Z.ai (Zhipu AI)GLM-4.7VSZ.ai (Zhipu AI)GLM-5.1
Our Take
We recommend GLM-4.7 for practical local execution on standard developer hardware, or GLM-5.1 if you need peak reasoning and have the VRAM (or a cloud API) to host it. While GLM-5.1 offers frontier capability, its massive parameter size makes it extremely difficult to host locally. Choose GLM-4.7 for standard local setups, or GLM-5.1 for peak reasoning.
▶WHY?
Benchmark Calculations & Evidence:
Model Size: GLM-5.1 is a massive 754B parameter model requiring heavy GPU infrastructure, while GLM-4.7 is a 358B parameter model.
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Benchmarks & Scores
Coding (swe-bench-verified)
73.8%multi-file code and clearly defined tasks
Reasoning (gpqa-diamond)
85.7%graduate-level science QA
Cost & Context
Cost (per 1M tokens)2.1x cheaper
$1.00Input: $0.60 | Output: $2.20Context Window
202.75k tokensBenchmarks & Scores
Coding (swe-bench-pro)
58.4%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+0.5%)
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 GLM-4.7 vs GLM-5.1
GLM-4.7 is cheaper than GLM-5.1. GLM-4.7 has a blended cost of $1.00/1M tokens, which is about 2.1x cheaper than GLM-5.1 at $2.15/1M tokens.
For coding tasks, GLM-4.7 scores 73.8% on swe-bench-verified (multi-file code and clearly defined tasks), while GLM-5.1 scores 58.4% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning).
Related Matchups
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