Updated October 19, 2026. Rank says Kimi K2.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Kimi K2.6 has the higher public point estimate, 58.4 versus 49.61. Their conditional score ranges overlap. These ranges do not establish rank confidence. 9 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 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Kimi K2.6
Kimi K2.6 has the higher public agentic point estimate, 44.3 to 35.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the larger documented context window.
1K fresh input + 500 output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
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.
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 31.2FrontierMath v2 (Tier 4)Math
Normalized gap 25.6LiveCodeBench (Vals)Coding
Normalized gap 7.8SWE-bench ProCoding
Normalized gap 4.4MMLU-Pro (Vals)Knowledge
Normalized gap 1.8Each 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 | Gemini 3.5 Flash-Lite | Kimi K2.6 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 35.6Supported · #81/130 | 44.3Supported · #61/130 | Like-for-likeBenchAlign v5.8 lane · 3 vs 12 public rows | Kimi K2.6 leads · intervals overlap |
| Coding | 35.8Supported · #77/151 | 43.2Supported · #59/151 | Like-for-likeBenchAlign v5.8 lane · 4 vs 10 public rows | Kimi K2.6 leads · intervals overlap |
| Knowledge | 49.1Supported · #86/186 | 60.3Supported · #49/186 | Like-for-likeBenchAlign v5.8 lane · 2 vs 5 public rows | Kimi K2.6 leads · intervals overlap |
| Reasoning | 70.9Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 80.9Unranked · 1 rankable row | 73.8#27/55 | Not comparableProvisional lane · 0 vs 2 weighted 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 | 37.8Unranked · 2 rankable rows | 76.8#4/12 | Not comparableProvisional lane · 2 vs 4 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
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
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.
Gemini 3.5 Flash-Lite
Kimi K2.6
256K
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationKimi K2.6
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingKimi K2.6
$0.16 per 1M cached input tokens
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationKimi K2.6
Not sourced
Gemini 3.5 Flash-Lite
Kimi K2.6
Not sourced
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideKimi K2.6
Not sourced
Gemini 3.5 Flash-Lite
Reasoning
Kimi K2.6
Reasoning
Gemini 3.5 Flash-Lite
Proprietary
Kimi K2.6
Open Weight
Gemini 3.5 Flash-Lite
Proprietary
Kimi K2.6
Open Weight
Gemini 3.5 Flash-Lite
2026-07-21
Kimi K2.6
2026-04-20
Kimi K2.6 has the higher public point estimate, 58.4 versus 49.61. Their conditional score ranges overlap. These ranges do not establish rank confidence. 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 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Kimi K2.6 has the higher public agentic tasks point estimate, 44.3 to 35.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.00295 on Kimi K2.6; repository review costs $0.0225 and $0.0595; the cache-heavy agent loop costs $0.037 and $0.091. Costs use the listed standard API rates.
Gemini 3.5 Flash-Lite 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 2.1
Not directly comparable
OSWorld-Verified
Gemini 3.5 Flash-Lite leads this result
Terminal-Bench 2.1 (Vals)
Kimi K2.6 leads this result
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Not directly comparable
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
WideResearch
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
Kimi K2.6 leads this result
LiveCodeBench (Vals)
Kimi K2.6 leads this result
SWE-bench (Vals)
Kimi K2.6 leads this result
SWE-bench Verified
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE Multilingual
Not directly comparable
SciCode
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
MRCRv2
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
CharXiv
Not directly comparable
MathVision
Not directly comparable
V*
Not directly comparable
GPQA Diamond (Vals)
Kimi K2.6 leads this result
MMLU-Pro (Vals)
Kimi K2.6 leads this result
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceKimi K2.6 leads this result
FrontierMath v2 (Tier 4)
Shared sourceKimi K2.6 leads this result
AIME26
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
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Last updated October 19, 2026
