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Model comparison

Claude 4.1 Opus vs Claude Opus 4.6 (Adaptive)

Updated August 11, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Claude 4.1 Opus

Anthropic

45.0/100

Supported · Public rank #155

90% interval 42.1–48.0

Claude Opus 4.6 (Adaptive)

Anthropic

63.8/100

Estimated · Public rank #40

90% interval 52.3–75.4

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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.

  • Long documents

    Prompts that approach the documented context limit

    Claude Opus 4.6 (Adaptive)

    Claude Opus 4.6 (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

    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

  • 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude 4.1 Opus does not fit this workload in one request. Claude 4.1 Opus 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.

    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
0
Claude 4.1 Opus only
2
Claude Opus 4.6 (Adaptive) only
1
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 4.1 Opus
Not measured
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Claude 4.1 Opus
74.5
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.6 (Adaptive)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude 4.1 Opus
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 4.1 Opus
$0.0525
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 4.1 Opus
$0.975
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 4.1 Opus
$4.05
Does not fit 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 4.1 Opus does not fit this workload in one request. Claude 4.1 Opus 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 4.1 Opus

200K

Claude Opus 4.6 (Adaptive)

Cached-input rate

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

Claude 4.1 Opus

Not published

Claude Opus 4.6 (Adaptive)

No comparable hosted API rate

Provider availability

Claude 4.1 Opus

Not sourced

Claude Opus 4.6 (Adaptive)

Generally Available · Claude API

Anthropic model overview

Reasoning profile

Claude 4.1 Opus

Non-Reasoning

Claude Opus 4.6 (Adaptive)

Reasoning

Weight access

Claude 4.1 Opus

Proprietary

Claude Opus 4.6 (Adaptive)

Proprietary

License

Claude 4.1 Opus

Proprietary

Claude Opus 4.6 (Adaptive)

Proprietary

Release date

Claude 4.1 Opus

2025-08-01

Claude Opus 4.6 (Adaptive)

2026-02-01

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Claude Opus 4.6 (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 evidence3 rows

Agentic

  • JobBench

    Claude 4.1 Opus21.9%
    Source
    Claude Opus 4.6 (Adaptive)

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude 4.1 Opus74.5%
    Source
    Claude Opus 4.6 (Adaptive)

    Not directly comparable

  • Vibe Code Bench

    Claude 4.1 Opus
    Claude Opus 4.6 (Adaptive)53.50%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude 4.1 Opus or Claude Opus 4.6 (Adaptive)?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude 4.1 Opus 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 4.1 Opus 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 4.1 Opus 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 4.1 Opus or Claude Opus 4.6 (Adaptive)?

Claude Opus 4.6 (Adaptive) has the larger documented context window: 1M, compared with 200K.

Related comparisons

Last updated August 11, 2026

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