Updated October 2, 2026. We do not rank this pair: at least one has no public score. 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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 7 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.
Code generation, repair, and software-engineering tasks
Kimi K3
Kimi K3 has the higher public coding point estimate, 61.4 to 37.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Kimi K3
Kimi K3 has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiMo-V2.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · BenchAlign v5.8
Kimi K3 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.
2 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 10.3LiveCodeBench (Vals)Coding
Normalized gap 5.7MMLU-Pro (Vals)Knowledge
Normalized gap 5.1MMMU-ProMultimodal
Normalized gap 3.7Each 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 | Kimi K3 | MiMo-V2.5 | Basis | Reading |
|---|---|---|---|---|
| Coding | 61.4Supported · #18/144 | 37.5Supported · #71/144 | Like-for-likeBenchAlign v5.8 lane · 14 vs 4 public rows | Kimi K3 leads |
| Agentic | 68.1Supported · #8/119 | 30.1Estimated · #81/119 | Directional onlyBenchAlign v5.8 lane · 12 vs 6 public rows | Directional only |
| Multimodal | 89.4#1/49 | 60.1#30/49 | Directional onlyProvisional lane · 3 vs 2 weighted rows | Directional only |
| Reasoning | 65.8#18/27 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 67.9Supported · #18/171 | Not ranked | Not comparableBenchAlign v5.8 lane · 6 vs 2 public 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 v5.8) 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
MiMo-V2.5 has no comparable published API token rate.
50K fresh input + 3K output tokens
MiMo-V2.5 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
MiMo-V2.5 has no comparable published API token 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.
Kimi K3
1.05M
MiMo-V2.5
1M
Kimi K3
Not sourced
MiMo-V2.5
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K3
$0.3 per 1M cached input tokens
MiMo-V2.5
No comparable hosted API rate
Kimi K3
Not sourced
MiMo-V2.5
Not sourced
Kimi K3
Not sourced
MiMo-V2.5
Not sourced
Kimi K3
Not sourced
MiMo-V2.5
Not sourced
Kimi K3
Reasoning
MiMo-V2.5
Reasoning
Kimi K3
Pending
MiMo-V2.5
Proprietary
Kimi K3
Pending
MiMo-V2.5
Proprietary
Kimi K3
2026-07-16
MiMo-V2.5
2026-04-22
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Kimi K3 has the higher public coding point estimate, 61.4 to 37.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Kimi K3 scores higher for agentic tasks on the public lane, 68.1 to 30.1. MiMo-V2.5 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Kimi K3 has the larger documented context window: 1.05M, compared with 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
Not directly comparable
BrowseComp
Not directly comparable
DeepSearchQA
Not directly comparable
Toolathlon-Verified
Not directly comparable
MCP Atlas
Not directly comparable
AutomationBench
Not directly comparable
JobBench
Not directly comparable
APEX-Agents
Not directly comparable
SpreadsheetBench 2
Not directly comparable
DECK-Bench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Kimi K3 leads this result
ApprenticeBench
Not directly comparable
Claw-Eval
Not directly comparable
MM-ClawBench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
DeepSWE
Not directly comparable
CursorBench 3.2
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
OpenHarmony Bench
Not directly comparable
FrontierSWE v2
Not directly comparable
LiveCodeBench (Vals)
Kimi K3 leads this result
SWE-bench (Vals)
Kimi K3 leads this result
PostTrainBench v1.1
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
OfficeQA Pro
Not directly comparable
MMMU-Pro
Kimi K3 leads this result
MMMU-Pro w/ Python
Not directly comparable
CharXiv w/o tools
Not directly comparable
CharXiv
Kimi K3 leads this result
MathVision
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
Video-MME (with subtitle)
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Kimi K3 leads this result
MMLU-Pro (Vals)
Kimi K3 leads this result
Gray Swan IPI (15 attempts)
Not directly comparable
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Last updated October 2, 2026
