Moonshot AI (Kimi)kimi-k2.6VSxAIGrok 4.20
Our Take
We recommend choosing kimi-k2.6 as it delivers superior coding capability while matching the reasoning accuracy of Grok 4.20 at a 1.8x API cost saving. Choose kimi-k2.6 for superior overall value and coding efficiency.
▶WHY?
Benchmark Calculations & Evidence:
Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. kimi-k2.6 scored 58.6%, while Grok 4.20 scored 51.8%.
Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. kimi-k2.6 scored 90.5%, while Grok 4.20 scored 90%.
Cost Efficiency: Grok 4.20 pricing ($2/M input, $6/M output) is 1.8x cheaper than kimi-k2.6 ($0.95/M input, $4/M output).
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Benchmarks & Scores
Coding (swe-bench-pro)Winner (+6.8%)
58.6%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+0.5%)
90.5%graduate-level science QA
Cost & Context
Cost (per 1M tokens)1.8x cheaper
$1.71Input: $0.95 | Output: $4.00Context Window
262.14k tokensBenchmarks & Scores
Coding (swe-bench-pro)
51.8%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
90%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$3.00Input: $2.00 | Output: $6.00Context WindowLarger
1.05M tokensFrequently Asked Questions about kimi-k2.6 vs Grok 4.20
kimi-k2.6 is cheaper than Grok 4.20. kimi-k2.6 has a blended cost of $1.71/1M tokens, which is about 1.8x cheaper than Grok 4.20 at $3.00/1M tokens.
kimi-k2.6 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 Grok 4.20 which scores 51.8%.
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