Agentic
Directional only- Claude Opus 4.5
- 43.2
- Estimated · #98/153
- Claude Opus 4.7 (Adaptive)
- 57.5
- Supported · #32/153
- Basis
- BenchAlign lane · 15 vs 7 public rows
- Reading
- Directional only
Claude Opus 4.5 vs Claude Opus 4.7 (Adaptive)
Updated September 15, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Anthropic
69.39/100
Estimated · Public rank #21
90% interval 50.1–80.9
Decision reading
Claude Opus 4.7 (Adaptive) has the higher public score estimate, 69.39 versus 58.55, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
10 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.
Long documents
Prompts that approach the documented context limit
Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Opus 4.5 and Claude Opus 4.7 (Adaptive) are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Claude Opus 4.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
No clear pick
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
No clear pick
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.
35 Claude Opus 4.5 only
10 Shared
11 Claude Opus 4.7 (Adaptive) only
- Shared results
- 10
- Claude Opus 4.5 only
- 35
- Claude Opus 4.7 (Adaptive) only
- 11
- Like-for-like categories
- 0 / 8
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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
Directional only- Claude Opus 4.5
- 56.7
- Estimated · #36/152
- Claude Opus 4.7 (Adaptive)
- 57.7
- Estimated · #31/152
- Basis
- BenchAlign lane · 5 vs 3 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Opus 4.5
- 54.0
- Estimated · #59/183
- Claude Opus 4.7 (Adaptive)
- 61.2
- Estimated · #33/183
- Basis
- BenchAlign lane · 6 vs 4 public rows
- Reading
- Directional only
Multimodal
Directional only- Claude Opus 4.5
- 23.5
- #46/48
- Claude Opus 4.7 (Adaptive)
- 50.3
- #36/48
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Opus 4.5
- 72.1
- Unranked · 4 rankable rows
- Claude Opus 4.7 (Adaptive)
- 49.7
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.5
- 58.3
- #6/7
- Claude Opus 4.7 (Adaptive)
- Not ranked
- Basis
- Provisional lane · 4 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.5
- 82.9
- #2/12
- Claude Opus 4.7 (Adaptive)
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.5
- 39.5
- #105/123
- Claude Opus 4.7 (Adaptive)
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.5 | Claude Opus 4.7 (Adaptive) | Basis | Reading |
|---|---|---|---|---|
| Agentic | 43.2Estimated · #98/153 | 57.5Supported · #32/153 | Directional onlyBenchAlign lane · 15 vs 7 public rows | Directional only |
| Coding | 56.7Estimated · #36/152 | 57.7Estimated · #31/152 | Directional onlyBenchAlign lane · 5 vs 3 public rows | Directional only |
| Knowledge | 54.0Estimated · #59/183 | 61.2Estimated · #33/183 | Directional onlyBenchAlign lane · 6 vs 4 public rows | Directional only |
| Multimodal | 23.5#46/48 | 50.3#36/48 | Directional onlyProvisional lane · 2 vs 2 weighted rows | Directional only |
| Reasoning | 72.1Unranked · 4 rankable rows | 49.7Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 2 weighted rows | Not comparable |
| Math | 58.3#6/7 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
| Multilingual | 82.9#2/12 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Instruction following | 39.5#105/123 | 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.
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.
HLE
Knowledge
Claude Opus 4.5: 30.8%Claude Opus 4.7 (Adaptive): 54.7%Normalized gap 23.9Claude Opus 4.5 source Claude Opus 4.7 (Adaptive) sourceCharXiv
Multimodal
Claude Opus 4.5: 68.5%Claude Opus 4.7 (Adaptive): 91%Normalized gap 22.5Claude Opus 4.5 source Claude Opus 4.7 (Adaptive) sourceOSWorld-Verified
Agentic
Claude Opus 4.5: 66.3%Claude Opus 4.7 (Adaptive): 78%Normalized gap 11.7Claude Opus 4.5 source Claude Opus 4.7 (Adaptive) sourceTerminal-Bench 2.0
Agentic
Claude Opus 4.5: 59.3%Claude Opus 4.7 (Adaptive): 69.4%Normalized gap 10.1Claude Opus 4.5 source Claude Opus 4.7 (Adaptive) sourceGPQA
Knowledge
Claude Opus 4.5: 87%Claude Opus 4.7 (Adaptive): 94.2%Normalized gap 7.2Claude Opus 4.5 source Claude Opus 4.7 (Adaptive) source
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 4.5
- $0.0175
- Fits in one request
- Claude Opus 4.7 (Adaptive)
- $0.0175
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.5
- $0.325
- Fits in one request
- Claude Opus 4.7 (Adaptive)
- $0.325
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Opus 4.5
- $1.35
- Does not fit in one request
- Cached input priced at the published list-input rate
- Claude Opus 4.7 (Adaptive)
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate. 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 4.5Claude Opus 4.7 (Adaptive)
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.5
200K
Claude Opus 4.7 (Adaptive)
1M
API model ID
Claude Opus 4.5
Not sourced
Claude Opus 4.7 (Adaptive)
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 4.5
Not published
Claude Opus 4.7 (Adaptive)
Not published
Documented inputs
Claude Opus 4.5
Not sourced
Claude Opus 4.7 (Adaptive)
Not sourced
Documented outputs
Claude Opus 4.5
Not sourced
Claude Opus 4.7 (Adaptive)
Not sourced
Provider availability
Claude Opus 4.5
Not sourced
Claude Opus 4.7 (Adaptive)
Not sourced
Reasoning profile
Claude Opus 4.5
Non-Reasoning
Claude Opus 4.7 (Adaptive)
Reasoning
Weight access
Claude Opus 4.5
Proprietary
Claude Opus 4.7 (Adaptive)
Proprietary
License
Claude Opus 4.5
Proprietary
Claude Opus 4.7 (Adaptive)
Proprietary
Release date
Claude Opus 4.5
2025-11-01
Claude Opus 4.7 (Adaptive)
2026-04-16
If you already use one of these models
- Deployment change
- Both entries list Anthropic as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- Claude Opus 4.7 (Adaptive) has the higher public score estimate, 69.39 versus 58.55, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.325 vs $0.325. Cache-heavy agent loop: $1.35 vs $1.35.
- 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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence56 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.559.3%SourceClaude Opus 4.7 (Adaptive)69.4%SourceClaude Opus 4.7 (Adaptive) leads this result
OSWorld-Verified
Claude Opus 4.566.3%SourceClaude Opus 4.7 (Adaptive)78%SourceClaude Opus 4.7 (Adaptive) leads this result
OSWorld
Claude Opus 4.566.3%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
Claw-Eval
Claude Opus 4.559.6%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
QwenClawBench
Claude Opus 4.552.3%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
τ³-bench results
Claude Opus 4.570.2%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
VITA-Bench
Claude Opus 4.523.3%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
DeepPlanning
Claude Opus 4.526.4%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
Toolathlon
Claude Opus 4.543.5%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
MCP Atlas
Claude Opus 4.542.3%SourceClaude Opus 4.7 (Adaptive)77.3%SourceClaude Opus 4.7 (Adaptive) leads this result
MCP-Tasks
Claude Opus 4.571.8%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
WideResearch
Claude Opus 4.576.4%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
CyberGym
Claude Opus 4.550.6%SourceClaude Opus 4.7 (Adaptive)73.1%SourceClaude Opus 4.7 (Adaptive) leads this result
Gert Labs
Claude Opus 4.564.23%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
JobBench
Shared sourceClaude Opus 4.532.3%Claude Opus 4.7 (Adaptive)45.9%Claude Opus 4.7 (Adaptive) leads this result
BrowseComp
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)79.3%SourceNot directly comparable
OSWorld 2.0
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)18.2%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Opus 4.580.9%SourceClaude Opus 4.7 (Adaptive)87.6%SourceClaude Opus 4.7 (Adaptive) leads this result
LiveCodeBench v6
Claude Opus 4.584.8%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
SWE-bench Pro
Claude Opus 4.557.1%SourceClaude Opus 4.7 (Adaptive)64.3%SourceClaude Opus 4.7 (Adaptive) leads this result
SWE Multilingual
Claude Opus 4.577.5%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
NL2Repo
Claude Opus 4.543.2%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)69.4%SourceNot directly comparable
Reasoning
LongBench v2
Claude Opus 4.564.4%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
AI-Needle
Claude Opus 4.574%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
MRCR v2 128K-256K
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)59.2%SourceNot directly comparable
ARC-AGI-2
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)75.8%SourceNot directly comparable
ARC-AGI-3
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)0.2%SourceNot directly comparable
Knowledge
GPQA
Claude Opus 4.587%SourceClaude Opus 4.7 (Adaptive)94.2%SourceClaude Opus 4.7 (Adaptive) leads this result
SuperGPQA
Claude Opus 4.570.6%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
MMLU-Pro
Claude Opus 4.589.5%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
MMLU-Redux
Claude Opus 4.596.6%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
C-Eval
Claude Opus 4.592.2%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
HLE
Claude Opus 4.530.8%SourceClaude Opus 4.7 (Adaptive)54.7%SourceClaude Opus 4.7 (Adaptive) leads this result
GPQA-D
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)94.2%SourceNot directly comparable
HLE w/o tools
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)46.9%SourceNot directly comparable
Math
AIME26
Claude Opus 4.595.1%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
HMMT Feb 2025
Claude Opus 4.592.9%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
HMMT Nov 2025
Claude Opus 4.593.3%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
HMMT Feb 2026
Claude Opus 4.585.3%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
MMAnswerBench
Claude Opus 4.584.0%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.520.690%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.54.167%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
FrontierMath (legacy)
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)43.8%SourceNot directly comparable
Multilingual
Multimodal
MMMU-Pro
Claude Opus 4.570.6%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
MathVision
Claude Opus 4.574.3%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
CharXiv
Claude Opus 4.568.5%SourceClaude Opus 4.7 (Adaptive)91%SourceClaude Opus 4.7 (Adaptive) leads this result
VideoMMMU
Claude Opus 4.584.4%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
ScreenSpot Pro
Claude Opus 4.545.7%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
V*
Claude Opus 4.567.0%SourceClaude Opus 4.7 (Adaptive)—Not directly comparable
OfficeQA Pro
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)43.6%SourceNot directly comparable
CharXiv w/o tools
Claude Opus 4.5—Claude Opus 4.7 (Adaptive)82.1%SourceNot directly comparable
Instruction following
Frequently asked questions
Which is better, Claude Opus 4.5 or Claude Opus 4.7 (Adaptive)?
Claude Opus 4.7 (Adaptive) has the higher public score estimate, 69.39 versus 58.55, 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 4.5 or Claude Opus 4.7 (Adaptive)?
Claude Opus 4.7 (Adaptive) scores higher for coding on the public lane, 57.7 to 56.7. Claude Opus 4.5 and Claude Opus 4.7 (Adaptive) are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Claude Opus 4.5 or Claude Opus 4.7 (Adaptive)?
Claude Opus 4.7 (Adaptive) scores higher for agentic tasks on the public lane, 57.5 to 43.2. Claude Opus 4.5 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 4.5 or Claude Opus 4.7 (Adaptive)?
For the stated presets, chat costs $0.0175 on Claude Opus 4.5 and $0.0175 on Claude Opus 4.7 (Adaptive); repository review costs $0.325 and $0.325; the cache-heavy agent loop costs $1.35 and $1.35. Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate. 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 4.5 or Claude Opus 4.7 (Adaptive)?
Claude Opus 4.7 (Adaptive) has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated September 15, 2026
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