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BenchLM
Anthropic logo
Model A
Claude Opus 4.8

Anthropic

76.45/100

Supported · Public rank #8

90% interval 73.8–79.1

Claude Opus 4.8 vs Kimi K2.6

Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Moonshot AI logo
Model B
Kimi K2.6

Moonshot AI

60.14/100

Estimated · Public rank #66

90% interval 50.3–70.0

Decision reading

Claude Opus 4.8 has the higher public score, 76.45 versus 60.14, and the 90% score intervals do not overlap.

20 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.

  • Agentic work

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

    Claude Opus 4.8

    Claude Opus 4.8 leads on the same 3 weighted benchmark rows.

    Confidence: stronger

  • Long documents

    Prompts that approach the documented context limit

    Claude Opus 4.8

    Claude Opus 4.8 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Kimi K2.6

    Kimi K2.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Cache-heavy agent loop cost

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

    Kimi K2.6

    Kimi K2.6 has the lower estimated token cost for this stated workload. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.6 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

    Kimi K2.6

    Kimi K2.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

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
20
Claude Opus 4.8 only
13
Kimi K2.6 only
12
Like-for-like categories
2 / 8

3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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

Like-for-like
Claude Opus 4.8
80.3
Kimi K2.6
73.5
Weighted basis
3 vs 3 rows
Reading
Claude Opus 4.8 leads

Knowledge

Like-for-like
Claude Opus 4.8
62.7
Kimi K2.6
42.2
Weighted basis
2 vs 2 rows
Reading
Claude Opus 4.8 leads

Coding

Directional only
Claude Opus 4.8
81.1
Kimi K2.6
64.4
Weighted basis
2 vs 3 rows
Reading
Directional only

Math

Directional only
Claude Opus 4.8
53.9
Kimi K2.6
67.1
Weighted basis
3 vs 4 rows
Reading
Directional only

Multimodal

Directional only
Claude Opus 4.8
77.0
Kimi K2.6
79.8
Weighted basis
2 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 4.8
72.1
Kimi K2.6
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.8
Not measured
Kimi K2.6
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.8
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.

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.

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 4.8
$0.0175
Fits in one request
Kimi K2.6
$0.00295
Fits in one request

Kimi K2.6 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.8
$0.325
Fits in one request
Kimi K2.6
$0.0595
Fits in one request

Kimi K2.6 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Opus 4.8
$1.35
Fits in one request
Cached input priced at the published list-input rate
Kimi K2.6
$0.249
Fits in one request
Cached input priced at the published list-input rate

Kimi K2.6 has the lower modeled cost

Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.6 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 4.8

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 4.8

Not published

Kimi K2.6

Not published

Reasoning profile

Claude Opus 4.8

Reasoning

Kimi K2.6

Reasoning

Weight access

Claude Opus 4.8

Proprietary

Kimi K2.6

Open Weight

License

Claude Opus 4.8

Proprietary

Kimi K2.6

Open Weight

Release date

Claude Opus 4.8

2026-05-28

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 4.8 has the higher public score, 76.45 versus 60.14, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.325 vs $0.0595. Cache-heavy agent loop: $1.35 vs $0.249.
Context tradeoff
Claude Opus 4.8 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 4.8
API / mo$22,500
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 evidence45 rows

Agentic

  • Terminal-Bench 3.0

    Claude Opus 4.821.1%
    Source
    Kimi K2.6—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.874.6%
    Source
    Kimi K2.666.7%
    Source

    Claude Opus 4.8 leads this result

  • BrowseComp

    Claude Opus 4.884.3%
    Source
    Kimi K2.683.2%
    Source

    Claude Opus 4.8 leads this result

  • DeepSearchQA

    Claude Opus 4.893.1%
    Source
    Kimi K2.692.5%
    Source

    Claude Opus 4.8 leads this result

  • OSWorld-Verified

    Claude Opus 4.883.4%
    Source
    Kimi K2.673.1%
    Source

    Claude Opus 4.8 leads this result

  • Finance Agent v2

    Claude Opus 4.853.9%
    Source
    Kimi K2.6—

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.882.2%
    Source
    Kimi K2.655.9%
    Source

    Claude Opus 4.8 leads this result

  • Toolathlon

    Claude Opus 4.859.9%
    Source
    Kimi K2.650%
    Source

    Claude Opus 4.8 leads this result

  • Gert Labs

    Shared source
    Claude Opus 4.872.97%
    Kimi K2.656.82%

    Claude Opus 4.8 leads this result

  • ResearchClawBench

    Shared source
    Claude Opus 4.821.1%
    Kimi K2.618.0%

    Claude Opus 4.8 leads this result

  • OSWorld 2.0

    Shared source
    Claude Opus 4.820.6%
    Kimi K2.64.6%

    Claude Opus 4.8 leads this result

  • Claw-Eval

    Claude Opus 4.8—
    Kimi K2.662.3%
    Source

    Not directly comparable

  • WideResearch

    Claude Opus 4.8—
    Kimi K2.680.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.888.6%
    Source
    Kimi K2.680.2%
    Source

    Claude Opus 4.8 leads this result

  • SWE-bench Pro

    Claude Opus 4.869.2%
    Source
    Kimi K2.658.6%
    Source

    Claude Opus 4.8 leads this result

  • SWE Multilingual

    Claude Opus 4.884.4%
    Source
    Kimi K2.676.7%
    Source

    Claude Opus 4.8 leads this result

  • SWE Multimodal

    Claude Opus 4.838.4%
    Source
    Kimi K2.6—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.874.6%
    Source
    Kimi K2.666.7%
    Source

    Claude Opus 4.8 leads this result

  • cursorBench31

    Shared source
    Claude Opus 4.858.4%
    Kimi K2.647.6%

    Claude Opus 4.8 leads this result

  • cursorBench32

    Claude Opus 4.862.3%
    Source
    Kimi K2.6—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Opus 4.846.5%
    Source
    Kimi K2.6—

    Not directly comparable

  • LiveCodeBench v6

    Claude Opus 4.8—
    Kimi K2.689.6%
    Source

    Not directly comparable

  • SciCode

    Claude Opus 4.8—
    Kimi K2.652.2%
    Source

    Not directly comparable

  • Vibe Code Bench

    Claude Opus 4.8—
    Kimi K2.637.89%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Claude Opus 4.872.1%
    Source
    Kimi K2.6—

    Not directly comparable

  • ARC-AGI-3

    Claude Opus 4.81.5%
    Source
    Kimi K2.6—

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.893.6%
    Source
    Kimi K2.690.5%
    Source

    Claude Opus 4.8 leads this result

  • GPQA-D

    Claude Opus 4.893.6%
    Source
    Kimi K2.690.5%
    Source

    Claude Opus 4.8 leads this result

  • HLE

    Claude Opus 4.857.9%
    Source
    Kimi K2.634.7%
    Source

    Claude Opus 4.8 leads this result

  • HLE w/o tools

    Claude Opus 4.849.8%
    Source
    Kimi K2.6—

    Not directly comparable

Math

  • USAMO 2026

    Claude Opus 4.896.7%
    Source
    Kimi K2.6—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Claude Opus 4.847.241%
    Kimi K2.638.966%

    Claude Opus 4.8 leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Claude Opus 4.831.250%
    Kimi K2.614.580%

    Claude Opus 4.8 leads this result

  • AIME26

    Claude Opus 4.8—
    Kimi K2.696.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude Opus 4.8—
    Kimi K2.692.7%
    Source

    Not directly comparable

  • MMAnswerBench

    Claude Opus 4.8—
    Kimi K2.686.0%
    Source

    Not directly comparable

Multilingual

  • INCLUDE

    Claude Opus 4.887.6%
    Source
    Kimi K2.6—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Claude Opus 4.866.2%
    Source
    Kimi K2.6—

    Not directly comparable

  • ScreenSpot Pro

    Claude Opus 4.887.9%
    Source
    Kimi K2.6—

    Not directly comparable

  • CharXiv

    Claude Opus 4.889.9%
    Source
    Kimi K2.680.4%
    Source

    Claude Opus 4.8 leads this result

  • CharXiv w/o tools

    Claude Opus 4.880.5%
    Source
    Kimi K2.6—

    Not directly comparable

  • MMMU-Pro

    Claude Opus 4.8—
    Kimi K2.679.4%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Claude Opus 4.8—
    Kimi K2.680.1%
    Source

    Not directly comparable

  • MathVision

    Claude Opus 4.8—
    Kimi K2.687.4%
    Source

    Not directly comparable

  • V*

    Claude Opus 4.8—
    Kimi K2.696.9%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.8 or Kimi K2.6?

Claude Opus 4.8 has the higher public score, 76.45 versus 60.14, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Opus 4.8 or Kimi K2.6?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Opus 4.8 or Kimi K2.6?

Claude Opus 4.8 leads the like-for-like agentic tasks comparison across 3 shared weighted benchmark rows.

Which costs less, Claude Opus 4.8 or Kimi K2.6?

For the stated presets, chat costs $0.0175 on Claude Opus 4.8 and $0.00295 on Kimi K2.6; repository review costs $0.325 and $0.0595; the cache-heavy agent loop costs $1.35 and $0.249. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.8 or Kimi K2.6?

Claude Opus 4.8 has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 26, 2026

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