Model Detail

GLM-5.3-Flash is a multimodal model with 160.7B parameters released by zai-org. The model is registered under the image-text-to-text pipeline tag on Hugging Face, and supports text+image+video->text inputs, distributed under the permissive mit license.

GLM-5.3-Flash reports a Chatbot Arena ELO of 1,475 across 9,479 votes. Other benchmark slots are still empty in our dataset, so this single figure is best read as a partial picture rather than a full evaluation.

GLM-5.3-Flash is priced at $0.11/M input tokens and $0.35/M output tokens. Operationally the model offers a 1049K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.

GLM-5.3-Flash ships with 160.7B parameters. Total weight footprint is approximately 321.3 GB, which is the relevant figure when planning local-inference VRAM. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.

Downloads of GLM-5.3-Flash have moved +0.2% over the past 24 hours, +0.6% over the trailing seven days. The trend is mildly positive, consistent with a model that is being picked up incrementally rather than going viral. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

GLM-5.3-Flash is best fit for mixed text-and-image reasoning tasks such as document understanding, high-volume batch jobs where per-call cost dominates the budget, and long-context tasks such as full-codebase analysis or book-length summarization (1049K tokens). Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.