Model overview
Two quants, 87 GB and 112 GB, built by assigning every tensor class by hand instead of using a preset. GLM-5.3-Flash is 320.6B total parameters with 17.3B active. Three things about its architecture drive every decision in these quants. **It is 94.9% routed experts.** 304.4B of the 320.6B parameters sit in `ffn_*_exps` tensors across 42 sparse layers, 288 experts each, 8 active per token plus 1 shared. Everything else in the model, all the attention and embeddings and norms, is 8.9B parameters. That means the file size is set almost entirely by one decision: how many bits the experts get. Everything else can be kept generous for almost nothing.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。