GLM-5.2
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
50.7
AA Index
Coding
67.0
AA Index
Agentic
43.1
AA Index
Intelligence Index - GLM-5.2 vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Agentic Index comparison
Benchmark data from Artificial Analysis · updated 2026-06-18.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| GPQA | 89.5 |
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve zai-org/GLM-5.2-FP8New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
GLM-5.2 is a 753.4B-parameter mit model from Z AI. It scores 50.7 on the Artificial Analysis Intelligence Index (coding 67). At Q4_K_M it needs roughly 437 GB of VRAM, placing it in the 48 GB+ / multi-GPU hardware tier.
Benchmarks
Artificial Analysis Intelligence Index - GLM-5.2 vs. leading closed models:
| Model | Intelligence | Coding | GPQA |
|---|---|---|---|
| GLM-5.2 | 50.7 | 67 | 89.5 |
| Claude Fable 5 (with fallback) | 59.9 | 76.5 | 92.6 |
| Claude Opus 4.8 (max) | 55.7 | 56.7 | 92 |
| GPT-5.5 (xhigh) | 54.8 | 74.9 | 93.5 |
| Claude Opus 4.7 (max) | 53.5 | 52.5 | 91.4 |
| Gemini 3.5 Flash | 50.2 | 45 | 92.2 |
Source: Artificial Analysis (2026-06-18).
Popularity
GLM-5.2 has 24,967 downloads in the last month on HuggingFace and 83 likes.
Frequently asked
Quick answers to common questions
How much VRAM does GLM-5.2 need?
GLM-5.2 with 753.4B parameters needs approximately 437 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is GLM-5.2 better than other zai-org models?
GLM-5.2 has 753.4B parameters with 1,048,576 context - a strong choice for general use.
What license is GLM-5.2 under?
GLM-5.2 is released under the mit license, making it suitable for most commercial and personal projects.
What hardware runs GLM-5.2 well?
With 753.4B parameters, GLM-5.2 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for GLM-5.2?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~535 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can GLM-5.2's context window handle?
GLM-5.2 supports a 1,048,576-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with GLM-5.2?
GLM-5.2 competes with other 377B–1130B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec
Comments coming soon
Configure NEXT_PUBLIC_GISCUS_REPO_ID and NEXT_PUBLIC_GISCUS_CATEGORY_ID at giscus.app to enable.