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GoogleGemini 3.5 FlashVSMoonshot AI (Kimi)kimi-k2.5

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

Our Take

We recommend Gemini 3.5 Flash if you need peak intelligence for reasoning and coding tasks, or the 2.8x cheaper kimi-k2.5 to optimize your budget for high-volume pipelines. While Gemini 3.5 Flash holds a clear performance lead, it carries a heavy price premium. Choose Gemini 3.5 Flash for complex logic, or kimi-k2.5 for budget efficiency.
WHY?
Benchmark Calculations & Evidence:
  • Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Gemini 3.5 Flash scored 55.1%, while kimi-k2.5 scored 50.7%.
  • Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Gemini 3.5 Flash scored 92.2%, while kimi-k2.5 scored 87.6%.
  • Cost Efficiency: kimi-k2.5 pricing ($0.6/M input, $3/M output) is 2.8x cheaper than Gemini 3.5 Flash ($1.5/M input, $9/M output).
  • Was this recommendation helpful?
    Model Specs

    Gemini 3.5 Flash

    Benchmarks & Scores

    Coding (swe-bench-pro)Winner (+4.4%)
    55.1%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)Winner (+4.6%)
    92.2%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)
    $3.38Input: $1.50 | Output: $9.00
    Context WindowLarger
    1.05M tokens
    Model Specs

    kimi-k2.5

    Open SourceAPI Available

    Benchmarks & Scores

    Coding (swe-bench-pro)
    50.7%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)
    87.6%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)2.8x cheaper
    $1.20Input: $0.60 | Output: $3.00
    Context Window
    262.14k tokens

    Frequently Asked Questions about Gemini 3.5 Flash vs kimi-k2.5

    kimi-k2.5 is cheaper than Gemini 3.5 Flash. kimi-k2.5 has a blended cost of $1.20/1M tokens, which is about 2.8x cheaper than Gemini 3.5 Flash at $3.38/1M tokens.

    Gemini 3.5 Flash is better for coding tasks on this benchmark. It scores 55.1% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to kimi-k2.5 which scores 50.7%.

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