Moonshot AI (Kimi)kimi-k2.5VSZ.ai (Zhipu AI)GLM-5.1
Our Take
We recommend GLM-5.1 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-5.1 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-5.1 is a 754B parameter model.
Was this recommendation helpful?
Benchmarks & Scores
Coding (swe-bench-pro)
50.7%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+1.4%)
87.6%graduate-level science QA
Cost & Context
Cost (per 1M tokens)1.8x cheaper
$1.20Input: $0.60 | Output: $3.00Context WindowLarger
262.14k tokensBenchmarks & Scores
Coding (swe-bench-pro)Winner (+7.7%)
58.4%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
86.2%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$2.15Input: $1.40 | Output: $4.40Context Window
202.75k tokensFrequently Asked Questions about kimi-k2.5 vs GLM-5.1
kimi-k2.5 is cheaper than GLM-5.1. kimi-k2.5 has a blended cost of $1.20/1M tokens, which is about 1.8x cheaper than GLM-5.1 at $2.15/1M tokens.
GLM-5.1 is better for coding tasks on this benchmark. It scores 58.4% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to kimi-k2.5 which scores 50.7%.
Related Matchups
Explore similar comparisons for kimi-k2.5 and GLM-5.1.
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.