The "Niulai" model GLM-5.3-Flash has been officially open-sourced: it features 320B parameters with only 18B activated, and is priced at just one-tenth of GLM-5.2.
39 minutes ago
Beating AI Express News: After Ox Alpha was announced during the day, Zhipu AI officially open-sourced GLM-5.3-Flash in the evening. The model weights are now available on Hugging Face under the MIT license. With a total of 320 billion parameters, only 18 billion are activated per forward pass. It natively supports text, images, and video, making it the first native multimodal model in the GLM-5 series. According to the official, GLM-5.3-Flash outperforms GLM-5.2 overall, with its programming and agent evaluation scores approaching Claude Opus 4.8, while costing only one-tenth of GLM-5.2’s price. It also features a new base model, marking the first time a hybrid sparse and linear attention architecture has been introduced in the GLM main series, and is pre-trained on 30 trillion tokens of multimodal data. Earlier, the community’s small-sample DeepSWE test on Ox Alpha once yielded an 80% score, but this was based on only 10 questions. After expanding the test sample, the score dropped to around 63%, and the tester voluntarily corrected their initial claim. This score still places it in the first tier, though it is not as high as the initial 80% figure. With the model weights now open-sourced, developers can deploy it directly using frameworks like vLLM, SGLang, and KTransformers, eliminating the need to use APIs from anonymous models.
GLM-5.3 Flash slashes prices once more: its API is 50% off for two weeks, with output costing just $0.25 per million tokens.
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