Moonshot AI (Kimi)kimi-k2.5VSZ.ai (Zhipu AI)GLM-4.6
Our Take
We recommend GLM-4.6 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-4.6 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-4.6 is a 357B 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)Winner (+4.7%)
87.6%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$1.20Input: $0.60 | Output: $3.00Context WindowLarger
262.14k tokensBenchmarks & Scores
Coding (swe-bench-verified)
68%multi-file code and clearly defined tasks
Reasoning (gpqa-diamond)
82.9%graduate-level science QA
Cost & Context
Cost (per 1M tokens)1.2x cheaper
$1.00Input: $0.60 | Output: $2.20Context Window
202.75k tokensFrequently Asked Questions about kimi-k2.5 vs GLM-4.6
GLM-4.6 is cheaper than kimi-k2.5. GLM-4.6 has a blended cost of $1.00/1M tokens, which is about 1.2x cheaper than kimi-k2.5 at $1.20/1M tokens.
For coding tasks, kimi-k2.5 scores 50.7% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while GLM-4.6 scores 68% on swe-bench-verified (multi-file code and clearly defined tasks).
Related Matchups
Explore similar comparisons for kimi-k2.5 and GLM-4.6.
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