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BenchLM
Anthropic logo
Model A
Claude Opus 4.7

Anthropic

70.22/100

Supported · Public rank #18

90% interval 63.9–76.6

Claude Opus 4.7 vs Claude Opus 4.7 (Adaptive)

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.

Anthropic logo
Model B
Claude Opus 4.7 (Adaptive)

Anthropic

69.94/100

Estimated · Public rank #23

90% interval 51.6–81.5

Decision reading

Claude Opus 4.7 has the higher public score estimate, 70.22 versus 69.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    Claude Opus 4.7 (Adaptive)

    Claude Opus 4.7 (Adaptive) leads on the public agentic lane, 61 to 58.5, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Claude Opus 4.7 (Adaptive) is scored on Estimated evidence for coding, so the reading is 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

    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

    No clear pick

    A complete comparable API-rate estimate is not available for both models.

    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.

Shared results
1
Claude Opus 4.7 only
12
Claude Opus 4.7 (Adaptive) only
20
Like-for-like categories
1 / 8

2 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.

Agentic

Like-for-like
Claude Opus 4.7
58.5
Supported · #32/151
Claude Opus 4.7 (Adaptive)
61.0
Supported · #21/151
Basis
BenchAlign lane · 4 vs 7 public rows
Reading
Claude Opus 4.7 (Adaptive) leads · intervals overlap

Coding

Directional only
Claude Opus 4.7
62.7
Supported · #15/183
Claude Opus 4.7 (Adaptive)
59.6
Estimated · #27/183
Basis
BenchAlign lane · 5 vs 3 public rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 4.7
64.9
Estimated · #26/181
Claude Opus 4.7 (Adaptive)
61.9
Estimated · #31/181
Basis
BenchAlign lane · 2 vs 4 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 4.7
Not ranked
Claude Opus 4.7 (Adaptive)
49.7
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.7
60.8
Unranked · 2 rankable rows
Claude Opus 4.7 (Adaptive)
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.7
Not ranked
Claude Opus 4.7 (Adaptive)
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.7
Not ranked
Claude Opus 4.7 (Adaptive)
50.3
#36/48
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.7
Not ranked
Claude Opus 4.7 (Adaptive)
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
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.

  • OSWorld 2.0

    Agentic

    Claude Opus 4.7: 13.9%Claude Opus 4.7 (Adaptive): 18.2%Normalized gap 4.3Shared 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 4.7
$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 4.7
$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 4.7
$1.35
Fits 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

Modeled costs are equal

Claude Opus 4.7 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.

Context window

Maximum documented context; output-token limits may be lower.

Claude Opus 4.7

1M

Anthropic model overview

Claude Opus 4.7 (Adaptive)

1M

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

Not published

Claude Opus 4.7 (Adaptive)

Not published

Provider availability

Claude Opus 4.7

Generally Available · Claude API

Anthropic model overview

Claude Opus 4.7 (Adaptive)

Not sourced

Reasoning profile

Claude Opus 4.7

Non-Reasoning

Claude Opus 4.7 (Adaptive)

Reasoning

Weight access

Claude Opus 4.7

Proprietary

Claude Opus 4.7 (Adaptive)

Proprietary

License

Claude Opus 4.7

Proprietary

Claude Opus 4.7 (Adaptive)

Proprietary

Release date

Claude Opus 4.7

2026-04-16

Claude Opus 4.7 (Adaptive)

2026-04-16

If you are choosing between sibling variants
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 has the higher public score estimate, 70.22 versus 69.94, 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
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 evidence33 rows

Agentic

  • Gert Labs

    Claude Opus 4.765.59%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • ResearchClawBench

    Claude Opus 4.720.7%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • OSWorld 2.0

    Shared source
    Claude Opus 4.713.9%
    Claude Opus 4.7 (Adaptive)18.2%

    Claude Opus 4.7 (Adaptive) leads this result

  • Terminal-Bench 2.1 (Vals)

    Claude Opus 4.768.5%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)69.4%
    Source

    Not directly comparable

  • BrowseComp

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)79.3%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)77.3%
    Source

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)78%
    Source

    Not directly comparable

  • CyberGym

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)73.1%
    Source

    Not directly comparable

  • JobBench

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)45.9%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Claude Opus 4.771.00%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • React Native Evals

    Claude Opus 4.782.8%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Opus 4.738.5%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Opus 4.785.1%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Opus 4.782.0%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • SWE-bench Verified

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)87.6%
    Source

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)64.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)69.4%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 128K-256K

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)59.2%
    Source

    Not directly comparable

  • ARC-AGI-2

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)75.8%
    Source

    Not directly comparable

  • ARC-AGI-3

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)0.2%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Claude Opus 4.790.2%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Opus 4.789.9%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • GPQA

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)94.2%
    Source

    Not directly comparable

  • GPQA-D

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)94.2%
    Source

    Not directly comparable

  • HLE

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)54.7%
    Source

    Not directly comparable

  • HLE w/o tools

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)46.9%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 4.743.793%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 4.722.917%
    Source
    Claude Opus 4.7 (Adaptive)—

    Not directly comparable

  • FrontierMath (legacy)

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)43.8%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)43.6%
    Source

    Not directly comparable

  • CharXiv

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)91%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Claude Opus 4.7—
    Claude Opus 4.7 (Adaptive)82.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.7 or Claude Opus 4.7 (Adaptive)?

Claude Opus 4.7 has the higher public score estimate, 70.22 versus 69.94, 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 or Claude Opus 4.7 (Adaptive)?

Claude Opus 4.7 scores higher for coding on the public lane, 62.7 to 59.6. Claude Opus 4.7 (Adaptive) is 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 4.7 or Claude Opus 4.7 (Adaptive)?

Claude Opus 4.7 (Adaptive) leads the public agentic tasks lane, 61 to 58.5, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Claude Opus 4.7 or Claude Opus 4.7 (Adaptive)?

For the stated presets, chat costs $0.0175 on Claude Opus 4.7 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 4.7 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 4.7 or Claude Opus 4.7 (Adaptive)?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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