Agentic work
Tool use, computer use, and multi-step task completion
Kimi K3
Kimi K3 leads on the public agentic lane, 69 to 66.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Claude Sonnet 5.5 vs Kimi K3
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.
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 5.5 has the higher public score estimate, 80.49 versus 71.88, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- Claude Sonnet 5.5 only
- 41
- Kimi K3 only
- 40
- Like-for-like categories
- 2 / 8
Estimated: Claude Sonnet 5.5 · Supported: Kimi K3How 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
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
Claude Sonnet 5.5
Claude Sonnet 5.5 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
Claude Sonnet 5.5
Claude Sonnet 5.5 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
Claude Sonnet 5.5
Claude Sonnet 5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding 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.
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 5.561.5Kimi K3
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.
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.
41 Claude Sonnet 5.5 only
6 Shared
40 Kimi K3 only
2 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.
HLE w/o toolsKnowledge
Normalized gap 13.4Claude Sonnet 5.5:56.9%Kimi K3:43.5%OfficeQA ProMultimodal
Normalized gap 2.3Claude Sonnet 5.5:65.6%Kimi K3:63.3%
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 5.5
- 66.1
- Supported · #10/111
- Kimi K3
- 69.0
- Supported · #7/111
- Basis
- BenchAlign v5.7 lane · 11 vs 12 public rows
- Reading
- Kimi K3 leads · intervals overlap
Knowledge
Like-for-like- Claude Sonnet 5.5
- 80.9
- Supported · #6/160
- Kimi K3
- 68.5
- Supported · #15/160
- Basis
- BenchAlign v5.7 lane · 16 vs 6 public rows
- Reading
- Claude Sonnet 5.5 leads · intervals overlap
Coding
Directional only- Claude Sonnet 5.5
- 79.6
- Estimated · #3/136
- Kimi K3
- 61.5
- Supported · #15/136
- Basis
- BenchAlign v5.7 lane · 9 vs 13 public rows
- Reading
- Directional only
Reasoning
Directional only- Claude Sonnet 5.5
- 79.2
- #7/27
- Kimi K3
- 65.6
- #17/27
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Multimodal
Not comparable- Claude Sonnet 5.5
- 83.3
- Unranked · 7 rankable rows
- Kimi K3
- 89.4
- #1/50
- Basis
- Provisional lane · 1 vs 3 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5.5
- Not ranked
- Kimi K3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5.5
- Not ranked
- Kimi K3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5.5
- Not ranked
- Kimi K3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5.5 | Kimi K3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 66.1Supported · #10/111 | 69.0Supported · #7/111 | Like-for-likeBenchAlign v5.7 lane · 11 vs 12 public rows | Kimi K3 leads · intervals overlap |
| Knowledge | 80.9Supported · #6/160 | 68.5Supported · #15/160 | Like-for-likeBenchAlign v5.7 lane · 16 vs 6 public rows | Claude Sonnet 5.5 leads · intervals overlap |
| Coding | 79.6Estimated · #3/136 | 61.5Supported · #15/136 | Directional onlyBenchAlign v5.7 lane · 9 vs 13 public rows | Directional only |
| Reasoning | 79.2#7/27 | 65.6#17/27 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Multimodal | 83.3Unranked · 7 rankable rows | 89.4#1/50 | Not comparableProvisional lane · 1 vs 3 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | 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 K3
- $0.0105
- Fits in one request
Claude Sonnet 5.5 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 K3
- $0.195
- Fits in one request
Claude Sonnet 5.5 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 Sonnet 5.5
- $0.18
- Fits in one request
- Kimi K3
- $0.27
- Fits in one request
Claude Sonnet 5.5 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 K3
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5.5
Kimi K3
1.05M
API model ID
Claude Sonnet 5.5
claude-sonnet-5-5
Anthropic Claude Sonnet 5.5 model documentationKimi K3
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 pricingKimi K3
$0.3 per 1M cached input tokens
Documented inputs
Claude Sonnet 5.5
Kimi K3
Not sourced
Documented outputs
Claude Sonnet 5.5
Kimi K3
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 documentationKimi K3
Not sourced
Reasoning profile
Claude Sonnet 5.5
Reasoning
Kimi K3
Reasoning
Weight access
Claude Sonnet 5.5
Proprietary
Kimi K3
Pending
License
Claude Sonnet 5.5
Proprietary
Kimi K3
Pending
Release date
Claude Sonnet 5.5
2026-09-28
Kimi K3
2026-07-16
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 estimate, 80.49 versus 71.88, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.13 vs $0.195. Cache-heavy agent loop: $0.18 vs $0.27.
- Context tradeoff
- Kimi K3 has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Sonnet 5.5 or Kimi K3?
Claude Sonnet 5.5 has the higher public score estimate, 80.49 versus 71.88, 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 Sonnet 5.5 or Kimi K3?
Claude Sonnet 5.5 scores higher for coding on the public lane, 79.6 to 61.5. 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 K3?
Kimi K3 leads the public agentic tasks lane, 69 to 66.1, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Sonnet 5.5 or Kimi K3?
For the stated presets, chat costs $0.007 on Claude Sonnet 5.5 and $0.0105 on Kimi K3; repository review costs $0.13 and $0.195; the cache-heavy agent loop costs $0.18 and $0.27. Costs use the listed standard API rates.
Which has the larger context window, Claude Sonnet 5.5 or Kimi K3?
Kimi K3 has the larger documented context window: 1.05M, compared with 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence87 rows
Agentic
Terminal-Bench 4.0
Claude Sonnet 5.570.60%SourceKimi K3—Not directly comparable
HLE w/ tools
Claude Sonnet 5.564.5%SourceKimi K3—Not directly comparable
DRACO
Claude Sonnet 5.587.0%SourceKimi K3—Not directly comparable
Terminal-Bench-Science 0.1
Claude Sonnet 5.559.9%SourceKimi K3—Not directly comparable
LAB all-pass (Harvey held-out)
Claude Sonnet 5.510.0%SourceKimi K3—Not directly comparable
LAB criterion-pass (Harvey held-out)
Claude Sonnet 5.593.1%SourceKimi K3—Not directly comparable
Toolathlon Verified Pass@3
Claude Sonnet 5.585.2%SourceKimi K3—Not directly comparable
Toolathlon Verified Pass³
Claude Sonnet 5.568.5%SourceKimi K3—Not directly comparable
Toolathlon Verified avg. turns
Claude Sonnet 5.531.6 turnsSourceKimi K3—Not directly comparable
AutomationBench (Zapier 1.0.6)
Claude Sonnet 5.544.7%SourceKimi K3—Not directly comparable
Toolathlon-Verified
Claude Sonnet 5.577.8%SourceKimi K373.2%SourceClaude Sonnet 5.5 leads this result
Terminal-Bench 2.1
Claude Sonnet 5.5—Kimi K388.3%SourceNot directly comparable
BrowseComp
Claude Sonnet 5.5—Kimi K391.2%SourceNot directly comparable
DeepSearchQA
Claude Sonnet 5.5—Kimi K395.0%SourceNot directly comparable
MCP Atlas
Claude Sonnet 5.5—Kimi K384.2%SourceNot directly comparable
AutomationBench
Claude Sonnet 5.5—Kimi K330.8%SourceNot directly comparable
JobBench
Claude Sonnet 5.5—Kimi K352.9%SourceNot directly comparable
APEX-Agents
Claude Sonnet 5.5—Kimi K337.6%SourceNot directly comparable
SpreadsheetBench 2
Claude Sonnet 5.5—Kimi K334.8%SourceNot directly comparable
DECK-Bench
Claude Sonnet 5.5—Kimi K373.5%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 5.5—Kimi K380.9%SourceNot directly comparable
ApprenticeBench
Claude Sonnet 5.5—Kimi K318%SourceNot directly comparable
Coding
FrontierCode 1.1 Main
Claude Sonnet 5.546.2%SourceKimi K3—Not directly comparable
cursorBench40
Claude Sonnet 5.555.5%SourceKimi K3—Not directly comparable
SWE-bench Pro
Claude Sonnet 5.581.3%SourceKimi K3—Not directly comparable
SWE Multilingual
Claude Sonnet 5.590.3%SourceKimi K3—Not directly comparable
SWE Multimodal
Claude Sonnet 5.554.3%SourceKimi K3—Not directly comparable
DeepSWE
Claude Sonnet 5.571.0%SourceKimi K367.5%SourceClaude Sonnet 5.5 leads this result
FrontierCode 1.1 Extended
Claude Sonnet 5.559.1%SourceKimi K3—Not directly comparable
ProgramBench
Claude Sonnet 5.579.7%SourceKimi K377.8%SourceClaude Sonnet 5.5 leads this result
FrontierSWE v2
Claude Sonnet 5.561.9%SourceKimi K325.9%SourceClaude Sonnet 5.5 leads this result
cursorBench32
Claude Sonnet 5.5—Kimi K360.8%SourceNot directly comparable
FrontierSWE
Claude Sonnet 5.5—Kimi K381.2%SourceNot directly comparable
Kimi Code Bench v2
Claude Sonnet 5.5—Kimi K372.9%SourceNot directly comparable
sweMarathon
Claude Sonnet 5.5—Kimi K342%SourceNot directly comparable
PostTrain Bench
Claude Sonnet 5.5—Kimi K336.6%SourceNot directly comparable
MLS-Bench Lite
Claude Sonnet 5.5—Kimi K348.3%SourceNot directly comparable
VulcanBench v3
Claude Sonnet 5.5—Kimi K373.7%SourceNot directly comparable
OpenHarmony Bench
Claude Sonnet 5.5—Kimi K357.3%SourceNot directly comparable
LiveCodeBench (Vals)
Claude Sonnet 5.5—Kimi K387.2%SourceNot directly comparable
SWE-bench (Vals)
Claude Sonnet 5.5—Kimi K393.4%SourceNot directly comparable
Reasoning
Multimodal
Chartography (no tools)
Claude Sonnet 5.561.6%SourceKimi K3—Not directly comparable
Chartography (tools)
Claude Sonnet 5.590.2%SourceKimi K3—Not directly comparable
BenchCAD Vision2Code (no tools)
Claude Sonnet 5.50.747SourceKimi K3—Not directly comparable
BenchCAD Vision2Code (tools)
Claude Sonnet 5.50.963SourceKimi K3—Not directly comparable
Biomedical image analysis
Claude Sonnet 5.572.2%SourceKimi K3—Not directly comparable
OfficeQA
Claude Sonnet 5.576.9%SourceKimi K3—Not directly comparable
OfficeQA Pro
Claude Sonnet 5.565.6%SourceKimi K363.3%SourceClaude Sonnet 5.5 leads this result
MMMU-Pro
Claude Sonnet 5.5—Kimi K381.6%SourceNot directly comparable
MMMU-Pro w/ Python
Claude Sonnet 5.5—Kimi K383.4%SourceNot directly comparable
CharXiv w/o tools
Claude Sonnet 5.5—Kimi K384.8%SourceNot directly comparable
CharXiv
Claude Sonnet 5.5—Kimi K391.3%SourceNot directly comparable
MathVision
Claude Sonnet 5.5—Kimi K394.3%SourceNot directly comparable
MathVision w/ Python
Claude Sonnet 5.5—Kimi K397.8%SourceNot directly comparable
BabyVision w/ Python
Claude Sonnet 5.5—Kimi K385.7%SourceNot directly comparable
ZeroBench
Claude Sonnet 5.5—Kimi K323.0%SourceNot directly comparable
ZeroBench w/ Python
Claude Sonnet 5.5—Kimi K341.0%SourceNot directly comparable
WorldVQA ForceAnswer
Claude Sonnet 5.5—Kimi K351.0%SourceNot directly comparable
OmniDocBench
Claude Sonnet 5.5—Kimi K391.1%SourceNot directly comparable
PerceptionBench
Claude Sonnet 5.5—Kimi K358.5%SourceNot directly comparable
Knowledge
HealthBench (raw)
Claude Sonnet 5.569.4%SourceKimi K3—Not directly comparable
HealthBench (length-adjusted)
Claude Sonnet 5.565.4%SourceKimi K3—Not directly comparable
HealthBench Professional (raw)
Claude Sonnet 5.577.1%SourceKimi K3—Not directly comparable
HealthBench Professional
Claude Sonnet 5.569.2%SourceKimi K3—Not directly comparable
BioMysteryBench (human-solvable)
Claude Sonnet 5.589.2%SourceKimi K3—Not directly comparable
BioMysteryBench (human-difficult)
Claude Sonnet 5.544.7%SourceKimi K3—Not directly comparable
SpatialBench Verified
Claude Sonnet 5.572.5%SourceKimi K3—Not directly comparable
SingleCellBench
Claude Sonnet 5.559.1%SourceKimi K3—Not directly comparable
Protein Design
Claude Sonnet 5.551.0%SourceKimi K3—Not directly comparable
Morphology-to-molecule matching
Claude Sonnet 5.525.0%SourceKimi K3—Not directly comparable
Medicinal chemistry
Claude Sonnet 5.565.3%SourceKimi K3—Not directly comparable
Protein Design library ranking
Claude Sonnet 5.554.8%SourceKimi K3—Not directly comparable
De novo protein-binder design
Claude Sonnet 5.582.3%SourceKimi K3—Not directly comparable
Protocols (troubleshooting)
Claude Sonnet 5.567.3%SourceKimi K3—Not directly comparable
Protocols (understanding)
Claude Sonnet 5.566.6%SourceKimi K3—Not directly comparable
HLE w/o tools
Claude Sonnet 5.556.9%SourceKimi K343.5%SourceClaude Sonnet 5.5 leads this result
GPQA
Claude Sonnet 5.5—Kimi K393.5%SourceNot directly comparable
GPQA-D
Claude Sonnet 5.5—Kimi K393.5%SourceNot directly comparable
HLE
Claude Sonnet 5.5—Kimi K356%SourceNot directly comparable
GPQA Diamond (Vals)
Claude Sonnet 5.5—Kimi K392.9%SourceNot directly comparable
MMLU-Pro (Vals)
Claude Sonnet 5.5—Kimi K388.0%SourceNot directly comparable
Multilingual
Math
87 public results · 6 shared
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Last updated September 28, 2026