GLM-5.3
Q4 GGUF download: unsloth/GLM-5.3-Flash-GGUF - 0.01 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
44.8
AA Index
Coding
74.8
AA Index
Agentic
53.1
AA Index
Intelligence Index - GLM-5.3 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 2026-10-15.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $1.40 |
| Output (per 1M tokens) | $4.40 |
| Median speed (hosted API) | 58 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-15. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| GPQA Diamond | 91.7 |
| HLE | 42.3 |
| SciCode | 59 |
Source: Artificial Analysis component evals - updated 2026-10-15.
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-5.3-FlashNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
GLM-5.3 is a 321.3B-parameter mit model from Z AI. It scores 44.8 on the Artificial Analysis Intelligence Index. At Q4_K_M it needs roughly 186 GB of VRAM, placing it in the 48 GB+ / multi-GPU hardware tier.
Benchmarks
Artificial Analysis Intelligence Index - GLM-5.3 vs. leading closed models:
| Model | Intelligence | Coding | GPQA |
|---|---|---|---|
| GLM-5.3 | 59.5 | - | 91.7 |
| Claude Opus 5 (max) | 63.1 | - | 93.2 |
| Claude Fable 5 (with fallback) | 62.1 | - | 92.6 |
| GPT-5.6 Sol (max) | 60.9 | - | 94.1 |
| Grok 4.6 (high) | 60.9 | - | 94.9 |
| Qwen3.8 Max | 58.1 | - | 92.7 |
Source: Artificial Analysis (2026-08-29).
Popularity
GLM-5.3 has 34 downloads in the last month on HuggingFace and 1,526 likes.
Frequently asked
Quick answers to common questions
How much VRAM does GLM-5.3 need?
GLM-5.3 with 321.3B parameters needs approximately 186 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is GLM-5.3 better than other zai-org models?
GLM-5.3 has 321.3B parameters with 1,048,576 context - a strong choice for general use.
What license is GLM-5.3 under?
GLM-5.3 is released under the mit license, making it suitable for most commercial and personal projects.
What hardware runs GLM-5.3 well?
With 321.3B parameters, GLM-5.3 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-5.3?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~228 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can GLM-5.3's context window handle?
GLM-5.3 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-5.3?
GLM-5.3 competes with other 161B–482B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec