GLM-5 — What is it?

GLM-5 is an open-source AI model designed for complex systems engineering and long-horizon agentic tasks, offering advanced coding and reasoning capabilities.

⭐ 37 Stars 🍴 0 Forks Apache-2.0 Author: zai-org
Source: README View on GitHub →

Why it matters

GLM-5 is gaining attention due to its significant improvements in coding and agentic tasks, addressing the limitations of previous models by enhancing long-horizon effectiveness and reducing deployment costs. Its integration of DeepSeek Sparse Attention and asynchronous RL infrastructure stands out as unique technical choices.

Source: Synthesis of README and project traits

Core Features

Advanced Coding Capabilities

GLM-5.1 demonstrates strong coding capabilities, achieving state-of-the-art performance on SWE-Bench Pro and leading on NL2Repo and Terminal-Bench 2.0.

Source: README
Long-Horizon Agentic Tasks

GLM-5 is built for long-horizon agentic tasks, sustaining optimization over hundreds of rounds and thousands of tool calls, with improved judgment and productivity over extended sessions.

Source: README
DeepSeek Sparse Attention

GLM-5 integrates DeepSeek Sparse Attention, reducing deployment costs while preserving long-context capacity.

Source: README
Asynchronous RL Infrastructure

The project utilizes an asynchronous RL infrastructure, [slime](https://github.com/THUDM/slime), to improve training throughput and efficiency.

Source: README

Architecture

The architecture is inferred to be modular, with a focus on scalable AI models. Key technical decisions include the integration of DeepSeek Sparse Attention and asynchronous RL for efficient training and deployment.

Source: Code tree + dependency files

Project Knowledge Graph

Knowledge graph: project (center) + core features (inner hexagons) + key dependencies (outer chips) transformers pre-commit accelerate Advanced Coding CapabilitiesAdvanced Coding Cap… Long-Horizon Agentic TasksLong-Horizon Agenti… DeepSeek Sparse AttentionDeepSeek Sparse Att… Asynchronous RL InfrastructureAsynchronous RL Inf… GLM-5 Project Core feature Key dependency

Center: project; inner ring: core feature modules; outer ring: key dependencies. Auto-generated from core_features and tech_stack.key_deps.

Tech Stack

LanguageNot enough information.Frameworktransformers, pre-commit, accelerate
transformerspre-commitaccelerate
Docker, potentially CUDA for GPU support
Source: Dependency files + code tree

Quick Start

Using Docker: vLLM: ```shell docker pull vllm/vllm-openai:glm51 docker pull vllm/vllm-openai:glm51-cu130 # For CUDA 13.0 ``` SGLang: ```bash SGLANG_ENABLE_SPEC_V2=1 sglang serve --model-path zai-org/GLM-5.1-FP8 --tp-size 8 --tool-call-parser glm47 --reasoning-parser glm45 --speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 --mem-fraction-static 0.85 --served-model-name glm-5.1-fp8 ```
Source: README Installation/Quick Start

Use Cases

GLM-5 is suitable for complex systems engineering, long-horizon agentic tasks, and scenarios requiring advanced coding and reasoning capabilities. It can be used in real-world terminal tasks, repo generation, and long-term operational capabilities like simulated vending machine businesses.

Source: README

Strengths & Limitations

Strengths

  • Strength 1: Advanced capabilities in coding and agentic tasks
  • Strength 2: Efficient training and deployment with DeepSeek Sparse Attention and asynchronous RL
  • Strength 3: Scalable for complex systems engineering

Limitations

  • Limitation 1: Limited information on primary programming language
  • Limitation 2: Potential high computational requirements for training and deployment
Source: Synthesis of README, code structure and dependencies

Latest Release

No release records available.

Source: GitHub Releases

Verdict

GLM-5 is a promising project for teams or individuals involved in complex systems engineering and long-horizon AI tasks. Its advanced capabilities and unique technical choices make it a valuable resource for those seeking to push the boundaries of AI applications.

Transparency Notice
This page is auto-generated by AI (a large language model) from the following public materials: GitHub README, code tree, dependency files and release notes. Analyzed at: 2026-05-23 17:53. Quality score: 85/100.

Data sources: README, GitHub API, dependency files