GoogleGemini 3.6 FlashVSMoonshot AI (Kimi)kimi-k2.6

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

Our Take

Both models perform almost identically, with an average score gap of just 1.3%. We recommend choosing kimi-k2.6 as it is the more cost-effective option (being 1.8x cheaper) without sacrificing any real-world capability.

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Model Specs

Gemini 3.6 Flash

Website

Benchmarks & Scores

Coding (swe-bench-pro)Winner (+0.1%)

58.7%

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

Reasoning (gpqa-diamond)Winner (+2.5%)

93%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)

$3.00Input: $1.50 | Output: $7.50

Context WindowLarger

1.05M tokens

Model Specs

kimi-k2.6

Open SourceAPI Available

Website 🤗HF

Benchmarks & Scores

Coding (swe-bench-pro)

58.6%

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

Reasoning (gpqa-diamond)

90.5%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)1.8x cheaper

$1.71Input: $0.95 | Output: $4.00

Context Window

262.14k tokens

Read our data collection methodology

Frequently Asked Questions about Gemini 3.6 Flash vs kimi-k2.6

kimi-k2.6 is cheaper than Gemini 3.6 Flash. kimi-k2.6 has a blended cost of $1.71/1M tokens, which is about 1.8x cheaper than Gemini 3.6 Flash at $3.00/1M tokens.

Gemini 3.6 Flash is better for coding tasks on this benchmark. It scores 58.7% on swe-bench-pro (excellent at multi-file repositories, autonomous agents, and industrial codebases) compared to kimi-k2.6 which scores 58.6%.

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