Z.ai Releases 320B GLM-5.3-Flash With MIT Weights, 1M Context and Low API Rates

Z.ai has released GLM-5.3-Flash, a 320-billion-parameter mixture-of-experts model that activates 18 billion parameters for each token. It combines MIT-licensed open weights, native image and video input, a 1-million-token context window and API input priced at $0.15 per million tokens.

The August 26 release is the first natively multimodal model in Z.ai’s GLM-5 series. Its mixture-of-experts design has 320 billion total parameters but selects 18 billion per token. Z.ai says the model supports image and video inputs natively, rather than limiting the release to text.

GLM-5.3-Flash uses a hybrid sparse- and linear-attention architecture. Z.ai describes that design as cutting compute requirements significantly, a consequential claim for a model built to accept up to 1 million tokens of context in one request.

$0.15 per millionInput tokens

Z.ai lists input usage at $0.15 per million tokens.

$0.50 per millionOutput tokens

Z.ai lists output usage at $0.50 per million tokens.

$0.03 per millionCached input

Cached inputs are listed at $0.03 per million tokens.

The company offers the model through its API and has released the weights under the MIT license in FP8 and BF16 formats on Hugging Face and ModelScope. The permissive license allows use, modification and commercial distribution of the weights, according to the launch coverage.

On the DeepSWE v1.1 coding benchmark, GLM-5.3-Flash scored 63.4, compared with 46.2 for GLM-5.2. That establishes a reported improvement over the prior model; it does not by itself establish a ranking against other frontier systems.

Z.ai says GLM-5.3-Flash runs across clusters of domestically manufactured Chinese AI accelerators. It also reports that custom serving stacks and hardware-tailored quantization produced a threefold end-to-end performance improvement over a more generic deployment approach. How that result transfers to other hardware and workloads remains unresolved.