Agentic
Directional only- Claude Opus 4.5
- 62.6
- Kimi K3
- 89.5
- Weighted basis
- 2 vs 2 rows
- Reading
- Directional only
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start free briefUpdated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Kimi K3 has the higher public score, 80.53 versus 63.65, and the 90% score intervals do not overlap.
8 results are shared. Category rows based on 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 K3
Kimi K3 has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Kimi K3
Kimi K3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
50K fresh input + 3K output tokens
Kimi K3
Kimi K3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
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.
Confidence: rate-fallback
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
| Category | Claude Opus 4.5 | Kimi K3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 62.6 | 89.5 | Directional only2 vs 2 rows | Directional only |
| Knowledge | 58.1 | 61.0 | Directional only4 vs 2 rows | Directional only |
| Multimodal | 69.9 | 78.5 | Directional only2 vs 3 rows | Directional only |
| Coding | 71.7 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Reasoning | 64.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 57.5 | Not measured | Not comparable4 vs 0 rows | Not comparable |
| Multilingual | 85.7 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Instruction following | 69.5 | Not measured | Not comparable2 vs 0 rows | Not comparable |
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.
Terminal-Bench 2.0
Agentic
HLE
Knowledge
CharXiv
Multimodal
MMMU-Pro
Multimodal
GPQA
Knowledge
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 K3 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K3 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.
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 K3
1.05M
Claude Opus 4.5
Not sourced
Kimi K3
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 K3
$0.3 per 1M cached input tokens
Claude Opus 4.5
Not sourced
Kimi K3
Not sourced
Claude Opus 4.5
Not sourced
Kimi K3
Not sourced
Claude Opus 4.5
Not sourced
Kimi K3
Not sourced
Claude Opus 4.5
Non-Reasoning
Kimi K3
Reasoning
Claude Opus 4.5
Proprietary
Kimi K3
Pending
Claude Opus 4.5
Proprietary
Kimi K3
Pending
Claude Opus 4.5
2025-11-01
Kimi K3
2026-07-16
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Kimi K3 leads this result
OSWorld-Verified
Not directly comparable
OSWorld
Not directly comparable
Claw-Eval
Not directly comparable
QwenClawBench
Not directly comparable
τ³-bench results
Not directly comparable
VITA-Bench
Not directly comparable
DeepPlanning
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Kimi K3 leads this result
MCP-Tasks
Not directly comparable
WideResearch
Not directly comparable
CyberGym
Not directly comparable
Gert Labs
Not directly comparable
JobBench
Kimi K3 leads this result
BrowseComp
Not directly comparable
DeepSearchQA
Not directly comparable
Toolathlon-Verified
Not directly comparable
AutomationBench
Not directly comparable
APEX-Agents
Not directly comparable
SpreadsheetBench 2
Not directly comparable
DECK-Bench
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
deepSwe
Not directly comparable
cursorBench32
Not directly comparable
FrontierSWE
Not directly comparable
ProgramBench
Not directly comparable
Kimi Code Bench v2
Not directly comparable
sweMarathon
Not directly comparable
PostTrain Bench
Not directly comparable
MLS-Bench Lite
Not directly comparable
VulcanBench v3
Not directly comparable
APEX-SWE
Not directly comparable
EEBench
Not directly comparable
InferenceEval
Not directly comparable
KernelBench Internal
Not directly comparable
GPQA
Kimi K3 leads this result
SuperGPQA
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-Redux
Not directly comparable
C-Eval
Not directly comparable
HLE
Kimi K3 leads this result
GPQA-D
Not directly comparable
HLE w/o tools
Not directly comparable
AIME26
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
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
MMMU-Pro
Kimi K3 leads this result
MathVision
Kimi K3 leads this result
CharXiv
Kimi K3 leads this result
VideoMMMU
Not directly comparable
ScreenSpot Pro
Not directly comparable
V*
Not directly comparable
OfficeQA Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
CharXiv w/o tools
Not directly comparable
MathVision w/ Python
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench
Not directly comparable
ZeroBench w/ Python
Not directly comparable
WorldVQA ForceAnswer
Not directly comparable
OmniDocBench
Not directly comparable
PerceptionBench
Not directly comparable
Kimi K3 has the higher public score, 80.53 versus 63.65, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. 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 $0.0105 on Kimi K3; repository review costs $0.325 and $0.195; the cache-heavy agent loop costs $1.35 and $0.27. 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 K3 has the larger documented context window: 1.05M, compared with 200K.
Last updated August 19, 2026
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