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 Opus 4.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Opus 4.5 has the higher public score estimate, 54.05 versus 53.05, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 51 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
Claude Opus 4.5 and Kimi K2.5 are 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
Claude Opus 4.5 and Kimi K2.5 are 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 Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 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 Opus 4.5 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.
4 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.
CharXivMultimodal
Normalized gap 9.0Terminal-Bench 2.0Agentic
Normalized gap 8.5MMMU-ProMultimodal
Normalized gap 7.9SWE-bench ProCoding
Normalized gap 6.4SWE MultilingualCoding
Normalized gap 4.5Each 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 Opus 4.5 | Kimi K2.5 | Basis | Reading |
|---|---|---|---|---|
| Multimodal | 48.6#49/54 | 70.4#32/54 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Kimi K2.5 leads |
| Multilingual | 96.5#2/17 | 87.3#8/17 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Claude Opus 4.5 leads |
| Agentic | 32.3Estimated · #89/128 | 40.7Estimated · #66/128 | Directional onlyBenchAlign v5.8 lane · 15 vs 16 public rows | Directional only |
| Coding | 40.3Estimated · #64/150 | 38.5Estimated · #68/150 | Directional onlyBenchAlign v5.8 lane · 6 vs 10 public rows | Directional only |
| Knowledge | 51.2Estimated · #77/184 | 51.4Estimated · #75/184 | Directional onlyBenchAlign v5.8 lane · 6 vs 8 public rows | Directional only |
| Instruction following | 31.9#131/148 | 85.8#48/148 | Directional onlyProvisional lane · 1 vs 0 weighted rows | Directional only |
| Reasoning | 78.3Unranked · 4 rankable rows | 72.2Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Math | 61.3#9/12 | 83.3Unranked · 7 rankable rows | Not comparableProvisional lane · 4 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 Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 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 Opus 4.5
200K
Kimi K2.5
256K
Claude Opus 4.5
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 Opus 4.5
Not published
Kimi K2.5
Not published
Claude Opus 4.5
Not sourced
Kimi K2.5
Not sourced
Claude Opus 4.5
Not sourced
Kimi K2.5
Not sourced
Claude Opus 4.5
Not sourced
Kimi K2.5
Not sourced
Claude Opus 4.5
Non-Reasoning
Kimi K2.5
Non-Reasoning
Claude Opus 4.5
Proprietary
Kimi K2.5
Open Weight
Claude Opus 4.5
Proprietary
Kimi K2.5
Open Weight
Claude Opus 4.5
2025-11-24
Kimi K2.5
2026-01-27
Claude Opus 4.5 has the higher public score estimate, 54.05 versus 53.05, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Claude Opus 4.5 scores higher for coding on the public lane, 40.3 to 38.5. Claude Opus 4.5 and Kimi K2.5 are 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.
Kimi K2.5 scores higher for agentic tasks on the public lane, 40.7 to 32.3. Claude Opus 4.5 and Kimi K2.5 are 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 $0.0175 on Claude Opus 4.5 and $0.0021 on Kimi K2.5; repository review costs $0.325 and $0.039; the cache-heavy agent loop costs $1.35 and $0.162. Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 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 Opus 4.5 leads this result
OSWorld-Verified
Claude Opus 4.5 leads this result
OSWorld
Not directly comparable
Claw-Eval
Claude Opus 4.5 leads this result
QwenClawBench
Shared sourceKimi K2.5 leads this result
τ³-bench results
Shared sourceClaude Opus 4.5 leads this result
VITA-Bench
Not directly comparable
DeepPlanning
Shared sourceClaude Opus 4.5 leads this result
Toolathlon
Claude Opus 4.5 leads this result
MCP Atlas
Claude Opus 4.5 leads this result
MCP-Tasks
Shared sourceClaude Opus 4.5 leads this result
WideResearch
Claude Opus 4.5 leads this result
CyberGym
Not directly comparable
Gert Labs
Shared sourceClaude Opus 4.5 leads this result
JobBench
Shared sourceClaude Opus 4.5 leads this result
BrowseComp
Not directly comparable
DeepSearchQA
Not directly comparable
BFCL v4
Not directly comparable
ResearchClawBench
Not directly comparable
SWE-bench Verified
Claude Opus 4.5 leads this result
LiveCodeBench v6
Kimi K2.5 leads this result
SWE-bench Pro
Claude Opus 4.5 leads this result
SWE Multilingual
Claude Opus 4.5 leads this result
NL2Repo
Claude Opus 4.5 leads this result
Terminal-Bench 2.0
Claude Opus 4.5 leads this result
SWE-bench Verified*
Not directly comparable
SWE-Rebench
Not directly comparable
React Native Evals
Not directly comparable
SciCode
Not directly comparable
MMMU
Shared sourceKimi K2.5 leads this result
MMMU-Pro
Kimi K2.5 leads this result
RealWorldQA
Shared sourceKimi K2.5 leads this result
OmniDocBench 1.5
Kimi K2.5 leads this result
Video-MME (with subtitle)
Shared sourceKimi K2.5 leads this result
Video-MME (w/o subtitle)
Shared sourceKimi K2.5 leads this result
MathVision
Kimi K2.5 leads this result
We-Math
Shared sourceKimi K2.5 leads this result
DynaMath
Shared sourceKimi K2.5 leads this result
SimpleVQA
Kimi K2.5 leads this result
CharXiv
Kimi K2.5 leads this result
CountBench
Shared sourceKimi K2.5 leads this result
AI2D_TEST
Shared sourceKimi K2.5 leads this result
ERQA
Not directly comparable
VideoMMMU
Shared sourceKimi K2.5 leads this result
MLVU (M-Avg)
Shared sourceKimi K2.5 leads this result
ScreenSpot Pro
Not directly comparable
V*
Not directly comparable
Video-MME
Not directly comparable
MMLongBench-Doc
Not directly comparable
CC-OCR
Not directly comparable
RefCOCO (avg)
Not directly comparable
MMVU
Not directly comparable
GPQA
Kimi K2.5 leads this result
SuperGPQA
Shared sourceClaude Opus 4.5 leads this result
MMLU-Pro
Claude Opus 4.5 leads this result
MMLU-Redux
Claude Opus 4.5 leads this result
C-Eval
Kimi K2.5 leads this result
HLE
Claude Opus 4.5 leads this result
GPQA-D
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
MMLU-ProX
Shared sourceClaude Opus 4.5 leads this result
NOVA-63
Shared sourceClaude Opus 4.5 leads this result
INCLUDE
Shared sourceClaude Opus 4.5 leads this result
PolyMath
Shared sourceClaude Opus 4.5 leads this result
MAXIFE
Shared sourceClaude Opus 4.5 leads this result
IFEval
Shared sourceKimi K2.5 leads this result
IFBench
Not directly comparable
AIME26
Kimi K2.5 leads this result
HMMT Feb 2025
Kimi K2.5 leads this result
HMMT Nov 2025
Claude Opus 4.5 leads this result
HMMT Feb 2026
Kimi K2.5 leads this result
MMAnswerBench
Claude Opus 4.5 leads this result
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
AIME 2025
Not directly comparable
AIME25 (Arcee)
Not directly comparable
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 5,600+ readers.
Last updated October 17, 2026