Moonshot AI (Kimi)Kimi K2.7 CodeVSZ.ai (Zhipu AI)GLM-4.5
Our Take
We recommend GLM-4.5 for practical local execution on standard developer hardware, or Kimi K2.7 Code if you need peak reasoning and have the VRAM (or a cloud API) to host it. While Kimi K2.7 Code offers frontier capability, its massive parameter size makes it extremely difficult to host locally. Choose GLM-4.5 for standard local setups, or Kimi K2.7 Code for peak reasoning.
▶WHY?
Benchmark Calculations & Evidence:
Model Size: Kimi K2.7 Code is a massive 1000B parameter model requiring heavy GPU infrastructure, while GLM-4.5 is a 358B parameter model.
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Benchmarks & Scores
Coding (swe-bench-pro)
58.6%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+10.1%)
90%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$1.71Input: $0.95 | Output: $4.00Context WindowLarger
262.14k tokensBenchmarks & Scores
Coding (swe-bench-verified)
64.2%multi-file code and clearly defined tasks
Reasoning (gpqa-diamond)
79.9%graduate-level science QA
Cost & Context
Cost (per 1M tokens)1.7x cheaper
$1.00Input: $0.60 | Output: $2.20Context Window
131.07k tokensFrequently Asked Questions about Kimi K2.7 Code vs GLM-4.5
GLM-4.5 is cheaper than Kimi K2.7 Code. GLM-4.5 has a blended cost of $1.00/1M tokens, which is about 1.7x cheaper than Kimi K2.7 Code at $1.71/1M tokens.
For coding tasks, Kimi K2.7 Code scores 58.6% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while GLM-4.5 scores 64.2% on swe-bench-verified (multi-file code and clearly defined tasks).
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