GLM-4.5 AI Model

A GLM-4.5 AI Model is an MIT-licensed mixture-of-experts large language model that implements glm-4.5 sparse activation with 355B total parameters (32B active) to achieve glm-4.5 sota performance on glm-4.5 reasoning benchmarks by Z.ai.

• AKA: GLM-4.5, GLM 4.5 Model, General Language Model 4.5.
• Context:
  • It can typically achieve GLM-4.5 AIME Performance of 91.0 on glm-4.5 mathematical reasoning.
  • It can typically demonstrate GLM-4.5 SWE-Bench Leadership with 64.2% on glm-4.5 coding tasks.
  • It can typically implement GLM-4.5 Loss-Free Routing with glm-4.5 sigmoid gates.
  • It can typically support GLM-4.5 Agentic Capability for glm-4.5 tool use and glm-4.5 planning.
  • It can typically enable GLM-4.5 Multi-Stage Training through glm-4.5 sft and glm-4.5 rl alignment.
  • ...
  • It can often excel at GLM-4.5 Complex Reasoning scoring 98.2 on glm-4.5 math 500.
  • It can often integrate GLM-4.5 Hybrid Architecture combining glm-4.5 moe design with glm-4.5 transformer backbone.
  • It can often leverage GLM-4.5 Self-Distillation for glm-4.5 domain unification.
  • It can often deploy GLM-4.5 Coding Agent creating glm-4.5 full-stack applications.
  • ...
  • It can range from being a Base GLM-4.5 AI Model to being a Specialized GLM-4.5 AI Model, depending on its glm-4.5 fine-tuning domain.
  • It can range from being a Small Batch GLM-4.5 AI Model to being a Large Batch GLM-4.5 AI Model, depending on its glm-4.5 inference configuration.
  • It can range from being a Speed-Optimized GLM-4.5 AI Model to being a Quality-Optimized GLM-4.5 AI Model, depending on its glm-4.5 deployment priority.
  • It can range from being a Single-Task GLM-4.5 AI Model to being a Multi-Task GLM-4.5 AI Model, depending on its glm-4.5 capability scope.
  • It can range from being an Academic GLM-4.5 AI Model to being a Commercial GLM-4.5 AI Model, depending on its glm-4.5 usage context.
  • ...
  • It can integrate with GLM-4.5 MoE Router for glm-4.5 expert selection.
  • It can connect to GLM-4.5 Training Pipeline for glm-4.5 capability enhancement.
  • It can interface with GLM-4.5 Agent Framework for glm-4.5 application building.
  • It can communicate with GLM-4.5 Evaluation System for glm-4.5 benchmark testing.
  • It can synchronize with GLM-4.5 Optimization Engine for glm-4.5 performance tuning.
  • ...
• Example(s):
  • GLM-4.5 Training Stages, such as:
    • GLM-4.5 Cold-Start SFT Stage implementing glm-4.5 initial training.
    • GLM-4.5 Large-Scale SFT Stage performing glm-4.5 self-distillation.
    • GLM-4.5 RL Alignment Stage enhancing glm-4.5 reasoning capability.
  • GLM-4.5 Performance Achievements, such as:
    • GLM-4.5 GPQA Score demonstrating glm-4.5 graduate-level reasoning.
    • GLM-4.5 Terminal-Bench Score showing glm-4.5 system interaction skill.
    • GLM-4.5 LiveCodeBench Score indicating glm-4.5 real-time coding ability.
  • GLM-4.5 Agent Applications, such as:
    • GLM-4.5 Web Development Agent creating glm-4.5 interactive websites.
    • GLM-4.5 Physics Simulation Agent building glm-4.5 scientific demos.
    • GLM-4.5 Presentation Agent generating glm-4.5 slide decks.
  • ...
• Counter-Example(s):
  • GPT-4 Model, which uses dense architecture rather than glm-4.5 moe design.
  • Claude 4 Opus Model, which is proprietary unlike glm-4.5 mit license.
  • Mixtral Model, which has different moe implementation from glm-4.5 loss-free routing.
  • LLaMA Model, which lacks glm-4.5 agentic specialization.
• See: Z.ai, GLM Model Series, Mixture-of-Experts Architecture, MIT-Licensed AI Model, SWE-Bench Verified Benchmark, Agentic AI System, Supervised Fine-Tuning, Reinforcement Learning Alignment, Loss-Free MoE Routing.

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