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Model comparison

Claude Opus 4.6 (Adaptive) vs Kimi K2.6

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

Claude Opus 4.6 (Adaptive)

Anthropic

63.8/100

Estimated · Public rank #40

90% interval 52.3–75.4

Kimi K2.6

Moonshot AI

59.7/100

Estimated · Public rank #55

90% interval 49.8–69.5

Claude Opus 4.6 (Adaptive) has the higher public score estimate, 63.84 versus 59.66, 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.

  • 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

    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 (Adaptive) only
0
Kimi K2.6 only
31
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 (Adaptive)
Not measured
Kimi K2.6
73.5
Weighted basis
0 vs 3 rows
Reading
Not comparable

Coding

Not comparable
Claude Opus 4.6 (Adaptive)
Not measured
Kimi K2.6
64.4
Weighted basis
0 vs 3 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 4.6 (Adaptive)
Not measured
Kimi K2.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Opus 4.6 (Adaptive)
Not measured
Kimi K2.6
42.2
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.6 (Adaptive)
Not measured
Kimi K2.6
67.1
Weighted basis
0 vs 4 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.6 (Adaptive)
Not measured
Kimi K2.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.6 (Adaptive)
Not measured
Kimi K2.6
79.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.6 (Adaptive)
Not measured
Kimi K2.6
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 (Adaptive)
API rate not published
Fits in one request
Kimi K2.6
$0.00295
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 (Adaptive)
API rate not published
Fits in one request
Kimi K2.6
$0.0595
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 (Adaptive)
API rate not published
Fits in one request
Cached-input rate unavailable
Kimi K2.6
$0.249
Fits in one request
Cached input priced at the published list-input rate

Kimi K2.6 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 (Adaptive)

1M

Anthropic model overview

Kimi K2.6

256K

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 (Adaptive)

No comparable hosted API rate

Kimi K2.6

Not published

Provider availability

Claude Opus 4.6 (Adaptive)

Generally Available · Claude API

Anthropic model overview

Kimi K2.6

Not sourced

Reasoning profile

Claude Opus 4.6 (Adaptive)

Reasoning

Kimi K2.6

Reasoning

Weight access

Claude Opus 4.6 (Adaptive)

Proprietary

Kimi K2.6

Open Weight

License

Claude Opus 4.6 (Adaptive)

Proprietary

Kimi K2.6

Open Weight

Release date

Claude Opus 4.6 (Adaptive)

2026-02-01

Kimi K2.6

2026-04-20

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 4.6 (Adaptive) has the higher public score estimate, 63.84 versus 59.66, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Claude Opus 4.6 (Adaptive) has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Opus 4.6 (Adaptive)
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.6
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence32 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.666.7%
    Source

    Not directly comparable

  • BrowseComp

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.683.2%
    Source

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.673.1%
    Source

    Not directly comparable

  • Toolathlon

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.650%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.655.9%
    Source

    Not directly comparable

  • Claw-Eval

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.662.3%
    Source

    Not directly comparable

  • DeepSearchQA

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.692.5%
    Source

    Not directly comparable

  • WideResearch

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.680.8%
    Source

    Not directly comparable

  • Gert Labs

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.656.82%
    Source

    Not directly comparable

  • ResearchClawBench

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.618.0%
    Source

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.64.6%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Shared source
    Claude Opus 4.6 (Adaptive)53.50%
    Kimi K2.637.89%

    Claude Opus 4.6 (Adaptive) leads this result

  • SWE-bench Verified

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.680.2%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.689.6%
    Source

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.658.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.676.7%
    Source

    Not directly comparable

  • SciCode

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.652.2%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.666.7%
    Source

    Not directly comparable

  • cursorBench31

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.647.6%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.690.5%
    Source

    Not directly comparable

  • GPQA-D

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.690.5%
    Source

    Not directly comparable

  • HLE

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.634.7%
    Source

    Not directly comparable

Math

  • AIME26

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.696.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.692.7%
    Source

    Not directly comparable

  • MMAnswerBench

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.686.0%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.638.966%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.614.580%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.679.4%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.680.1%
    Source

    Not directly comparable

  • CharXiv

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.680.4%
    Source

    Not directly comparable

  • MathVision

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.687.4%
    Source

    Not directly comparable

  • V*

    Claude Opus 4.6 (Adaptive)—
    Kimi K2.696.9%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.6 (Adaptive) or Kimi K2.6?

Claude Opus 4.6 (Adaptive) has the higher public score estimate, 63.84 versus 59.66, 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 (Adaptive) or Kimi K2.6?

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 (Adaptive) or Kimi K2.6?

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 (Adaptive) or Kimi K2.6?

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 (Adaptive) or Kimi K2.6?

Claude Opus 4.6 (Adaptive) has the larger documented context window: 1M, compared with 256K.

Related comparisons

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Last updated August 11, 2026

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