GoogleGemini 3.6 FlashVSMoonshot AI (Kimi)Kimi K2.7 Code

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

Our Take

Both models perform almost identically, with an average score gap of just 1.6%. We recommend choosing Kimi K2.7 Code 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 (+3.0%)

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 K2.7 Code

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%

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 K2.7 Code

Kimi K2.7 Code is cheaper than Gemini 3.6 Flash. Kimi K2.7 Code 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 K2.7 Code which scores 58.6%.

Related Matchups

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