GLM-5: The New Open-Source Giant - Validated Benchmarks & User Guide

Published on February 15, 2026

GLM-5: The 700B+ Parameter Behemoth

Release Date: February 11, 2026
Developer: Zhipu AI
Param Count: 744B (40B Active via MoE)
Context Window: 200k Tokens

The AI landscape has just shifted again. Zhipu AI has released GLM-5, a massive 744 billion parameter Mixture-of-Experts (MoE) model that challenges the dominance of closed-source giants like GPT-4.5 and Claude 3.7. Unlike many competitors that hide behind APIs, GLM-5 brings open weights to the table, though running it requires serious hardware.

This guide provides a validated technical breakdown, accurate benchmark comparisons, and practical instructions on how to use it—whether you have a cluster of H100s or just an API key.

📊 Validated Benchmarks: GLM-5 vs. The World

We’ve compiled validated data comparing GLM-5 against current state-of-the-art (SOTA) models. Note that GLM-5 shines particularly brightly in graduate-level reasoning (GPQA).

> Llama 4 benchmarks are based on the closest available 70B proxies (e.g., Llama 3.3) where official Llama 4 numbers are pending broad validation.

Key Takeaways

1. Graduate-Level Reasoning: GLM-5 achieves a startling 86.0% on GPQA-Diamond, theoretically outperforming both GPT-4.5 and Claude 3.7 in this specific scientific reasoning capability.
2. Coding Proficiency: With a 90.0% HumanEval score, it is a top-tier coding assistant, suitable for complex refactoring tasks.
3. Active Parameters: Despite its massive 744B size, it uses a sparse MoE architecture to keep active parameters at ~40B per token, making inference surprisingly efficient if you can load the weights.

🛠️ Technical Innovations

GLM-5 isn’t just big; it’s smart. It introduces several architectural shifts:

• DeepSeek Sparse Attention (DSA): Drastically reduces computational overhead for long-context tasks (up to 200k tokens) without losing accuracy.
• “Slime” RL Framework: A novel reinforcement learning framework designed to handle post-training for massive-scale models, improving alignment and complex instruction following.
• Hardware Independence: Uniquely, the entire model was trained on Huawei Ascend chips using the MindSpore framework, demonstrating high-performance AI independence from NVIDIA’s ecosystem.

🚀 How to Use GLM-5

1. The Easy Way: Cloud APIs

For most users, running a 744B model locally is impossible. Use these providers:

• Zhipu AI Open Platform: The official API.
  • Cost: ~$1.00 / 1M input tokens, ~$3.20 / 1M output tokens.
• OpenRouter: Often the easiest way to access it without a specific account.
• Google Vertex AI: GLM-5 is available in the Model Garden as of Feb 2026.

2. The Hard Way: Local Deployment

Warning: You cannot run the full model on a consumer GPU (like an RTX 4090).

Hardware Requirements:

• Full Model (FP16/BF16): ~1.5 TB VRAM (Requires H100/H200 cluster).
• 4-bit Quantization: ~450 GB VRAM.
• 2-bit Quantization: ~300 GB VRAM/RAM.
  • Minimum viable setup: Mac Studio (M3 Ultra with 192GB RAM) might run a heavily quantized version slowly, or a dual-server setup with massive CPU RAM offloading.

Running with llama.cpp: If you have the hardware (e.g., a server with 512GB DDR5 RAM and decent CPUs), you can try CPU inference:

# 1. Install llama.cpp with CUDA support (if you have GPUs)
make LLAMA_CUDA=1

# 2. Download the GGUF (approx 300GB for Q2)
./llama-cli -m glm-5-744b-q2_k.gguf -p "Explain quantum entanglement" -n 512

3. Where to Test for Free

• Z.ai: Official chat interface (Chat & Agent modes).
• GLM-5 App: Direct web chat.
• Visual Studio Code: via the Kilo Code extension (currently offering free trials).

🎯 Verdict

GLM-5 is a monumental release for the open-source community. It proves that open weights can tackle the hardest reasoning benchmarks (GPQA) head-on against the best closed models. While its sheer size makes local deployment a niche pursuit for now, its availability via API provides a powerful, cost-effective alternative to GPT-4.5 for complex reasoning and agentic workflows.

Updated: February 15, 2026