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Model Detail

zai-org logo

GLM-5.3

▲ 101.0%
Provider: zai-orgCategory: llmPipeline: text-generation
DB Score
54.3
Downloads
370K
Likes
2K
Day
+101.0%
Week
+0.0%
Month
+0.0%
Overview

GLM-5.3 is a large language model with 376.7B parameters released by zai-org. The model is registered under the text-generation pipeline tag on Hugging Face, and supports text->text inputs, distributed under a other license.

Pricing & Throughput

GLM-5.3 is priced at $1.26/M input tokens and $3.96/M output tokens. Operationally the model offers a 1049K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. Pricing in this range is the working middle of the API market — neither the cheapest nor the most expensive option per token, so cost-fit is usually a function of how much output you generate.

Technical

GLM-5.3 ships with 376.7B parameters. Total weight footprint is approximately 753.3 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.

Trending Signal

Downloads of GLM-5.3 have moved +101.0% over the past 24 hours. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

Read about databubble_score →
Use Cases

GLM-5.3 is best fit for general-purpose chat and instruction-following workloads, and long-context tasks such as full-codebase analysis or book-length summarization (1049K tokens). Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Pricing
Input ($/M tokens)
$1.26
Output ($/M tokens)
$3.96
Context Window
1049K
Research Paper
arXiv: 2602.15763→
Model Info
Licenseother
Modalitytext->text
Citations1,720 (188 influential)
Recent newsView all news →
Related News
arxivbearish152d ago

AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents

arXiv:2604.02947v1 Announce Type: new Abstract: Computer-use agents extend language models from text generation to persistent action over tools, files, and execution environments. Unlike chat systems, they maintain state across interactions and translate intermediate outputs into concrete actions. T

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