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BenchLM

Claude Opus 5.5 vs Kimi K2.5

Updated September 23, 2026. Rank says Claude Opus 5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Claude Opus 5.5 has the higher public score, 88.45 versus 53.79, and the 90% score intervals do not overlap. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Anthropic logo

Anthropic

88.45/100

Estimated · Public rank #2

90% interval 76.9100.0

Model B
Moonshot AI logo

Moonshot AI

53.79/100

Supported · Public rank #65

90% interval 45.662.0

Shared results
2
Claude Opus 5.5 only
45
Kimi K2.5 only
43
Like-for-like categories
0 / 8
Estimated: Claude Opus 5.5 · Supported: Kimi K2.5How the comparison works

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 5.5

    Claude Opus 5.5 has the larger documented context window.

    Confidence: documented
  • Chat turn cost

    1K fresh input + 500 output tokens

    Kimi K2.5

    Kimi K2.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Cache-heavy agent loop cost

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

    Kimi K2.5

    Kimi K2.5 has the lower estimated token cost for this stated workload. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback
Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Kimi K2.5

    Kimi K2.5 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

    Kimi K2.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    Kimi K2.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

87.1Claude Opus 5.540.9Kimi K2.5

Directional only · BenchAlign v5.6

Claude Opus 5.5 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.6 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

Directional only
Claude Opus 5.5
88.8
Supported · #1/105
Kimi K2.5
35.5
Estimated · #54/105
Basis
BenchAlign v5.6 lane · 11 vs 14 public rows
Reading
Directional only

Coding

Directional only
Claude Opus 5.5
87.1
Supported · #1/135
Kimi K2.5
40.9
Estimated · #58/135
Basis
BenchAlign v5.6 lane · 9 vs 8 public rows
Reading
Directional only

Multimodal

Directional only
Claude Opus 5.5
88.8
#3/50
Kimi K2.5
65.8
#25/50
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 5.5
90.0
Supported · #1/160
Kimi K2.5
46.4
Estimated · #68/160
Basis
BenchAlign v5.6 lane · 16 vs 6 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 5.5
78.5
#4/18
Kimi K2.5
54.1
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 5.5
Not ranked
Kimi K2.5
38.2
#8/12
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 5.5
Not ranked
Kimi K2.5
84.4
#41/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 5.5
Not ranked
Kimi K2.5
62.1
#4/7
Basis
Provisional lane · 0 vs 4 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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 5.5
$0.014
Fits in one request
Kimi K2.5
$0.0021
Fits in one request

Kimi K2.5 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 5.5
$0.26
Fits in one request
Kimi K2.5
$0.039
Fits in one request

Kimi K2.5 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 5.5
$0.32
Fits in one request
Kimi K2.5
$0.162
Fits in one request
Cached input priced at the published list-input rate

Kimi K2.5 has the lower modeled cost

Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

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

Claude Opus 5.5

$0.2 per 1M cached input tokens

Claude Opus 5.5 model documentation

Kimi K2.5

Not published

Reasoning profile

Claude Opus 5.5

Reasoning

Kimi K2.5

Non-Reasoning

Weight access

Claude Opus 5.5

Proprietary

Kimi K2.5

Open Weight

License

Claude Opus 5.5

Proprietary

Kimi K2.5

Open Weight

Release date

Claude Opus 5.5

2026-09-22

Kimi K2.5

2026-02-01

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 5.5 has the higher public score, 88.45 versus 53.79, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.26 vs $0.039. Cache-heavy agent loop: $0.32 vs $0.162.
Context tradeoff
Claude Opus 5.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Opus 5.5 or Kimi K2.5?

Claude Opus 5.5 has the higher public score, 88.45 versus 53.79, 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 5.5 or Kimi K2.5?

Claude Opus 5.5 scores higher for coding on the public lane, 87.1 to 40.9. Kimi K2.5 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 5.5 or Kimi K2.5?

Claude Opus 5.5 scores higher for agentic tasks on the public lane, 88.8 to 35.5. Kimi K2.5 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Opus 5.5 or Kimi K2.5?

For the stated presets, chat costs $0.014 on Claude Opus 5.5 and $0.0021 on Kimi K2.5; repository review costs $0.26 and $0.039; the cache-heavy agent loop costs $0.32 and $0.162. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 5.5 or Kimi K2.5?

Claude Opus 5.5 has the larger documented context window: 1M, compared with 256K.

Self-host vs API cost

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

Claude Opus 5.5
API / mo$18,000
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Kimi K2.5
API / mo$2,700
Self-host / mo$5,221
Break-even132M/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 evidence90 rows

Agentic

  • Terminal-Bench 4.0

    Claude Opus 5.566.40%
    Source
    Kimi K2.5

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Claude Opus 5.558.7%
    Source
    Kimi K2.5

    Not directly comparable

  • AutomationBench

    Claude Opus 5.540.0%
    Source
    Kimi K2.5

    Not directly comparable

  • HLE w/ tools

    Claude Opus 5.567.7%
    Source
    Kimi K2.5

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 5.548.7%
    Source
    Kimi K2.5

    Not directly comparable

  • LAB all-pass (Harvey held-out)

    Claude Opus 5.58.3%
    Source
    Kimi K2.5

    Not directly comparable

  • LAB criterion-pass (Harvey held-out)

    Claude Opus 5.591.2%
    Source
    Kimi K2.5

    Not directly comparable

  • Toolathlon-Verified

    Claude Opus 5.577.8%
    Source
    Kimi K2.5

    Not directly comparable

  • Toolathlon Verified Pass@3

    Claude Opus 5.582.4%
    Source
    Kimi K2.5

    Not directly comparable

  • Toolathlon Verified Pass³

    Claude Opus 5.572.2%
    Source
    Kimi K2.5

    Not directly comparable

  • Toolathlon Verified avg. turns

    Claude Opus 5.526.9 turns
    Source
    Kimi K2.5

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 5.5
    Kimi K2.550.8%
    Source

    Not directly comparable

  • BrowseComp

    Claude Opus 5.5
    Kimi K2.560.6%
    Source

    Not directly comparable

  • Claw-Eval

    Claude Opus 5.5
    Kimi K2.552.3%
    Source

    Not directly comparable

  • QwenClawBench

    Claude Opus 5.5
    Kimi K2.554.3%
    Source

    Not directly comparable

  • τ³-bench results

    Claude Opus 5.5
    Kimi K2.565.7%
    Source

    Not directly comparable

  • DeepSearchQA

    Claude Opus 5.5
    Kimi K2.577.1%
    Source

    Not directly comparable

  • DeepPlanning

    Claude Opus 5.5
    Kimi K2.514.4%
    Source

    Not directly comparable

  • Toolathlon

    Claude Opus 5.5
    Kimi K2.527.8%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Opus 5.5
    Kimi K2.529.5%
    Source

    Not directly comparable

  • MCP-Tasks

    Claude Opus 5.5
    Kimi K2.559.1%
    Source

    Not directly comparable

  • WideResearch

    Claude Opus 5.5
    Kimi K2.572.7%
    Source

    Not directly comparable

  • Gert Labs

    Claude Opus 5.5
    Kimi K2.545.88%
    Source

    Not directly comparable

  • ResearchClawBench

    Claude Opus 5.5
    Kimi K2.514.0%
    Source

    Not directly comparable

  • JobBench

    Claude Opus 5.5
    Kimi K2.58.7%
    Source

    Not directly comparable

Coding

  • FrontierCode 1.1 Main

    Claude Opus 5.554.4%
    Source
    Kimi K2.5

    Not directly comparable

  • cursorBench40

    Claude Opus 5.557.8%
    Source
    Kimi K2.5

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 5.589.9%
    Source
    Kimi K2.550.7%
    Source

    Claude Opus 5.5 leads this result

  • SWE Multilingual

    Claude Opus 5.593.9%
    Source
    Kimi K2.573%
    Source

    Claude Opus 5.5 leads this result

  • SWE Multimodal

    Claude Opus 5.561.4%
    Source
    Kimi K2.5

    Not directly comparable

  • DeepSWE

    Claude Opus 5.574.2%
    Source
    Kimi K2.5

    Not directly comparable

  • FrontierCode 1.1 Extended

    Claude Opus 5.563.6%
    Source
    Kimi K2.5

    Not directly comparable

  • FrontierSWE v2

    Claude Opus 5.562.3%
    Source
    Kimi K2.5

    Not directly comparable

  • ProgramBench

    Claude Opus 5.591.2%
    Source
    Kimi K2.5

    Not directly comparable

  • SWE-bench Verified

    Claude Opus 5.5
    Kimi K2.576.8%
    Source

    Not directly comparable

  • SWE-bench Verified*

    Claude Opus 5.5
    Kimi K2.570.8%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Claude Opus 5.5
    Kimi K2.585.0%
    Source

    Not directly comparable

  • SWE-Rebench

    Claude Opus 5.5
    Kimi K2.558.5%
    Source

    Not directly comparable

  • React Native Evals

    Claude Opus 5.5
    Kimi K2.577.2%
    Source

    Not directly comparable

  • SciCode

    Claude Opus 5.5
    Kimi K2.548.7%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    Claude Opus 5.5
    Kimi K2.561%
    Source

    Not directly comparable

Multimodal

  • Chartography (tools)

    Claude Opus 5.589.0%
    Source
    Kimi K2.5

    Not directly comparable

  • Chartography (no tools)

    Claude Opus 5.564.4%
    Source
    Kimi K2.5

    Not directly comparable

  • BenchCAD Vision2Code (no tools)

    Claude Opus 5.50.730
    Source
    Kimi K2.5

    Not directly comparable

  • BenchCAD Vision2Code (tools)

    Claude Opus 5.50.962
    Source
    Kimi K2.5

    Not directly comparable

  • Biomedical image analysis

    Claude Opus 5.571.4%
    Source
    Kimi K2.5

    Not directly comparable

  • OfficeQA

    Claude Opus 5.578.9%
    Source
    Kimi K2.5

    Not directly comparable

  • OfficeQA Pro

    Claude Opus 5.567.7%
    Source
    Kimi K2.5

    Not directly comparable

  • MMMU-Pro

    Claude Opus 5.5
    Kimi K2.578.5%
    Source

    Not directly comparable

  • Video-MME

    Claude Opus 5.5
    Kimi K2.587.4%
    Source

    Not directly comparable

  • MMVU

    Claude Opus 5.5
    Kimi K2.580.4%
    Source

    Not directly comparable

  • VideoMMMU

    Claude Opus 5.5
    Kimi K2.586.6%
    Source

    Not directly comparable

Knowledge

  • HLE w/o tools

    Claude Opus 5.564.4%
    Source
    Kimi K2.5

    Not directly comparable

  • HealthBench (raw)

    Claude Opus 5.568.1%
    Source
    Kimi K2.5

    Not directly comparable

  • HealthBench (length-adjusted)

    Claude Opus 5.560.6%
    Source
    Kimi K2.5

    Not directly comparable

  • HealthBench Professional

    Claude Opus 5.565.6%
    Source
    Kimi K2.5

    Not directly comparable

  • HealthBench Professional (raw)

    Claude Opus 5.577.1%
    Source
    Kimi K2.5

    Not directly comparable

  • BioMysteryBench (human-solvable)

    Claude Opus 5.589.3%
    Source
    Kimi K2.5

    Not directly comparable

  • BioMysteryBench (human-difficult)

    Claude Opus 5.550.0%
    Source
    Kimi K2.5

    Not directly comparable

  • SpatialBench Verified

    Claude Opus 5.572.0%
    Source
    Kimi K2.5

    Not directly comparable

  • SingleCellBench

    Claude Opus 5.561.2%
    Source
    Kimi K2.5

    Not directly comparable

  • Morphology-to-molecule matching

    Claude Opus 5.534.0%
    Source
    Kimi K2.5

    Not directly comparable

  • Medicinal chemistry

    Claude Opus 5.563.5%
    Source
    Kimi K2.5

    Not directly comparable

  • Protein Design

    Claude Opus 5.560.2%
    Source
    Kimi K2.5

    Not directly comparable

  • Protein Design library ranking

    Claude Opus 5.556.0%
    Source
    Kimi K2.5

    Not directly comparable

  • De novo protein-binder design

    Claude Opus 5.582.6%
    Source
    Kimi K2.5

    Not directly comparable

  • Protocols (troubleshooting)

    Claude Opus 5.573.7%
    Source
    Kimi K2.5

    Not directly comparable

  • Protocols (understanding)

    Claude Opus 5.569.0%
    Source
    Kimi K2.5

    Not directly comparable

  • GPQA

    Claude Opus 5.5
    Kimi K2.587.6%
    Source

    Not directly comparable

  • GPQA-D

    Claude Opus 5.5
    Kimi K2.587.6%
    Source

    Not directly comparable

  • SuperGPQA

    Claude Opus 5.5
    Kimi K2.569.2%
    Source

    Not directly comparable

  • MMLU-Pro

    Claude Opus 5.5
    Kimi K2.587.1%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Claude Opus 5.5
    Kimi K2.587.1%
    Source

    Not directly comparable

  • HLE

    Claude Opus 5.5
    Kimi K2.530.1%
    Source

    Not directly comparable

Multilingual

  • GMMLU

    Claude Opus 5.594.3%
    Source
    Kimi K2.5

    Not directly comparable

  • MILU

    Claude Opus 5.593.1%
    Source
    Kimi K2.5

    Not directly comparable

  • MMLU-ProX

    Claude Opus 5.5
    Kimi K2.582.3%
    Source

    Not directly comparable

  • NOVA-63

    Claude Opus 5.5
    Kimi K2.556.0%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Claude Opus 5.5
    Kimi K2.593.9%
    Source

    Not directly comparable

Math

  • ArXivMath Aug. 2026 (no tools)

    Claude Opus 5.591.2%
    Source
    Kimi K2.5

    Not directly comparable

  • ArXivMath Aug. 2026 (tools)

    Claude Opus 5.596.9%
    Source
    Kimi K2.5

    Not directly comparable

  • AIME 2025

    Claude Opus 5.5
    Kimi K2.596.1%
    Source

    Not directly comparable

  • AIME26

    Claude Opus 5.5
    Kimi K2.595.8%
    Source

    Not directly comparable

  • AIME25 (Arcee)

    Claude Opus 5.5
    Kimi K2.596.3%
    Source

    Not directly comparable

  • HMMT Feb 2025

    Claude Opus 5.5
    Kimi K2.595.4%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Claude Opus 5.5
    Kimi K2.591.1%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude Opus 5.5
    Kimi K2.587.1%
    Source

    Not directly comparable

  • MMAnswerBench

    Claude Opus 5.5
    Kimi K2.581.8%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 5.5
    Kimi K2.527.900%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 5.5
    Kimi K2.54.200%
    Source

    Not directly comparable

90 public results · 2 shared

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Last updated September 23, 2026