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AnthropicClaude Fable 5VSMoonshot AI (Kimi)kimi-k2.6

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

Our Take

We recommend Claude Fable 5 if you need peak intelligence for reasoning and coding tasks, or the 11.7x cheaper kimi-k2.6 to optimize your budget for high-volume pipelines. While Claude Fable 5 holds a clear performance lead, it carries a heavy price premium. Choose Claude Fable 5 for complex logic, or kimi-k2.6 for budget efficiency.
WHY?
Benchmark Calculations & Evidence:
  • Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Claude Fable 5 scored 80.3%, while kimi-k2.6 scored 58.6%.
  • Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Claude Fable 5 scored 94.1%, while kimi-k2.6 scored 90.5%.
  • Cost Efficiency: kimi-k2.6 pricing ($0.95/M input, $4/M output) is 11.7x cheaper than Claude Fable 5 ($10/M input, $50/M output).
  • Was this recommendation helpful?
    Model Specs

    Claude Fable 5

    Benchmarks & Scores

    Coding (swe-bench-pro)Winner (+21.7%)
    80.3%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)Winner (+3.6%)
    94.1%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)
    $20.00Input: $10.00 | Output: $50.00
    Context WindowLarger
    1.05M tokens
    Model Specs

    kimi-k2.6

    Open SourceAPI Available

    Benchmarks & Scores

    Coding (swe-bench-pro)
    58.6%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)
    90.5%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)11.7x cheaper
    $1.71Input: $0.95 | Output: $4.00
    Context Window
    262.14k tokens

    Frequently Asked Questions about Claude Fable 5 vs kimi-k2.6

    kimi-k2.6 is cheaper than Claude Fable 5. kimi-k2.6 has a blended cost of $1.71/1M tokens, which is about 11.7x cheaper than Claude Fable 5 at $20.00/1M tokens.

    Claude Fable 5 is better for coding tasks on this benchmark. It scores 80.3% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to kimi-k2.6 which scores 58.6%.

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