Updated October 19, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 13 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 K2.6
Kimi K2.6 has the higher public coding point estimate, 43.2 to 34.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
MiMo-V2.5
MiMo-V2.5 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 K2.6 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.
1 category rests 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.
LiveCodeBench (Vals)Coding
Normalized gap 5.3MMLU-Pro (Vals)Knowledge
Normalized gap 4.7SWE-bench ProCoding
Normalized gap 2.5MMMU-ProMultimodal
Normalized gap 1.5Terminal-Bench 2.0Agentic
Normalized gap 0.9Each 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 | Kimi K2.6 | MiMo-V2.5 | Basis | Reading |
|---|---|---|---|---|
| Coding | 43.2Supported · #59/151 | 34.8Supported · #79/151 | Like-for-likeBenchAlign v5.8 lane · 10 vs 4 public rows | Kimi K2.6 leads · intervals overlap |
| Multimodal | 73.8#27/55 | 72.2#32/55 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Kimi K2.6 leads |
| Agentic | 44.3Supported · #61/130 | 33.3Estimated · #88/130 | Directional onlyBenchAlign v5.8 lane · 12 vs 6 public rows | Directional only |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 60.3Supported · #49/186 | Not ranked | Not comparableBenchAlign v5.8 lane · 5 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 | 76.8#4/12 | Not ranked | Not comparableProvisional lane · 4 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 K2.6
256K
MiMo-V2.5
1M
Kimi K2.6
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 K2.6
$0.16 per 1M cached input tokens
MiMo-V2.5
No comparable hosted API rate
Kimi K2.6
Not sourced
MiMo-V2.5
Not sourced
Kimi K2.6
Not sourced
MiMo-V2.5
Not sourced
Kimi K2.6
Not sourced
MiMo-V2.5
Not sourced
Kimi K2.6
Reasoning
MiMo-V2.5
Reasoning
Kimi K2.6
Open Weight
MiMo-V2.5
Proprietary
Kimi K2.6
Open Weight
MiMo-V2.5
Proprietary
Kimi K2.6
2026-04-20
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 K2.6 has the higher public coding point estimate, 43.2 to 34.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Kimi K2.6 scores higher for agentic tasks on the public lane, 44.3 to 33.3. 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.
MiMo-V2.5 has the larger documented context window: 1M, compared with 256K.
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
Kimi K2.6 leads this result
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Not directly comparable
Claw-Eval
Shared sourceTie
DeepSearchQA
Not directly comparable
WideResearch
Not directly comparable
Gert Labs
Shared sourceKimi K2.6 leads this result
ResearchClawBench
Shared sourceKimi K2.6 leads this result
OSWorld 2.0
Not directly comparable
Terminal-Bench 2.1 (Vals)
MiMo-V2.5 leads this result
MM-ClawBench
Not directly comparable
SWE-bench Verified
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Kimi K2.6 leads this result
SWE Multilingual
Not directly comparable
SciCode
Not directly comparable
Terminal-Bench 2.0
Kimi K2.6 leads this result
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
LiveCodeBench (Vals)
Kimi K2.6 leads this result
SWE-bench (Vals)
Kimi K2.6 leads this result
MMMU-Pro
Kimi K2.6 leads this result
MMMU-Pro w/ Python
Not directly comparable
CharXiv
MiMo-V2.5 leads this result
MathVision
Not directly comparable
V*
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Kimi K2.6 leads this result
MMLU-Pro (Vals)
Kimi K2.6 leads this result
AIME26
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
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Last updated October 19, 2026
