Moonshot AI (Kimi)Kimi K2.7 CodeVSZ.ai (Zhipu AI)GLM-5.2
Our Take
We recommend GLM-5.2 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-5.2 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-5.2 is a 753B 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)
90%graduate-level science QA
Cost & Context
Cost (per 1M tokens)1.3x cheaper
$1.71Input: $0.95 | Output: $4.00Context Window
262.14k tokensBenchmarks & Scores
Coding (swe-bench-pro)Winner (+3.5%)
62.1%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+1.2%)
91.2%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$2.15Input: $1.40 | Output: $4.40Context WindowLarger
1.05M tokensFrequently Asked Questions about Kimi K2.7 Code vs GLM-5.2
Kimi K2.7 Code is cheaper than GLM-5.2. Kimi K2.7 Code has a blended cost of $1.71/1M tokens, which is about 1.3x cheaper than GLM-5.2 at $2.15/1M tokens.
GLM-5.2 is better for coding tasks on this benchmark. It scores 62.1% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to Kimi K2.7 Code which scores 58.6%.
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