 2.1Claude Opus 4.7 (Adaptive):54.7%dots3-note Preview:52.6%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Agentic
Directional only- Claude Opus 4.7 (Adaptive)
- 61.4
- Supported · #17/117
- dots3-note Preview
- 56.4
- Estimated · #28/117
- Basis
- BenchAlign v5.7 lane · 7 vs 9 public rows
- Reading
- Directional only
Coding
Not comparable- Claude Opus 4.7 (Adaptive)
- 57.2
- Estimated · #23/142
- dots3-note Preview
- Not ranked
- Basis
- BenchAlign v5.7 lane · 3 vs 7 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Opus 4.7 (Adaptive)
- 53.6
- Unranked · 3 rankable rows
- dots3-note Preview
- 72.9
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.7 (Adaptive)
- 50.1
- #39/50
- dots3-note Preview
- 67.2
- Unranked · 10 rankable rows
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Opus 4.7 (Adaptive)
- 64.1
- Estimated · #30/168
- dots3-note Preview
- Not ranked
- Basis
- BenchAlign v5.7 lane · 4 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.7 (Adaptive)
- Not ranked
- dots3-note Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.7 (Adaptive)
- Not ranked
- dots3-note Preview
- 85.2
- #37/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.7 (Adaptive)
- Not ranked
- dots3-note Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.7 (Adaptive) | dots3-note Preview | Basis | Reading |
|---|---|---|---|---|
| Agentic | 61.4Supported · #17/117 | 56.4Estimated · #28/117 | Directional onlyBenchAlign v5.7 lane · 7 vs 9 public rows | Directional only |
| Coding | 57.2Estimated · #23/142 | Not ranked | Not comparableBenchAlign v5.7 lane · 3 vs 7 public rows | Not comparable |
| Reasoning | 53.6Unranked · 3 rankable rows | 72.9Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Multimodal | 50.1#39/50 | 67.2Unranked · 10 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Knowledge | 64.1Estimated · #30/168 | Not ranked | Not comparableBenchAlign v5.7 lane · 4 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 85.2#37/124 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 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 v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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
- dots3-note Preview
- Self-hosted; infrastructure cost varies
- Fits in one request
dots3-note Preview has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.7 (Adaptive)
- $0.325
- Fits in one request
- dots3-note Preview
- Self-hosted; infrastructure cost varies
- Fits in one request
dots3-note Preview has no comparable published API token rate.
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
- dots3-note Preview
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate. dots3-note Preview has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
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)dots3-note Preview
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.7 (Adaptive)
1M
dots3-note Preview
API model ID
Claude Opus 4.7 (Adaptive)
Not sourced
dots3-note Preview
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
dots3-note Preview
No comparable hosted API rate
dots3-note Preview model cardDocumented inputs
Claude Opus 4.7 (Adaptive)
Not sourced
dots3-note Preview
Not sourced
Documented outputs
Claude Opus 4.7 (Adaptive)
Not sourced
dots3-note Preview
Not sourced
Provider availability
Claude Opus 4.7 (Adaptive)
Not sourced
dots3-note Preview
Not sourced
Reasoning profile
Claude Opus 4.7 (Adaptive)
Reasoning
dots3-note Preview
Reasoning
Weight access
Claude Opus 4.7 (Adaptive)
Proprietary
dots3-note Preview
Open Weight
License
Claude Opus 4.7 (Adaptive)
Proprietary
dots3-note Preview
Open Weight
Release date
Claude Opus 4.7 (Adaptive)
2026-04-16
dots3-note Preview
2026-08-14
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
- Claude Opus 4.7 (Adaptive) has the higher public score estimate, 68.53 versus 62.64, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Claude Opus 4.7 (Adaptive) has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Opus 4.7 (Adaptive) or dots3-note Preview?
Claude Opus 4.7 (Adaptive) has the higher public score estimate, 68.53 versus 62.64, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Opus 4.7 (Adaptive) or dots3-note Preview?
Dots3-note Preview 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 dots3-note Preview?
Claude Opus 4.7 (Adaptive) scores higher for agentic tasks on the public lane, 61.4 to 56.4. Dots3-note Preview is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Claude Opus 4.7 (Adaptive) or dots3-note Preview?
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, Claude Opus 4.7 (Adaptive) or dots3-note Preview?
Claude Opus 4.7 (Adaptive) has the larger documented context window: 1M, compared with 512K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence46 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.7 (Adaptive)69.4%Sourcedots3-note Preview—Not directly comparable
BrowseComp
Claude Opus 4.7 (Adaptive)79.3%Sourcedots3-note Preview83.3%Sourcedots3-note Preview leads this result
MCP Atlas
Claude Opus 4.7 (Adaptive)77.3%Sourcedots3-note Preview—Not directly comparable
OSWorld-Verified
Claude Opus 4.7 (Adaptive)78%Sourcedots3-note Preview—Not directly comparable
CyberGym
Claude Opus 4.7 (Adaptive)73.1%Sourcedots3-note Preview—Not directly comparable
OSWorld 2.0
Claude Opus 4.7 (Adaptive)18.2%Sourcedots3-note Preview—Not directly comparable
JobBench
Claude Opus 4.7 (Adaptive)45.9%Sourcedots3-note Preview—Not directly comparable
Claw-Eval
Claude Opus 4.7 (Adaptive)—dots3-note Preview73.4%SourceNot directly comparable
Terminal-Bench 2.1
Claude Opus 4.7 (Adaptive)—dots3-note Preview75.1%SourceNot directly comparable
Toolathlon-Verified
Claude Opus 4.7 (Adaptive)—dots3-note Preview55.6%SourceNot directly comparable
skillsBench
Claude Opus 4.7 (Adaptive)—dots3-note Preview52.8%SourceNot directly comparable
APEX-Agents
Claude Opus 4.7 (Adaptive)—dots3-note Preview30.8%SourceNot directly comparable
HLE w/ tools
Claude Opus 4.7 (Adaptive)—dots3-note Preview52.6%SourceNot directly comparable
DeepSearchQA
Claude Opus 4.7 (Adaptive)—dots3-note Preview92.1%SourceNot directly comparable
WideResearch
Claude Opus 4.7 (Adaptive)—dots3-note Preview78.9%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Opus 4.7 (Adaptive)87.6%Sourcedots3-note Preview78.4%SourceClaude Opus 4.7 (Adaptive) leads this result
SWE-bench Pro
Claude Opus 4.7 (Adaptive)64.3%Sourcedots3-note Preview61%SourceClaude Opus 4.7 (Adaptive) leads this result
Terminal-Bench 2.0
Claude Opus 4.7 (Adaptive)69.4%Sourcedots3-note Preview—Not directly comparable
Codeforces
Claude Opus 4.7 (Adaptive)—dots3-note Preview3056.0SourceNot directly comparable
LiveCodeBench v6
Claude Opus 4.7 (Adaptive)—dots3-note Preview91.5%SourceNot directly comparable
Terminal-Bench 2.1
Claude Opus 4.7 (Adaptive)—dots3-note Preview75.1%SourceNot directly comparable
SWE Multilingual
Claude Opus 4.7 (Adaptive)—dots3-note Preview75.7%SourceNot directly comparable
NL2Repo
Claude Opus 4.7 (Adaptive)—dots3-note Preview49.8%SourceNot directly comparable
Reasoning
Multimodal
OfficeQA Pro
Claude Opus 4.7 (Adaptive)43.6%Sourcedots3-note Preview—Not directly comparable
CharXiv
Claude Opus 4.7 (Adaptive)91%Sourcedots3-note Preview—Not directly comparable
CharXiv w/o tools
Claude Opus 4.7 (Adaptive)82.1%Sourcedots3-note Preview83.1%Sourcedots3-note Preview leads this result
SimpleVQA
Claude Opus 4.7 (Adaptive)—dots3-note Preview72.5%SourceNot directly comparable
MMMU-Pro
Claude Opus 4.7 (Adaptive)—dots3-note Preview79.1%SourceNot directly comparable
MathVision
Claude Opus 4.7 (Adaptive)—dots3-note Preview87.7%SourceNot directly comparable
ZeroBench
Claude Opus 4.7 (Adaptive)—dots3-note Preview19.0%SourceNot directly comparable
GDP.pdf (no tools)
Claude Opus 4.7 (Adaptive)—dots3-note Preview60.7%SourceNot directly comparable
PerceptionBench
Claude Opus 4.7 (Adaptive)—dots3-note Preview53.4%SourceNot directly comparable
BabyVision
Claude Opus 4.7 (Adaptive)—dots3-note Preview50.0%SourceNot directly comparable
MMVU
Claude Opus 4.7 (Adaptive)—dots3-note Preview79.9%SourceNot directly comparable
VideoMMMU
Claude Opus 4.7 (Adaptive)—dots3-note Preview86.8%SourceNot directly comparable
Knowledge
GPQA
Claude Opus 4.7 (Adaptive)94.2%Sourcedots3-note Preview—Not directly comparable
GPQA-D
Claude Opus 4.7 (Adaptive)94.2%Sourcedots3-note Preview—Not directly comparable
HLE
Claude Opus 4.7 (Adaptive)54.7%Sourcedots3-note Preview52.6%SourceClaude Opus 4.7 (Adaptive) leads this result
HLE w/o tools
Claude Opus 4.7 (Adaptive)46.9%Sourcedots3-note Preview—Not directly comparable
Instruction following
Math
46 public results · 6 shared
Watch Claude Opus 4.7 (Adaptive) vs dots3-note Preview
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Last updated September 28, 2026