Moonshot AI (Kimi)kimi-k2.5VSxAIGrok 4.20
Our Take
We recommend kimi-k2.5 for a 2.5x API cost saving at identical performance levels. While both models deliver similar intelligence, kimi-k2.5 is the optimal choice for high-volume pipelines. Choose kimi-k2.5 for budget efficiency without sacrificing quality.
▶WHY?
Benchmark Calculations & Evidence:
Performance Match: Both models perform almost identically, with an average score gap of just 1.8% across reasoning and coding benchmarks.
Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. kimi-k2.5 scored 50.7%, while Grok 4.20 scored 51.8%.
Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. kimi-k2.5 scored 87.6%, while Grok 4.20 scored 90%.
Cost Efficiency: kimi-k2.5 pricing ($0.6/M input, $3/M output) is 2.5x cheaper than Grok 4.20 ($2/M input, $6/M output).
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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)2.5x cheaper
$1.20Input: $0.60 | Output: $3.00Context Window
262.14k tokensBenchmarks & Scores
Coding (swe-bench-pro)Winner (+1.1%)
51.8%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+2.4%)
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-k2.5 vs Grok 4.20
kimi-k2.5 is cheaper than Grok 4.20. kimi-k2.5 has a blended cost of $1.20/1M tokens, which is about 2.5x cheaper than Grok 4.20 at $3.00/1M tokens.
Grok 4.20 is better for coding tasks on this benchmark. It scores 51.8% 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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