GoogleGemini 3.5 Flash LiteVSMoonshot AI (Kimi)kimi-k2.6

Analysis by:the whichllmmodel Editorial Team|Updated: June 2026

Our Take

If your budget allows, kimi-k2.6 is the superior choice here, offering a clear benchmark advantage while carrying only a moderate price premium (2.0x). We recommend choosing Gemini 3.5 Flash Lite only if you need to optimize costs for very high-volume pipelines.

Was this recommendation helpful?

Model Specs

Gemini 3.5 Flash Lite

Website

Benchmarks & Scores

Coding (swe-bench-pro)

54.2%

excellent at multi-file repositories, autonomous agents, and industrial codebases

Reasoning (gpqa-diamond)

84%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)2.0x cheaper

$0.85Input: $0.30 | Output: $2.50

Context WindowLarger

1.05M tokens

Model Specs

kimi-k2.6

Open SourceAPI Available

Website 🤗HF

Benchmarks & Scores

Coding (swe-bench-pro)Winner (+4.4%)

58.6%

excellent at multi-file repositories, autonomous agents, and industrial codebases

Reasoning (gpqa-diamond)Winner (+6.5%)

90.5%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)

$1.71Input: $0.95 | Output: $4.00

Context Window

262.14k tokens

Read our data collection methodology

Frequently Asked Questions about Gemini 3.5 Flash Lite vs kimi-k2.6

Gemini 3.5 Flash Lite is cheaper than kimi-k2.6. Gemini 3.5 Flash Lite has a blended cost of $0.85/1M tokens, which is about 2.0x cheaper than kimi-k2.6 at $1.71/1M tokens.

kimi-k2.6 is better for coding tasks on this benchmark. It scores 58.6% on swe-bench-pro (excellent at multi-file repositories, autonomous agents, and industrial codebases) compared to Gemini 3.5 Flash Lite which scores 54.2%.

Related Matchups

Explore similar comparisons for Gemini 3.5 Flash Lite and kimi-k2.6.

Browse More Comparisons

GoogleGemini 3.5 Flash Lite

OpenAIGPT-5.5

Compare Specs

Moonshot AI (Kimi)kimi-k2.6

OpenAIGPT-5.6 Sol

Compare Specs

GoogleGemini 3.5 Flash Lite

OpenAIGPT-5.6 Terra

Compare Specs

Do you want to find a model for your constraints?

Use our interactive model finder to filter LLMs by reasoning capability, coding performance, cost, and context length.

Open Model Finder