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Z.ai's release of GLM-5.3-Flash marks a significant shift in the multimodal model landscape by combining native image/video input with an unprecedented 1M-token context window. The architecture's hybrid attention mechanism and MoE design offer substantial compute efficiency gains, while the MIT license lowers barriers for self-hosting among organizations with sufficient GPU resources. This development directly impacts enterprise workflows involving long-context code analysis, log processing, and complex document reasoning.
Z.ai Releases GLM-5.3-Flash: A 320B-A18B Natively Multimodal MoE With a 1M-Token Context
Z.ai has released GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series — a 320B-total / 18B-active MoE with a 1,048,576-token context window, MIT-licensed weights on Hugging Face, and API pricing at $0.15/M input and $0.50/M output. It scores 84.3 on Terminal-Bench 2.1 and 63.4 on DeepSWE v1.1, using hybrid KDA linear plus NoPE sparse MLA attention to cut attention compute ~3× and KV cache 4.4× versus GLM-5.3. The post Z.ai Releases GLM-5.3-Flash: A 320B-A18B Natively Multimodal MoE With a 1M-Token Context appeared first on MarkTechPost.
Z.ai has released GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series and the cheapest capable coding model the lab has shipped. It is a mixture-of-experts model with 320B total parameters and 18B active per token, a 1,048,576-token context window, and image and video input — released under an MIT license with weights on Hugging Face. According to Z.ai reports, it beats GLM-5.2 across benchmarks and real workloads at roughly one-tenth the price, while landing within half a…
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