GLM-5 and DeepSeek V3.2 are the two open-weight models most likely to top your self-hosted shortlist. GLM-5, a 744B MoE that also integrates DeepSeek Sparse Attention, is purpose-built for agentic engineering and long-horizon coding, and it led the open-weights intelligence index at launch — a favourite inside coding harnesses.
DeepSeek V3.2 is the efficiency and value leader: a 671B MoE with sparse attention, strong reasoning-in-tool-use and some of the lowest prices in the industry. GLM-5 tends to edge agentic coding and long-horizon tasks; DeepSeek V3.2 wins on raw cost-efficiency and broad reasoning value. Both are excellent foundations for open deployments, so weigh GLM-5's coding focus against DeepSeek's price and efficiency.
Which should you choose?
The verdict
GLM-5 leads on agentic engineering and long-horizon coding; DeepSeek V3.2 leads on cost-efficiency and general value. Pick by whether your priority is coding depth or price.
Watch the comparison
GLM-5 vs DeepSeek V3.2 — video reviews on YouTubeWatch hands-on tests and side-by-side demosFrequently asked questions
Which is better for coding agents?
GLM-5 is tuned specifically for agentic engineering and shines in coding harnesses; DeepSeek V3.2 is a strong, cheaper all-rounder.
Do both use sparse attention?
Yes — DeepSeek pioneered DeepSeek Sparse Attention, and GLM-5 integrates it too for efficient long-context inference.