GLM-4.5

Product Introduction

1. GLM-4.5 is a 355 billion parameter open-weight Mixture-of-Experts (MoE) model with 32 billion active parameters, designed to unify advanced reasoning, coding, and agentic capabilities in a single architecture.
2. The core value of GLM-4.5 lies in its ability to handle complex agentic applications through dual-mode inference (thinking and non-thinking modes), enabling both rapid responses and deep analytical problem-solving across diverse domains.

Main Features

1. GLM-4.5 delivers state-of-the-art performance on 12 benchmarks spanning agentic tasks (TAU-bench, BFCL v3, BrowseComp), reasoning (MMLU Pro, AIME24, MATH 500), and coding (SWE-bench Verified, Terminal-Bench), outperforming competitors like Claude 4 Opus and GPT-4.1 in web browsing accuracy (26.4% vs. 18.8%) and coding tool success rates (90.6% vs. 89.5%).
2. The model supports hybrid inference modes: a thinking mode for multi-step tool usage and complex reasoning, and a non-thinking mode for low-latency responses, optimized via a 128K context window and native function calling for agentic workflows.
3. GLM-4.5 integrates seamlessly with coding frameworks like Claude Code and Roo Code, enabling full-stack development capabilities for web applications, database management, and automated testing through multi-turn human-AI collaboration.

Problems Solved

1. GLM-4.5 addresses the fragmentation of AI capabilities by unifying reasoning, coding, and agentic skills in one model, eliminating the need to switch between specialized models for tasks like mathematical proofs, software development, and tool-based workflows.
2. The model targets developers and enterprises requiring high-performance AI agents for applications such as automated customer service (TAU-bench Retail: 79.7% accuracy), codebase maintenance (SWE-bench Verified: 164.2 score), and dynamic content generation (e.g., SVG animations, physics simulations).
3. Typical use cases include building interactive web apps (e.g., Pokémon Pokédex Live), generating technical presentations from documents (PDF-to-PPT conversion), and solving complex STEM problems (98.2% accuracy on MATH 500 benchmark).

Unique Advantages

1. Unlike DeepSeek-V3 or Kimi K2, GLM-4.5 uses a deeper MoE architecture with 96 attention heads and partial RoPE in Grouped-Query Attention, optimizing reasoning performance while maintaining inference efficiency through loss-free balance routing and MTP layers for speculative decoding.
2. The model incorporates slime, a proprietary RL infrastructure enabling asynchronous, mixed-precision training for agentic tasks, achieving 53.9% win rates against Kimi K2 in coding evaluations and 90.6% tool-calling success rates through execution-based feedback.
3. Competitive advantages include Pareto Frontier-leading efficiency in performance-scale trade-offs, verified by superior BrowseComp accuracy (26.4%) and terminal command execution (Terminal-Bench: 37.5) compared to models of similar parameter counts like Gemini 2.5 Pro (14.7 BrowseComp).

Frequently Asked Questions (FAQ)

1. How does GLM-4.5 compare to GLM-4.5-Air? GLM-4.5 (355B total/32B active parameters) prioritizes maximum performance for enterprise agentic tasks, while GLM-4.5-Air (106B total/12B active) offers cost-efficient inference with 60.4% TAU-bench Airline accuracy versus 79.7% in the flagship model.
2. Can GLM-4.5 integrate with existing coding tools? Yes, the model natively supports Claude Code, Roo Code, and CodeGeex via standardized function calling, demonstrated in 52 coding tasks with 80.8% success rates against Qwen3-Coder and 53.9% win rates against Kimi K2.
3. How to deploy GLM-4.5 locally? Open-weight variants are available on HuggingFace and ModelScope, compatible with vLLM/SGLang frameworks, requiring 4xA100-80GB GPUs for base model inference and 8x for full MoE activation.
4. What makes GLM-4.5 superior in agentic tasks? The model combines 128K context handling, slime-optimized RL training for long-horizon rollouts, and hybrid inference modes, achieving 77.8% BFCL v3 accuracy versus Claude 4 Sonnet’s 74.4%.
5. Does GLM-4.5 support non-English applications? While optimized for English STEM/coding tasks, the model retains multilingual capabilities from its 15T-token pretraining corpus, though benchmarks focus on English performance metrics.