GLM-5.1

Overview

Z.AI's flagship MoE with 744B total and 40B active parameters under an MIT license. Ranked #1 open-weight model on Artificial Analysis as of April 2026.

When to pick this model

• Production agentic systems on dedicated server clusters
• Replacing closed frontier APIs with self-hosted weights
• Long-context document analysis up to 200K tokens
• Open-weight SWE-Bench-grade coding agents
• Commercial deployments that need MIT licensing

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	445 GB
Q5_K_M	535 GB
Q8_0	800 GB
FP16 (no quantization)	1488 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Benchmark	Score
SWE-Bench Pro	58.4

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• #1 open-weight model on Artificial Analysis (April 2026)
• 58.4 on SWE-Bench Pro, leading all open weights
• 200K context for whole-repo reasoning
• True MIT license with full commercial rights

Limitations

• 445GB+ in Q4 quantization requires a multi-GPU server
• No official Ollama tag at launch
• Operational complexity rules out single-workstation use

Architecture & training

Architecture: MoE · 744B/40B active · 200k ctx · Reasoning variant

Training: Successor to GLM-5 (February 2026).

Quick start

# HuggingFace (GGUF) : unsloth/GLM-5.1-GGUF

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.