Claude Sonnet 5.5 vs Kimi K2.7 Code

Updated September 28, 2026. Rank says Claude Sonnet 5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Claude Sonnet 5.5 has the higher public score, 80.49 versus 50.43, and the 90% score intervals do not overlap. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Claude Sonnet 5.5

Anthropic

80.49/100

Estimated · Public rank #5

90% interval 69.0–92.0

Model B

Kimi K2.7 Code

Moonshot AI

50.43/100

Estimated · Public rank #74

90% interval 42.2–58.6

Shared results: 1
Claude Sonnet 5.5 only: 46
Kimi K2.7 Code only: 10
Like-for-like categories: 1 / 8

Estimated: Claude Sonnet 5.5 and Kimi K2.7 CodeHow 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.

Agentic work
Tool use, computer use, and multi-step task completion
Claude Sonnet 5.5
Claude Sonnet 5.5 leads on the public agentic lane, 66.1 to 37.1, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Claude Sonnet 5.5
Claude Sonnet 5.5 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Kimi K2.7 Code
Kimi K2.7 Code 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 K2.7 Code
Kimi K2.7 Code has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Kimi K2.7 Code
Kimi K2.7 Code 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
Claude Sonnet 5.5 is scored on Estimated evidence for coding, 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.

Pick the job you would use them for

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

79.6Claude Sonnet 5.544.4Kimi K2.7 Code

Directional only · BenchAlign v5.7

Claude Sonnet 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.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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

Like-for-like

Claude Sonnet 5.5: 66.1; Supported · #10/111
Kimi K2.7 Code: 37.1; Supported · #55/111
Basis: BenchAlign v5.7 lane · 11 vs 4 public rows
Reading: Claude Sonnet 5.5 leads

Coding

Directional only

Claude Sonnet 5.5: 79.6; Estimated · #3/136
Kimi K2.7 Code: 44.4; Supported · #49/136
Basis: BenchAlign v5.7 lane · 9 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

Claude Sonnet 5.5: 80.9; Supported · #6/160
Kimi K2.7 Code: 53.3; Estimated · #48/160
Basis: BenchAlign v5.7 lane · 16 vs 0 public rows
Reading: Directional only

Reasoning

Not comparable

Claude Sonnet 5.5: 79.2; #7/27
Kimi K2.7 Code: 76.8; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Sonnet 5.5: 83.3; Unranked · 7 rankable rows
Kimi K2.7 Code: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Sonnet 5.5: Not ranked
Kimi K2.7 Code: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Sonnet 5.5: Not ranked
Kimi K2.7 Code: 75.1; #59/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Sonnet 5.5: Not ranked
Kimi K2.7 Code: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Sonnet 5.5 and Kimi K2.7 Code
Category	Claude Sonnet 5.5	Kimi K2.7 Code	Basis	Reading
Agentic	66.1Supported · #10/111	37.1Supported · #55/111	Like-for-likeBenchAlign v5.7 lane · 11 vs 4 public rows	Claude Sonnet 5.5 leads
Coding	79.6Estimated · #3/136	44.4Supported · #49/136	Directional onlyBenchAlign v5.7 lane · 9 vs 7 public rows	Directional only
Knowledge	80.9Supported · #6/160	53.3Estimated · #48/160	Directional onlyBenchAlign v5.7 lane · 16 vs 0 public rows	Directional only
Reasoning	79.2#7/27	76.8Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	83.3Unranked · 7 rankable rows	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	75.1#59/124	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 Sonnet 5.5: $0.007; Fits in one request
Kimi K2.7 Code: $0.00295; Fits in one request

Kimi K2.7 Code has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 5.5: $0.13; Fits in one request
Kimi K2.7 Code: $0.0595; Fits in one request

Kimi K2.7 Code 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 Sonnet 5.5: $0.18; Fits in one request
Kimi K2.7 Code: $0.097; Fits in one request

Kimi K2.7 Code has the lower modeled cost

Costs use the listed standard API rates.

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.

SpecificationClaude Sonnet 5.5Kimi K2.7 Code

Context window

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

Claude Sonnet 5.5

Anthropic Claude Sonnet 5.5 model documentation

Kimi K2.7 Code

256K

API model ID

Claude Sonnet 5.5

claude-sonnet-5-5

Anthropic Claude Sonnet 5.5 model documentation

Kimi K2.7 Code

Not sourced

Cached-input rate

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

Claude Sonnet 5.5

$0.2 per 1M cached input tokens

Claude API pricing

Kimi K2.7 Code

$0.19 per 1M cached input tokens

Documented inputs

Claude Sonnet 5.5

text, image

Anthropic Claude Sonnet 5.5 model documentation

Kimi K2.7 Code

Not sourced

Documented outputs

Claude Sonnet 5.5

text

Anthropic Claude Sonnet 5.5 model documentation

Kimi K2.7 Code

Not sourced

Provider availability

Claude Sonnet 5.5

Generally Available · Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS

Anthropic Claude Sonnet 5.5 model documentation

Kimi K2.7 Code

Not sourced

Reasoning profile

Claude Sonnet 5.5

Reasoning

Kimi K2.7 Code

Reasoning

Weight access

Claude Sonnet 5.5

Proprietary

Kimi K2.7 Code

Open Weight

License

Claude Sonnet 5.5

Proprietary

Kimi K2.7 Code

Open Weight

Release date

Claude Sonnet 5.5

2026-09-28

Kimi K2.7 Code

2026-06-12

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 Sonnet 5.5 has the higher public score, 80.49 versus 50.43, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.13 vs $0.0595. Cache-heavy agent loop: $0.18 vs $0.097.
Context tradeoff: Claude Sonnet 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 Sonnet 5.5 or Kimi K2.7 Code?

Claude Sonnet 5.5 has the higher public score, 80.49 versus 50.43, 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 Sonnet 5.5 or Kimi K2.7 Code?

Claude Sonnet 5.5 scores higher for coding on the public lane, 79.6 to 44.4. Claude Sonnet 5.5 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 Sonnet 5.5 or Kimi K2.7 Code?

Claude Sonnet 5.5 leads the public agentic tasks lane, 66.1 to 37.1, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, Claude Sonnet 5.5 or Kimi K2.7 Code?

For the stated presets, chat costs $0.007 on Claude Sonnet 5.5 and $0.00295 on Kimi K2.7 Code; repository review costs $0.13 and $0.0595; the cache-heavy agent loop costs $0.18 and $0.097. Costs use the listed standard API rates.

Which has the larger context window, Claude Sonnet 5.5 or Kimi K2.7 Code?

Claude Sonnet 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 Sonnet 5.5

API / mo$9,000

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Kimi K2.7 Code

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 evidence57 rows

Agentic

Terminal-Bench 4.0
Claude Sonnet 5.570.60%
Source
Kimi K2.7 Code—
Not directly comparable
HLE w/ tools
Claude Sonnet 5.564.5%
Source
Kimi K2.7 Code—
Not directly comparable
DRACO
Claude Sonnet 5.587.0%
Source
Kimi K2.7 Code—
Not directly comparable
Terminal-Bench-Science 0.1
Claude Sonnet 5.559.9%
Source
Kimi K2.7 Code—
Not directly comparable
LAB all-pass (Harvey held-out)
Claude Sonnet 5.510.0%
Source
Kimi K2.7 Code—
Not directly comparable
LAB criterion-pass (Harvey held-out)
Claude Sonnet 5.593.1%
Source
Kimi K2.7 Code—
Not directly comparable
Toolathlon Verified Pass@3
Claude Sonnet 5.585.2%
Source
Kimi K2.7 Code—
Not directly comparable
Toolathlon Verified Pass³
Claude Sonnet 5.568.5%
Source
Kimi K2.7 Code—
Not directly comparable
Toolathlon Verified avg. turns
Claude Sonnet 5.531.6 turns
Source
Kimi K2.7 Code—
Not directly comparable
AutomationBench (Zapier 1.0.6)
Claude Sonnet 5.544.7%
Source
Kimi K2.7 Code—
Not directly comparable
Toolathlon-Verified
Claude Sonnet 5.577.8%
Source
Kimi K2.7 Code—
Not directly comparable
Kimi Claw 24/7
Claude Sonnet 5.5—
Kimi K2.7 Code46.9%
Source
Not directly comparable
MCP Atlas
Claude Sonnet 5.5—
Kimi K2.7 Code76%
Source
Not directly comparable
MCP Mark Verified
Claude Sonnet 5.5—
Kimi K2.7 Code81.1%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 5.5—
Kimi K2.7 Code67.0%
Source
Not directly comparable

Coding

FrontierCode 1.1 Main
Claude Sonnet 5.546.2%
Source
Kimi K2.7 Code—
Not directly comparable
cursorBench40
Claude Sonnet 5.555.5%
Source
Kimi K2.7 Code—
Not directly comparable
SWE-bench Pro
Claude Sonnet 5.581.3%
Source
Kimi K2.7 Code—
Not directly comparable
SWE Multilingual
Claude Sonnet 5.590.3%
Source
Kimi K2.7 Code—
Not directly comparable
SWE Multimodal
Claude Sonnet 5.554.3%
Source
Kimi K2.7 Code—
Not directly comparable
DeepSWE
Claude Sonnet 5.571.0%
Source
Kimi K2.7 Code—
Not directly comparable
FrontierCode 1.1 Extended
Claude Sonnet 5.559.1%
Source
Kimi K2.7 Code—
Not directly comparable
ProgramBench
Claude Sonnet 5.579.7%
Source
Kimi K2.7 Code53.6%
Source
Claude Sonnet 5.5 leads this result
FrontierSWE v2
Claude Sonnet 5.561.9%
Source
Kimi K2.7 Code—
Not directly comparable
Kimi Code Bench v2
Claude Sonnet 5.5—
Kimi K2.7 Code62.0%
Source
Not directly comparable
MLS-Bench Lite
Claude Sonnet 5.5—
Kimi K2.7 Code35.1%
Source
Not directly comparable
cursorBench32
Claude Sonnet 5.5—
Kimi K2.7 Code49.7%
Source
Not directly comparable
OpenHarmony Bench
Claude Sonnet 5.5—
Kimi K2.7 Code52.1%
Source
Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 5.5—
Kimi K2.7 Code82.1%
Source
Not directly comparable
SWE-bench (Vals)
Claude Sonnet 5.5—
Kimi K2.7 Code78.2%
Source
Not directly comparable

Multimodal

Chartography (no tools)
Claude Sonnet 5.561.6%
Source
Kimi K2.7 Code—
Not directly comparable
Chartography (tools)
Claude Sonnet 5.590.2%
Source
Kimi K2.7 Code—
Not directly comparable
BenchCAD Vision2Code (no tools)
Claude Sonnet 5.50.747
Source
Kimi K2.7 Code—
Not directly comparable
BenchCAD Vision2Code (tools)
Claude Sonnet 5.50.963
Source
Kimi K2.7 Code—
Not directly comparable
Biomedical image analysis
Claude Sonnet 5.572.2%
Source
Kimi K2.7 Code—
Not directly comparable
OfficeQA
Claude Sonnet 5.576.9%
Source
Kimi K2.7 Code—
Not directly comparable
OfficeQA Pro
Claude Sonnet 5.565.6%
Source
Kimi K2.7 Code—
Not directly comparable

Knowledge

HealthBench (raw)
Claude Sonnet 5.569.4%
Source
Kimi K2.7 Code—
Not directly comparable
HealthBench (length-adjusted)
Claude Sonnet 5.565.4%
Source
Kimi K2.7 Code—
Not directly comparable
HealthBench Professional (raw)
Claude Sonnet 5.577.1%
Source
Kimi K2.7 Code—
Not directly comparable
HealthBench Professional
Claude Sonnet 5.569.2%
Source
Kimi K2.7 Code—
Not directly comparable
BioMysteryBench (human-solvable)
Claude Sonnet 5.589.2%
Source
Kimi K2.7 Code—
Not directly comparable
BioMysteryBench (human-difficult)
Claude Sonnet 5.544.7%
Source
Kimi K2.7 Code—
Not directly comparable
SpatialBench Verified
Claude Sonnet 5.572.5%
Source
Kimi K2.7 Code—
Not directly comparable
SingleCellBench
Claude Sonnet 5.559.1%
Source
Kimi K2.7 Code—
Not directly comparable
Protein Design
Claude Sonnet 5.551.0%
Source
Kimi K2.7 Code—
Not directly comparable
Morphology-to-molecule matching
Claude Sonnet 5.525.0%
Source
Kimi K2.7 Code—
Not directly comparable
Medicinal chemistry
Claude Sonnet 5.565.3%
Source
Kimi K2.7 Code—
Not directly comparable
Protein Design library ranking
Claude Sonnet 5.554.8%
Source
Kimi K2.7 Code—
Not directly comparable
De novo protein-binder design
Claude Sonnet 5.582.3%
Source
Kimi K2.7 Code—
Not directly comparable
Protocols (troubleshooting)
Claude Sonnet 5.567.3%
Source
Kimi K2.7 Code—
Not directly comparable
Protocols (understanding)
Claude Sonnet 5.566.6%
Source
Kimi K2.7 Code—
Not directly comparable
HLE w/o tools
Claude Sonnet 5.556.9%
Source
Kimi K2.7 Code—
Not directly comparable

Multilingual

GMMLU
Claude Sonnet 5.592.1%
Source
Kimi K2.7 Code—
Not directly comparable
MILU
Claude Sonnet 5.591.6%
Source
Kimi K2.7 Code—
Not directly comparable

Math

ArXivMath Aug. 2026 (no tools)
Claude Sonnet 5.586.8%
Source
Kimi K2.7 Code—
Not directly comparable
ArXivMath Aug. 2026 (tools)
Claude Sonnet 5.595.2%
Source
Kimi K2.7 Code—
Not directly comparable

57 public results · 1 shared

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Last updated September 28, 2026

Claude Sonnet 5.5 vs Kimi K2.7 Code

Decision reading

Which one for your work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Claude Sonnet 5.5 or Kimi K2.7 Code?

Which is better for coding, Claude Sonnet 5.5 or Kimi K2.7 Code?

Which is better for agentic tasks, Claude Sonnet 5.5 or Kimi K2.7 Code?

Which costs less, Claude Sonnet 5.5 or Kimi K2.7 Code?

Which has the larger context window, Claude Sonnet 5.5 or Kimi K2.7 Code?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Multilingual

Math

Related comparisons

Watch Claude Sonnet 5.5 vs Kimi K2.7 Code