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Model comparison

Claude Opus 4.6 vs Claude Opus 4.6 (Adaptive)

Updated July 29, 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.

Claude Opus 4.6

Anthropic

67.8/100

Supported · Public rank #20

90% interval 54.9–80.7

Claude Opus 4.6 (Adaptive)

Anthropic

63.4/100

Estimated · Public rank #38

90% interval 51.9–75.0

Claude Opus 4.6 has the higher public score estimate, 67.76 versus 63.44, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    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: rate-fallback

  • 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.

Shared results
1
Claude Opus 4.6 only
32
Claude Opus 4.6 (Adaptive) only
0
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
Claude Opus 4.6
73.0
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Claude Opus 4.6
68.1
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 4.6
Not measured
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Opus 4.6
69.1
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.6
36.3
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.6
Not measured
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.6
77.3
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.6
Not measured
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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 4.6
$0.0175
Fits in one request
Claude Opus 4.6 (Adaptive)
API rate not published
Fits in one request

Claude Opus 4.6 (Adaptive) has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.6
$0.325
Fits in one request
Claude Opus 4.6 (Adaptive)
API rate not published
Fits in one request

Claude Opus 4.6 (Adaptive) has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Opus 4.6
$1.35
Fits in one request
Cached input priced at the published list-input rate
Claude Opus 4.6 (Adaptive)
API rate not published
Fits in one request
Cached-input rate unavailable

Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.6 (Adaptive) 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.

Context window

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

Claude Opus 4.6

1M

Claude Opus 4.6 (Adaptive)

1M

Anthropic model overview

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Opus 4.6

Not published

Claude Opus 4.6 (Adaptive)

No comparable hosted API rate

Provider availability

Claude Opus 4.6

Not sourced

Claude Opus 4.6 (Adaptive)

Generally Available · Claude API

Anthropic model overview

Reasoning profile

Claude Opus 4.6

Non-Reasoning

Claude Opus 4.6 (Adaptive)

Reasoning

Weight access

Claude Opus 4.6

Proprietary

Claude Opus 4.6 (Adaptive)

Proprietary

License

Claude Opus 4.6

Proprietary

Claude Opus 4.6 (Adaptive)

Proprietary

Release date

Claude Opus 4.6

2026-02-01

Claude Opus 4.6 (Adaptive)

2026-02-01

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 4.6 has the higher public score estimate, 67.76 versus 63.44, but the 90% score intervals overlap.
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 evidence33 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.665.4%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • BrowseComp

    Claude Opus 4.683.7%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.672.7%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • Claw-Eval

    Claude Opus 4.670.4%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • DeepSearchQA

    Claude Opus 4.673.7%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • CyberGym

    Claude Opus 4.666.6%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • Gert Labs

    Claude Opus 4.661.85%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • ResearchClawBench

    Claude Opus 4.619.9%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • JobBench

    Claude Opus 4.636.7%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.680.8%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • SWE-bench Verified*

    Claude Opus 4.675.6%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • LiveCodeBench Pro

    Claude Opus 4.670.7%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.653.4%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • SWE-Rebench

    Claude Opus 4.665.3%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • React Native Evals

    Claude Opus 4.684.1%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    Claude Opus 4.657.57%
    Claude Opus 4.6 (Adaptive)53.50%

    Claude Opus 4.6 leads this result

  • FrontierCode 1.1 Main

    Claude Opus 4.626.9%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.691.3%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • GPQA-D

    Claude Opus 4.689.2%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • SuperGPQA

    Claude Opus 4.695%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • MMLU-Pro

    Claude Opus 4.682%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • MMLU-Pro (Arcee)

    Claude Opus 4.689.1%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • HLE

    Claude Opus 4.653%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • HLE w/o tools

    Claude Opus 4.640%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • HealthBench Hard

    Claude Opus 4.614.8%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • MedXpertQA (Text)

    Claude Opus 4.652.1%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

Math

  • AIME25 (Arcee)

    Claude Opus 4.699.8%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 4.640.700%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 4.622.900%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Claude Opus 4.677.3%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • ERQA

    Claude Opus 4.651.6%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • ScreenSpot Pro

    Claude Opus 4.683.1%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

  • MedXpertQA (MM)

    Claude Opus 4.664.8%
    Source
    Claude Opus 4.6 (Adaptive)—

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.6 or Claude Opus 4.6 (Adaptive)?

Claude Opus 4.6 has the higher public score estimate, 67.76 versus 63.44, 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 4.6 or Claude Opus 4.6 (Adaptive)?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Claude Opus 4.6 or Claude Opus 4.6 (Adaptive)?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Claude Opus 4.6 or Claude Opus 4.6 (Adaptive)?

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 4.6 or Claude Opus 4.6 (Adaptive)?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated July 29, 2026

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