GLM-5-Turbo Matches Gemini Flash Speed

🔑 Enhanced Key Takeaways

• •GLM-5-Turbo is a specialized high-speed variant of the GLM-5 family, optimized specifically for fast inference in agent-driven environments like OpenClaw.[5][6]
• •GLM-5, the base model, uses a Mixture-of-Experts (MoE) architecture with 744B total parameters (40B active), trained on 28.5T tokens, and integrates DeepSeek Sparse Attention (DSA) for efficiency.[1][2]
• •GLM-5 achieves top open-model scores on agentic benchmarks like SWE-bench Verified (77.8), Terminal Bench 2.0 (56.2), and leads in BrowseComp, MCP-Atlas, and τ²-Bench.[4]
• •GLM-5 tops the Artificial Analysis Agentic Index at 63 among open weights models, with GDPval-AA ELO of 1412, excelling in knowledge work tasks.[3]

🛠️ Technical Deep Dive

• •Architecture: Transformer-based Mixture-of-Experts (MoE) with 744B total parameters, 40B active per token, 80 layers, Multi-Head Attention, RMS Normalization, and Absolute Position Embedding.[1][2]
• •Attention Mechanism: DeepSeek Sparse Attention (DSA) dynamically allocates resources to reduce memory/compute for long sequences.[1][2]
• •Training: Pre-trained on 28.5T tokens emphasizing code and reasoning data; post-training uses 'slime' asynchronous RL framework for efficient multi-step interactions.[1][2][4]
• •Capabilities: 204,800-token context window, up to 128,000-token generation; supports tool-use, real-time streaming, structured output; text-only (no multimodal input).[1][3]
• •Deployment: Open weights under MIT License, BF16 precision requiring ~1,490GB VRAM; available via NVIDIA NIM and OpenRouter.[1][2]

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline