GLM-5

Product Introduction

1. Definition: GLM-5 is a 744B parameter Mixture-of-Experts (MoE) large language model with 40B active parameters, engineered for complex systems engineering and long-horizon agentic tasks. It falls under the technical category of open-source foundation models optimized for enterprise-grade AI workflows.
2. Core Value Proposition: GLM-5 bridges the performance gap with frontier models like Claude Opus 4.5 while drastically reducing deployment costs, enabling scalable AI solutions for systems engineering, multi-step automation, and document generation.

Main Features

1. DeepSeek Sparse Attention (DSA): Implements sparse computation techniques to compress context windows, maintaining 128K–202K token capacity while cutting GPU memory requirements by 40% versus dense transformers. This enables cost-efficient long-context deployments.
2. Slime RL Infrastructure: An asynchronous reinforcement learning framework accelerating policy optimization by 5.8× through parallelized reward modeling. It enables granular post-training for specialized agent behaviors without throughput bottlenecks.
3. Agentic Document Engine: Directly converts prompts into formatted .docx/.xlsx/.pdf outputs using structured templates. Integrates visual design rules (color hierarchies, responsive tables) for publish-ready financial reports, sponsorship proposals, and technical specs.

Problems Solved

1. Pain Point: High computational costs and unstable outputs in long-horizon agent tasks (e.g., multi-quarter business simulations).
2. Target Audience:
   • Systems Engineers: Building automated pipelines for DevOps or infrastructure management.
   • Enterprise Developers: Creating agent swarms for document automation (PRDs, financial reports).
   • Data Scientists: Fine-tuning task-specific agents via RL.
3. Use Cases:
   • Running year-long business simulations (Vending Bench 2) with dynamic resource allocation.
   • Converting research data into compliance-ready SEC filings or equity reports.
   • Collaborative coding with tools like Claude Code/OpenClaw for SWE-bench verified tasks.

Unique Advantages

1. Differentiation: Outperforms all open-source rivals on Vending Bench 2 ($4,432.12 vs. DeepSeek-V3.2’s $1,034) and narrows Claude Opus 4.5’s lead to <12% while using 60% fewer active parameters.
2. Key Innovation: Hybrid MoE architecture balancing 744B total parameters with 40B active experts—optimizing inference costs without sacrificing reasoning depth. Validated by #1 open-source scores on Terminal-Bench 2.0 (61.1) and SWE-bench Multilingual (73.3).

Frequently Asked Questions (FAQ)

1. How does GLM-5 reduce AI deployment costs?
   DeepSeek Sparse Attention cuts GPU memory needs by 40% versus conventional transformers, while MoE architecture limits active parameters to 40B during inference—slashing cloud compute expenses.
2. Can GLM-5 generate formatted business documents?
   Yes, its Agent Mode outputs editable .docx/.xlsx files with embedded visuals, tables, and brand-compliant styling for proposals, financial reports, or run sheets end-to-end.
3. What hardware supports GLM-5 locally?
   Deployable on non-NVIDIA chips like Huawei Ascend/Cambricon via kernel-optimized quantization, plus standard vLLM/SGLang frameworks.
4. How does GLM-5 handle year-long simulations?
   Slime RL infrastructure trains agents via asynchronous reward modeling, enabling stable long-horizon planning in benchmarks like Vending Bench 2 (1-year retail ops).
5. Is GLM-5 free for commercial use?
   Weights are MIT-licensed on Hugging Face/ModelScope, while API access requires Z.ai’s GLM Coding Plan (usage-based quota).