Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost
Kimi K3, DeepSeek V4 Pro, and GLM-5.2 dominate the open-weight leaderboard as trillion-scale Sparse Mixture-of-Experts (MoE) models with 1M-token context windows. Kimi K3 leads in raw capability and benchmarks but is currently API-only with high costs and a restrictive Modified MIT license pending weight release. DeepSeek V4 Pro offers the best cost-efficiency and clean MIT licensing, making it ideal for budget-conscious deployments requiring strong coding performance. GLM-5.2 provides a balance
Analysis
TL;DR
- Kimi K3, DeepSeek V4 Pro, and GLM-5.2 dominate the open-weight leaderboard as trillion-scale Sparse Mixture-of-Experts (MoE) models with 1M-token context windows.
- Kimi K3 leads in raw capability and benchmarks but is currently API-only with high costs and a restrictive Modified MIT license pending weight release.
- DeepSeek V4 Pro offers the best cost-efficiency and clean MIT licensing, making it ideal for budget-conscious deployments requiring strong coding performance.
- GLM-5.2 provides a balanced middle ground with faster inference speeds, self-hosting feasibility, and competitive performance at moderate costs.
Why It Matters
This comparison highlights a critical divergence in the open-weight market between peak performance (Kimi K3) and practical deployability (DeepSeek/GLM). For AI practitioners, the choice is no longer just about benchmark scores but involves complex trade-offs between licensing restrictions, serving infrastructure requirements, and total cost of ownership. The emergence of these models signals a shift toward specialized MoE architectures optimized for long-horizon agentic workflows and coding tasks.
Technical Details
- Architecture: All three utilize Sparse Mixture-of-Experts (MoE). Kimi K3 is a 2.8T parameter model activating 16/896 experts; DeepSeek V4 Pro is 1.6T with 49B active parameters; GLM-5.2 is 744B with ~40B active parameters.
- Context & Modality: Each supports a 1M-token context window. Kimi K3 is multimodal (text, vision, video), while DeepSeek V4 Pro and GLM-5.2 are text-only.
- Performance: Kimi K3 leads on the Artificial Analysis Intelligence Index (score ~57) and coding benchmarks like DeepSWE. DeepSeek V4 Pro excels in cost-per-task efficiency ($0.04) and SWE-bench Verified performance.
- Deployment Constraints: Self-hosting GLM-5.2 requires ~8x H200 GPUs. DeepSeek V4 Pro requires more VRAM, while Kimi K3 demands 64+ accelerators, effectively limiting local deployment for most organizations.
Industry Insight
- Licensing Strategy Impact: The delayed weight release and modified license for Kimi K3 demonstrate how top-tier capabilities can be leveraged to drive API usage, forcing enterprises to weigh immediate availability against potential future open-source benefits.
- Cost-Performance Trade-offs: Organizations prioritizing rapid iteration and low-cost scaling should favor DeepSeek V4 Pro or GLM-5.2, as the premium paid for Kimi K3's marginal capability gains may not justify the infrastructure and API costs for many use cases.
- Hardware Accessibility: The significant gap in self-hosting requirements (from 8 GPUs for GLM-5.2 to 64+ for K3) reinforces the trend where open-weight models are increasingly accessible only to well-resourced entities, potentially widening the gap between large tech firms and smaller developers.
Disclaimer: The above content is generated by AI and is for reference only.