Coding work
Code generation, repair, and software-engineering tasks
Claude Sonnet 5
Claude Sonnet 5 leads on the public coding lane, 59.8 to 44.4, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Claude Sonnet 5 vs Kimi K2.7 Code
Updated September 29, 2026. Rank says Claude Sonnet 5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Claude Sonnet 5 has the higher public score, 66.99 versus 50.47, and the 90% score intervals do not overlap. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 4
- Claude Sonnet 5 only
- 22
- Kimi K2.7 Code only
- 7
- Like-for-like categories
- 2 / 8
Supported: Claude Sonnet 5 · Estimated: 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
Claude Sonnet 5 leads on the public agentic lane, 64.6 to 37.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: strongerLong documents
Prompts that approach the documented context limit
Claude Sonnet 5
Claude Sonnet 5 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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-ratesCache-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-ratesRepository 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
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.
59.8Claude Sonnet 544.4Kimi K2.7 Code
Like-for-like · BenchAlign v5.7
Claude Sonnet 5 leads the like-for-like coding row, although the 90% intervals overlap.
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.
22 Claude Sonnet 5 only
4 Shared
7 Kimi K2.7 Code only
1 category rests 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.
cursorBench32Coding
Normalized gap 11.8Claude Sonnet 5:61.5%Kimi K2.7 Code:49.7%LiveCodeBench (Vals)Coding
Normalized gap 0.3Claude Sonnet 5:82.4%Kimi K2.7 Code:82.1%
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 v5.7 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
- 64.6
- Supported · #12/117
- Kimi K2.7 Code
- 37.2
- Supported · #59/117
- Basis
- BenchAlign v5.7 lane · 7 vs 4 public rows
- Reading
- Claude Sonnet 5 leads
Coding
Like-for-like- Claude Sonnet 5
- 59.8
- Supported · #19/142
- Kimi K2.7 Code
- 44.4
- Supported · #53/142
- Basis
- BenchAlign v5.7 lane · 11 vs 7 public rows
- Reading
- Claude Sonnet 5 leads · intervals overlap
Knowledge
Directional only- Claude Sonnet 5
- 64.5
- Supported · #26/168
- Kimi K2.7 Code
- 53.3
- Estimated · #52/168
- Basis
- BenchAlign v5.7 lane · 6 vs 0 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Sonnet 5
- 78.7
- Unranked · 2 rankable rows
- 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
- 78.4
- #15/50
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 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
- 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
- Not ranked
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Agentic | 64.6Supported · #12/117 | 37.2Supported · #59/117 | Like-for-likeBenchAlign v5.7 lane · 7 vs 4 public rows | Claude Sonnet 5 leads |
| Coding | 59.8Supported · #19/142 | 44.4Supported · #53/142 | Like-for-likeBenchAlign v5.7 lane · 11 vs 7 public rows | Claude Sonnet 5 leads · intervals overlap |
| Knowledge | 64.5Supported · #26/168 | 53.3Estimated · #52/168 | Directional onlyBenchAlign v5.7 lane · 6 vs 0 public rows | Directional only |
| Reasoning | 78.7Unranked · 2 rankable rows | 76.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 78.4#15/50 | 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
- $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
- $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
- $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 5Kimi K2.7 Code
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
Kimi K2.7 Code
256K
API model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewKimi 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
$0.2 per 1M cached input tokens
Claude API pricingKimi K2.7 Code
$0.19 per 1M cached input tokens
Documented inputs
Claude Sonnet 5
text, image
Anthropic model overviewKimi K2.7 Code
Not sourced
Documented outputs
Claude Sonnet 5
Kimi K2.7 Code
Not sourced
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewKimi K2.7 Code
Not sourced
Reasoning profile
Claude Sonnet 5
Reasoning
Kimi K2.7 Code
Reasoning
Weight access
Claude Sonnet 5
Proprietary
Kimi K2.7 Code
Open Weight
License
Claude Sonnet 5
Proprietary
Kimi K2.7 Code
Open Weight
Release date
Claude Sonnet 5
2026-06-30
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 has the higher public score, 66.99 versus 50.47, 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 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 or Kimi K2.7 Code?
Claude Sonnet 5 has the higher public score, 66.99 versus 50.47, 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 or Kimi K2.7 Code?
Claude Sonnet 5 leads the public coding lane, 59.8 to 44.4, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Sonnet 5 or Kimi K2.7 Code?
Claude Sonnet 5 leads the public agentic tasks lane, 64.6 to 37.2, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, Claude Sonnet 5 or Kimi K2.7 Code?
For the stated presets, chat costs $0.007 on Claude Sonnet 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 or Kimi K2.7 Code?
Claude Sonnet 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
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
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence33 rows
Agentic
Terminal-Bench 3.0
Claude Sonnet 514.6%SourceKimi K2.7 Code—Not directly comparable
Terminal-Bench 2.1
Claude Sonnet 580.4%SourceKimi K2.7 Code—Not directly comparable
BrowseComp
Claude Sonnet 584.7%SourceKimi K2.7 Code—Not directly comparable
HLE w/ tools
Claude Sonnet 557.4%SourceKimi K2.7 Code—Not directly comparable
OSWorld-Verified
Claude Sonnet 581.2%SourceKimi K2.7 Code—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%SourceKimi K2.7 Code67.0%SourceClaude Sonnet 5 leads this result
ApprenticeBench
Claude Sonnet 516%SourceKimi K2.7 Code—Not directly comparable
Kimi Claw 24/7
Claude Sonnet 5—Kimi K2.7 Code46.9%SourceNot directly comparable
MCP Atlas
Claude Sonnet 5—Kimi K2.7 Code76%SourceNot directly comparable
MCP Mark Verified
Claude Sonnet 5—Kimi K2.7 Code81.1%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceKimi K2.7 Code—Not directly comparable
SWE-bench Pro
Claude Sonnet 563.2%SourceKimi K2.7 Code—Not directly comparable
SWE Multilingual
Claude Sonnet 578.3%SourceKimi K2.7 Code—Not directly comparable
SWE Multimodal
Claude Sonnet 528.1%SourceKimi K2.7 Code—Not directly comparable
Terminal-Bench 2.1
Claude Sonnet 580.4%SourceKimi K2.7 Code—Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceKimi K2.7 Code—Not directly comparable
cursorBench32
Shared sourceClaude Sonnet 561.5%Kimi K2.7 Code49.7%Claude Sonnet 5 leads this result
VulcanBench CII v1
Claude Sonnet 589.2%SourceKimi K2.7 Code—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%SourceKimi K2.7 Code82.1%SourceClaude Sonnet 5 leads this result
SWE-bench (Vals)
Claude Sonnet 579.6%SourceKimi K2.7 Code78.2%SourceClaude Sonnet 5 leads this result
cursorBench40
Claude Sonnet 534.1%SourceKimi K2.7 Code—Not directly comparable
Kimi Code Bench v2
Claude Sonnet 5—Kimi K2.7 Code62.0%SourceNot directly comparable
ProgramBench
Claude Sonnet 5—Kimi K2.7 Code53.6%SourceNot directly comparable
MLS-Bench Lite
Claude Sonnet 5—Kimi K2.7 Code35.1%SourceNot directly comparable
OpenHarmony Bench
Claude Sonnet 5—Kimi K2.7 Code52.1%SourceNot directly comparable
Multimodal
Knowledge
HLE
Claude Sonnet 557.4%SourceKimi K2.7 Code—Not directly comparable
HLE w/o tools
Claude Sonnet 543.2%SourceKimi K2.7 Code—Not directly comparable
HLE-Verified
Claude Sonnet 531.0%SourceKimi K2.7 Code—Not directly comparable
LABBench2
Claude Sonnet 580.1%SourceKimi K2.7 Code—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 588.9%SourceKimi K2.7 Code—Not directly comparable
MMLU-Pro (Vals)
Claude Sonnet 587.5%SourceKimi K2.7 Code—Not directly comparable
33 public results · 4 shared
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Last updated September 29, 2026