DeepSeekDeepSeek V4 ProVSZ.ai (Zhipu AI)GLM-5.2
Our Take
We recommend GLM-5.2 for practical local execution on standard developer hardware, or DeepSeek V4 Pro if you need peak reasoning and have the VRAM (or a cloud API) to host it. While DeepSeek V4 Pro offers frontier capability, its massive parameter size makes it extremely difficult to host locally. Choose GLM-5.2 for standard local setups, or DeepSeek V4 Pro for peak reasoning.
▶WHY?
Benchmark Calculations & Evidence:
Model Size: DeepSeek V4 Pro is a massive 1600B 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)
52.1%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
88%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$2.17Input: $1.74 | Output: $3.48Context Window
1.05M tokensBenchmarks & Scores
Coding (swe-bench-pro)Winner (+10.0%)
62.1%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+3.2%)
91.2%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$2.15Input: $1.40 | Output: $4.40Context Window
1.05M tokensFrequently Asked Questions about DeepSeek V4 Pro vs GLM-5.2
GLM-5.2 is cheaper than DeepSeek V4 Pro. GLM-5.2 has a blended cost of $2.15/1M tokens, which is about 1.0x cheaper than DeepSeek V4 Pro at $2.17/1M 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 DeepSeek V4 Pro which scores 52.1%.
Related Matchups
Explore similar comparisons for DeepSeek V4 Pro and GLM-5.2.
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