Moonshot AI (Kimi)kimi-k2.5VSZ.ai (Zhipu AI)GLM-5.2
Our Take
We recommend GLM-5.2 for practical local execution on standard developer hardware, or kimi-k2.5 if you need peak reasoning and have the VRAM (or a cloud API) to host it. While kimi-k2.5 offers frontier capability, its massive parameter size makes it extremely difficult to host locally. Choose GLM-5.2 for standard local setups, or kimi-k2.5 for peak reasoning.
▶WHY?
Benchmark Calculations & Evidence:
Model Size: kimi-k2.5 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)
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)1.8x cheaper
$1.20Input: $0.60 | Output: $3.00Context Window
262.14k tokensBenchmarks & Scores
Coding (swe-bench-pro)Winner (+11.4%)
62.1%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+3.6%)
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-k2.5 vs GLM-5.2
kimi-k2.5 is cheaper than GLM-5.2. kimi-k2.5 has a blended cost of $1.20/1M tokens, which is about 1.8x 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-k2.5 which scores 50.7%.
Related Matchups
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