Introducing GLM-5.3-Flash
- Leading capabilities at a highly competitive price
- Natively multimodal with a 1M-token context window
- A 320B-A18B model released under the MIT License
- Previously previewed as Ox Alpha, running entirely on Chinese AI chips
Blog: https://t.co/tzOmB7gdZP
Available now across all official platforms:
Weights: https://t.co/9LRMahY9Wa
API: https://t.co/VcaQnzYmS9
Coding Plan: https://t.co/Nk8Y98HNhU
ZCode: https://t.co/Peepqv4XSx
Chat: https://t.co/WCqWT0qCQb
AutoClaw:
Standard API Pricing for GLM-5.3-Flash (per 1M tokens)
- Input: $0.15
- Output: $0.50
- Cached input: $0.03
On the https://t.co/w8mHB85z4n Code Bench, which measures real-world coding performance, GLM-5.3-Flash clearly outperforms GLM-5.2 at every effort level and performs on par with Claude Opus 4.8.
Architectural enhancements, combined with an optimized pre-training corpus, enable GLM-5.3-Flash to deliver greater intelligence with less compute.
multimodal — A model that works with more than text — reading images, audio, or video, and sometimes generating them too.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
Why it matters
Open MIT weights plus $0.15 input and $0.50 output per million tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → puts a 1M-context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →multimodalA model that works with more than text — reading images, audio, or video, and sometimes generating them too.Full definition → model within reach for self-hosting or cheap API routing, with vendor benchmarks claiming parity with far pricier closed models.
