Moonshot AI (Kimi)Kimi K2.7 CodeVSOpenAIGPT-5.4 nano
Our Take
We recommend Kimi K2.7 Code if you need peak intelligence for reasoning and coding tasks, or the 3.7x cheaper GPT-5.4 nano to optimize your budget for high-volume pipelines. While Kimi K2.7 Code holds a clear performance lead, it carries a heavy price premium. Choose Kimi K2.7 Code for complex logic, or GPT-5.4 nano for budget 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 GPT-5.4 nano scored 52.4%.
Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Kimi K2.7 Code scored 90%, while GPT-5.4 nano scored 82.8%.
Cost Efficiency: GPT-5.4 nano pricing ($0.2/M input, $1.25/M output) is 3.7x cheaper than Kimi K2.7 Code ($0.95/M input, $4/M output).
Was this recommendation helpful?
Benchmarks & Scores
Coding (swe-bench-pro)Winner (+6.2%)
58.6%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+7.2%)
90%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$1.71Input: $0.95 | Output: $4.00Context Window
262.14k tokensBenchmarks & Scores
Coding (swe-bench-pro)
52.4%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
82.8%graduate-level science QA
Cost & Context
Cost (per 1M tokens)3.7x cheaper
$0.46Input: $0.20 | Output: $1.25Context WindowLarger
400k tokensFrequently Asked Questions about Kimi K2.7 Code vs GPT-5.4 nano
GPT-5.4 nano is cheaper than Kimi K2.7 Code. GPT-5.4 nano has a blended cost of $0.46/1M tokens, which is about 3.7x 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 GPT-5.4 nano which scores 52.4%.
Related Matchups
Explore similar comparisons for Kimi K2.7 Code and GPT-5.4 nano.
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.