GLM-4.5

GLM-4.5 represents a breakthrough in the field of large language models. It integrates advanced agent capabilities, sophisticated reasoning, and artifact-enabled programming into a unified architecture. With 355 billion total parameters and 32 billion active parameters, this model is built using an innovative Mixture-of-Experts (MoE) architecture that dramatically improves computational efficiency during both training and inference. Unlike DeepSeek-V3 and Kimi K2, GLM-4.5 adopts a "depth over width" approach—reducing model width (hidden dimension and number of experts) while increasing height (number of layers), resulting in superior performance. The model features Grouped-Query Attention with partial RoPE, employs 96 attention heads for a hidden dimension of 5120, and uses the Muon optimizer to accelerate convergence and support large batch training. Notably, it incorporates QK-Norm to stabilize the range of attention logits and an MTP (Multi-Token Prediction) layer to enable speculative decoding during inference. These technical innovations allow GLM-4.5 to achieve exceptional performance on reasoning benchmarks such as MMLU and BBH, where the increased number of attention heads significantly enhances results. GLM-4.5’s hybrid reasoning system offers two operational modes: "thinking mode" for complex reasoning and tool usage, and "non-thinking mode" for instant responses. This design elegantly addresses the fundamental trade-off between response speed and reasoning quality by automatically selecting the optimal mode based on query complexity.

Impressive benchmark results confirm GLM-4.5’s status as a world-class model. In the global ranking across 12 comprehensive benchmarks, it secured 3rd place with a score of 63.2, trailing only Grok-4 and GPT-o3.

GLM-4.5 stands out from competitors in presentation creation thanks to its built-in PPT/Poster agent. Rather than relying on templates, the agent autonomously searches the web, retrieves relevant images, and generates content directly in HTML. Users can request simple or complex designs, or upload reference documents, after which the agent independently creates polished slides.

GLM-4.5’s full-stack development capabilities are remarkable in both depth and practicality. The model can build complete web applications, including frontend interfaces, database management, and backend deployment. A dedicated agent developed by the team enables users to create entire websites and complex autonomous artifacts—ranging from interactive mini-games to physics simulations—in formats such as HTML, SVG, and Python. Users need only provide a brief prompt to define the task, then easily add or refine functionality through natural dialogue.

Announce Date: 28.07.2025
Parameters: 385B
Experts: 160
Activated: 32B
Context: 131K
Attention Type: Full Attention
VRAM requirements: 225.3 GB using 4 bits quantization
Developer: Z.ai
Transformers Version: 4.54.0
License: Apache 2.0

Use our pre-built public endpoints to test inference and explore GLM-4.5 capabilities.

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

• maximize endpoint performance,
• enable full context for long sequences,
• ensure top-tier security for data processing in an isolated, dedicated environment,
• use custom weights, such as fine-tuned models or LoRA adapters.

Contact our dedicated neural networks support team at nn@immers.cloud or send your request to the sales department at sale@immers.cloud.