Gemini 3.5 Flash Lite vs Kimi K2.6
At a Glance
| Compare |  Gemini 3.5 Flash LiteGoogle DeepMind |  Kimi K2.6Moonshot AI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #39 of 4617.6 score · 3/3 sources · complete | #32 of 4650.5 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 33.6–67.0 |
| CostLower is better · Published-token output estimate | #8 of 44$0.029 per LiveBench case | #21 of 44$0.095 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #32 of 3846.2 score · 3/3 sources · complete | #24 of 3850.1 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 41.7–58.4 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.30Google AI ↗ · Aug 29, 2026 | $0.75Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $2.50Google AI ↗ · Aug 29, 2026 | $3.50Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 1,049K | 262K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Gemini 3.5 Flash-Lite | Kimi-K2.6 |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,435.5499% of row best · rating · gemini-3.5-flash-lite; 95% CI [1430.59505493, 1440.49024696]; votes 26165; rank 79 | 1,454.92100% of row best · rating · kimi-k2.6; 95% CI [1450.40277919, 1459.43693683]; votes 37502; rank 38 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,268.4299% of row best · rating · gemini-3.5-flash-lite; 95% CI [1258.88097193, 1277.96729164]; votes 5254; rank 37 | 1,280.40100% of row best · rating · kimi-k2.6; 95% CI [1273.38025179, 1287.42397051]; votes 15347; rank 28 |
| LiveBench2026-06-25 · overall · leader | 66.3589% of row best · percent · gemini-3.5-flash-lite-high · 11,526 output tokens / case | 74.18100% of row best · percent · kimi-k2.6-thinking · 27,001 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 73.4289% of row best · points · Gemini 3.5 Flash-Lite · 1,966 output tokens / case | 82.24100% of row best · points · Kimi K2.6 (thinking) · 18,080 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 0 benchmark wins | 2 benchmark winsOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Gemini 3.5 Flash-Lite | Kimi-K2.6 |
|---|---|---|
| Developer | Google DeepMind | Moonshot AI |
| Family | Gemini 3 | Kimi K2 6 |
| Model | Gemini 3.5 Flash-Lite | Kimi-K2.6 |
| Version | Gemini 3.5 Flash-Lite | Kimi-K2.6 |
| Lifecycle | active | active |
| Released | Unknown | 2026-04-20 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 262K |
| Total parameters | Unknown | 1T |
| Active parameters | Unknown | 32B |
| License | Unknown | other |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Gemini 3.5 Flash Lite Capabilities
Kimi K2.6 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.5 Flash Lite vs Kimi K2.6 FAQs
Is Gemini 3.5 Flash Lite or Kimi K2.6 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.5 Flash Lite and Kimi K2.6, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.5 Flash Lite or Kimi K2.6?+
Gemini 3.5 Flash Lite is $0.30 and Kimi K2.6 is $0.75 per million tokens, so Gemini 3.5 Flash Lite is cheaper on this metric. Gemini 3.5 Flash Lite is $2.50 and Kimi K2.6 is $3.50 per million tokens, so Gemini 3.5 Flash Lite is cheaper on this metric.
Which has a larger context window, Gemini 3.5 Flash Lite or Kimi K2.6?+
Gemini 3.5 Flash Lite has the larger sourced context window. Gemini 3.5 Flash Lite supports 1,049K and Kimi K2.6 supports 262K.
Which performs better in benchmarks, Gemini 3.5 Flash Lite or Kimi K2.6?+
Kimi K2.6 leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Gemini 3.5 Flash Lite or Kimi K2.6 be self-hosted?+
Kimi K2.6 is the only model in this pair currently marked as self-hostable. Gemini 3.5 Flash Lite is not marked open weight; Kimi K2.6 is open weight.
Can Gemini 3.5 Flash Lite and Kimi K2.6 understand images?+
Gemini 3.5 Flash Lite is documented with image input; Kimi K2.6 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.5 Flash Lite or Kimi K2.6?+
Neither has a larger sourced maximum output. Gemini 3.5 Flash Lite is 66K and Kimi K2.6 is —.
Do Gemini 3.5 Flash Lite and Kimi K2.6 support reasoning and tool use?+
Gemini 3.5 Flash Lite: reasoning, tool calling, and image input. Kimi K2.6: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.5 Flash Lite or Kimi K2.6?+
Gemini 3.5 Flash Lite has 2 sourced provider routes; Kimi K2.6 has 5, so Kimi K2.6 has broader tracked availability.
Which offers better value, Gemini 3.5 Flash Lite or Kimi K2.6?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.