FrontierThe story, in brief
Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost
Three trillion-scale open MoE models just went head-to-head. Here's who won on benchmarks, licensing, and serving cost.
The KeyNews take
Why it matters
Open-source MoE models are reaching parity with closed alternatives on reasoning and code tasks. For builders choosing between Kimi K3, DeepSeek V4 Pro, and GLM-5.2, this benchmark comparison directly impacts which stack to adopt—especially on serving cost and license flexibility.
The key facts
5 to knowThree trillion-scale MoE models compared: Kimi K3, DeepSeek V4 Pro, GLM-5.2
Benchmarks measured across intelligence, reasoning, and task performance
License comparison: MIT vs Modified MIT weights
Serving cost analysis provided for each model
Open-source flagships in direct capability competition
Go to the source
MarkTechPostmarktechpost.com
Publisher excerpt: Three open MoE flagships face off on measured intelligence, MIT versus Modified MIT weights, and real serving cost