Moonshot AI (Kimi)Kimi K2.7 CodeVSxAIGrok 4.20
Our Take
We recommend choosing Kimi K2.7 Code as it delivers superior coding capability while matching the reasoning accuracy of Grok 4.20 at a 1.8x API cost saving. Choose Kimi K2.7 Code for superior overall value and coding efficiency.
▶WHY?
Benchmark Calculations & Evidence:
Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Kimi K2.7 Code scored 58.6%, while Grok 4.20 scored 51.8%.
Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Kimi K2.7 Code scored 90%, while Grok 4.20 scored 90%.
Cost Efficiency: Grok 4.20 pricing ($2/M input, $6/M output) is 1.8x cheaper than Kimi K2.7 Code ($0.95/M input, $4/M output).
Was this recommendation helpful?
Benchmarks & Scores
Coding (swe-bench-pro)Winner (+6.8%)
58.6%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
90%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 K2.7 Code vs Grok 4.20
Kimi K2.7 Code is cheaper than Grok 4.20. Kimi K2.7 Code 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 K2.7 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 Grok 4.20 which scores 51.8%.
Related Matchups
Explore similar comparisons for Kimi K2.7 Code and Grok 4.20.
Do you want to find a model for your constraints?
Use our interactive model finder to filter LLMs by reasoning capability, coding performance, cost, and context length.