Long documents
Prompts that approach the documented context limit
Kimi K2.5
Kimi K2.5 has the larger documented context window.
Updated October 17, 2026. Rank says Claude Sonnet 4.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Sonnet 4.6 has the higher public score estimate, 55.89 versus 53.05, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Prompts that approach the documented context limit
Kimi K2.5
Kimi K2.5 has the larger documented context window.
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.
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.
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.
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.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate.
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.
Directional only · BenchAlign v5.8
Claude Sonnet 4.6 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
5 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
SuperGPQAKnowledge
Normalized gap 25.8HLEKnowledge
Normalized gap 18.9OSWorld-VerifiedAgentic
Normalized gap 8.8Terminal-Bench 2.0Agentic
Normalized gap 8.3MMLU-ProKnowledge
Normalized gap 7.9Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | Claude Sonnet 4.6 | Kimi K2.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 46.3Supported · #55/128 | 40.7Estimated · #66/128 | Directional onlyBenchAlign v5.8 lane · 10 vs 16 public rows | Directional only |
| Coding | 44.7Supported · #54/150 | 38.5Estimated · #68/150 | Directional onlyBenchAlign v5.8 lane · 9 vs 10 public rows | Directional only |
| Multimodal | 58.7#45/54 | 70.4#32/54 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Knowledge | 55.4Supported · #58/184 | 51.4Estimated · #75/184 | Directional onlyBenchAlign v5.8 lane · 6 vs 8 public rows | Directional only |
| Instruction following | 48.0#99/148 | 85.8#48/148 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 62.8Unranked · 4 rankable rows | 72.2Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | 87.3#8/17 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 83.3Unranked · 7 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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.
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.
1K fresh input + 500 output tokens
Kimi K2.5 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K2.5 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Sonnet 4.6
200K
Kimi K2.5
256K
Claude Sonnet 4.6
Not sourced
Kimi K2.5
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 4.6
Not published
Kimi K2.5
Not published
Claude Sonnet 4.6
Not sourced
Kimi K2.5
Not sourced
Claude Sonnet 4.6
Not sourced
Kimi K2.5
Not sourced
Claude Sonnet 4.6
Not sourced
Kimi K2.5
Not sourced
Claude Sonnet 4.6
Non-Reasoning
Kimi K2.5
Non-Reasoning
Claude Sonnet 4.6
Proprietary
Kimi K2.5
Open Weight
Claude Sonnet 4.6
Proprietary
Kimi K2.5
Open Weight
Claude Sonnet 4.6
2026-02-01
Kimi K2.5
2026-01-27
Claude Sonnet 4.6 has the higher public score estimate, 55.89 versus 53.05, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Claude Sonnet 4.6 scores higher for coding on the public lane, 44.7 to 38.5. 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.
Claude Sonnet 4.6 scores higher for agentic tasks on the public lane, 46.3 to 40.7. 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.
For the stated presets, chat costs $0.0105 on Claude Sonnet 4.6 and $0.0021 on Kimi K2.5; repository review costs $0.195 and $0.039; the cache-heavy agent loop costs $0.81 and $0.162. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate.
Kimi K2.5 has the larger documented context window: 256K, compared with 200K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Claude Sonnet 4.6 leads this result
OSWorld-Verified
Claude Sonnet 4.6 leads this result
Claw-Eval
Shared sourceClaude Sonnet 4.6 leads this result
CyberGym
Not directly comparable
Gert Labs
Shared sourceClaude Sonnet 4.6 leads this result
OSWorld 2.0
Not directly comparable
JobBench
Shared sourceClaude Sonnet 4.6 leads this result
Terminal-Bench 2.1 (Vals)
Not directly comparable
ApprenticeBench
Not directly comparable
MazeBench
Not directly comparable
BrowseComp
Not directly comparable
QwenClawBench
Not directly comparable
τ³-bench results
Not directly comparable
DeepSearchQA
Not directly comparable
DeepPlanning
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Not directly comparable
MCP-Tasks
Not directly comparable
WideResearch
Not directly comparable
BFCL v4
Not directly comparable
ResearchClawBench
Not directly comparable
SWE-bench Verified
Claude Sonnet 4.6 leads this result
SWE-Rebench
Shared sourceClaude Sonnet 4.6 leads this result
React Native Evals
Shared sourceClaude Sonnet 4.6 leads this result
Terminal-Bench 2.0
Claude Sonnet 4.6 leads this result
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
SWE-bench Verified*
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
NL2Repo
Not directly comparable
SciCode
Not directly comparable
CharXiv
Kimi K2.5 leads this result
MMMU
Not directly comparable
MMMU-Pro
Not directly comparable
RealWorldQA
Not directly comparable
OmniDocBench 1.5
Not directly comparable
Video-MME
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
Video-MME (w/o subtitle)
Not directly comparable
MathVision
Not directly comparable
We-Math
Not directly comparable
DynaMath
Not directly comparable
SimpleVQA
Not directly comparable
MMLongBench-Doc
Not directly comparable
CC-OCR
Not directly comparable
AI2D_TEST
Not directly comparable
CountBench
Not directly comparable
RefCOCO (avg)
Not directly comparable
MMVU
Not directly comparable
VideoMMMU
Not directly comparable
MLVU (M-Avg)
Not directly comparable
GPQA
Claude Sonnet 4.6 leads this result
SuperGPQA
Shared sourceClaude Sonnet 4.6 leads this result
MMLU-Pro
Kimi K2.5 leads this result
HLE
Claude Sonnet 4.6 leads this result
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
GPQA-D
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
MMLU-Redux
Not directly comparable
C-Eval
Not directly comparable
MMLU-ProX
Not directly comparable
NOVA-63
Not directly comparable
INCLUDE
Not directly comparable
PolyMath
Not directly comparable
MAXIFE
Not directly comparable
IFEval
Not directly comparable
AIME 2025
Not directly comparable
AIME26
Not directly comparable
AIME25 (Arcee)
Not directly comparable
HMMT Feb 2025
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
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Last updated October 17, 2026