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DeepSeekDeepSeek V4 FlashVSMoonshot AI (Kimi)Kimi K2.7 Code

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

Our Take

We recommend DeepSeek V4 Flash for practical local execution on standard developer hardware, or Kimi K2.7 Code if you need peak reasoning and have the VRAM (or a cloud API) to host it. While Kimi K2.7 Code offers frontier capability, its massive parameter size makes it extremely difficult to host locally. Choose DeepSeek V4 Flash for standard local setups, or Kimi K2.7 Code for peak reasoning.
▶WHY?
Benchmark Calculations & Evidence:
  • Model Size: Kimi K2.7 Code is a massive 1000B parameter model requiring heavy GPU infrastructure, while DeepSeek V4 Flash is a 284B parameter model.
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    Model Specs

    DeepSeek V4 Flash

    Open SourceAPI Available
    Website🤗HF

    Benchmarks & Scores

    Coding (swe-bench-pro)
    49.1%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)
    80%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)9.8x cheaper
    $0.17Input: $0.14 | Output: $0.28
    Context WindowLarger
    1.05M tokens
    Model Specs

    Kimi K2.7 Code

    Open SourceAPI Available
    Website🤗HF

    Benchmarks & Scores

    Coding (swe-bench-pro)Winner (+9.5%)
    58.6%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)Winner (+10.0%)
    90%

    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 DeepSeek V4 Flash vs Kimi K2.7 Code

    DeepSeek V4 Flash is cheaper than Kimi K2.7 Code. DeepSeek V4 Flash has a blended cost of $0.17/1M tokens, which is about 9.8x cheaper than Kimi K2.7 Code at $1.71/1M tokens.

    Kimi K2.7 Code is better for coding tasks on this benchmark. It scores 58.6% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to DeepSeek V4 Flash which scores 49.1%.

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