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This comparative analysis evaluates three leading open-weight Mixture-of-Experts models—Kimi K3, DeepSeek V4 Pro, and GLM-5.2—focusing on their architectural efficiency, benchmark performance via the Artificial Analysis Intelligence Index, and practical serving costs for long-context agent workloads. The findings highlight how sparse activation strategies allow these trillion-scale architectures to compete with proprietary closed models while offering distinct trade-offs between total parameter count, active compute requirements, and licensing terms.
Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost
Three open MoE flagships face off on measured intelligence, MIT versus Modified MIT weights, and real serving cost The post Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost appeared first on MarkTechPost.
Three Chinese labs now hold the top of the open-weight leaderboard. Moonshot AI’s Kimi K3, DeepSeek V4 Pro, and Zhipu AI’s GLM-5.2 are all sparse Mixture-of-Experts (MoE) models with million-token context windows. Each targets long-horizon coding and agent workloads. This article compares them on three axes an AI team actually decides on: measured capability, license terms, and serving cost. ‘Trillion-parameter’ fits Kimi K3 (2.8T) and DeepSeek V4 Pro (1.6T). GLM-5.2 is 744B total, so it is the smallest of the three by total parameters. It earns its place because it led…
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