GLM-5.3
A solid pick for self-hosting and full data control.
Overview
GLM-5.3 shares its base with GLM-5.2 but redirects post-training toward complex coding and long-horizon agentic work, gaining 50% on Z.ai's internal Code Bench and posting an open-weights SOTA on Terminal Bench 3.0. Its emergent cyber capability is strong for vulnerability discovery, though that same strength invites caution over dual use. Limit: on several agentic benchmarks it still trails GPT-5.6 Sol. Open weights under a commercial licence allow on-premise EU deployment with data staying local, but the 753-billion-parameter size demands serious server-grade GPU capacity.
Skill profile
Not disclosed
Strengths
- Very long context
- Open-source and self-hostable
Limitations
- No native GDPR guarantee
- API pricing not disclosed
Who is it for
- you build with a coding agent
- you want to control cost or self-host
- you handle sensitive EU data
Ideal use cases
- Complex coding agents
- DevSecOps automation
- Vulnerability discovery
- Long-horizon multi-step tasks
- Sovereign on-prem deployment
Access & availability
Key specifications
Privacy
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 429.2 GB | 474 GB | Server GPU infra (H100/A100…) |
| Q8 · balanced | 805.7 GB | 888 GB | Server GPU infra (H100/A100…) |
| FP16 · max quality | 1506 GB | 1659 GB | Server GPU infra (H100/A100…) |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve zai-org/GLM-5.3 \
--max-model-len 32768 \
--tensor-parallel-size 2 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |