GLM-5.3-Flash

About

What GLM-5.3-Flash is

GLM-5.3-Flash 👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/main/resources/wechat.png" target="blank"WeChat</a or <a href="https://discord.gg/QR7SARHRxK" target="blank"Discord</a community. 📖 Check out the GLM-5.3-Flash <a href="https://z.ai/blog/glm-5.3-flash" target="blank"blog</a and GLM-5 <a href="https://arxiv.org/abs/2602.15763" target="blank"Technical report</a. 📍 Use GLM-5.3-Flash API services on <a href="https://docs.z.ai/guides/llm/glm-5.3-flash"Z.ai API Platform. </a Introduction We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute. Serve GLM-5.3-Flash Locally GLM-5.3-Flash supports deployment with the following frameworks. Feel free to try them out: - SGLang — see cookbook - vLLM — see recipes - TokenSpeed — see here - Transformers — see transformers docs - KTransformers — see tutorial - Unsloth — see guide Note - GLM-5.3-Flash supports controlling the thinking budget through the reasoningeffort parameter, which accepts three levels: low, high, and max. It defaults to max if not passed (or if set to any other value). To use low or high, pass them explicitly. For benchmark and leaderboard reproduction, keep the default max. - In the chat template for GLM-5.3-Flash, clearthinking defaults to false if not passed. For chat scenarios, explicitly pass clearthinking=true. Footnotes HLE w/

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys glm-5-3-flash for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (glm-5-3-flash below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"glm-5-3-flash","messages":[{"role":"user","content":"Hello"}]}'

Details

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