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---
title: GLM-5.3-Flash | daily.dev
description: GLM-5.3-Flash is a new natively multimodal model in the GLM-5 series from Z.ai, with 320B total parameters but only 18B active parameters via a...
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og:description: GLM-5.3-Flash is a new natively multimodal model in the GLM-5 series from Z.ai, with 320B total parameters but only 18B active parameters via a...
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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# GLM-5.3-Flash

**[nerdalytics](https://daily.dev/sources/jcwojcedqzpspqbvk73nc)** · [@nerdalytics](https://daily.dev/nerdalytics) · 14 upvotes · 4 comments

## Summary

GLM-5.3-Flash is a new natively multimodal model in the GLM-5 series from Z.ai, with 320B total parameters but only 18B active parameters via a mixture-of-experts-style design. It introduces a hybrid sparse and linear attention architecture plus Manifold-Constrained Hyper-Connections (mHC), trained on a 30T-token multimodal corpus. It reportedly 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. The model card includes deployment instructions for Transformers, vLLM, SGLang, KTransformers, and Docker, along with benchmark results on Terminal-Bench 2.1, DeepSWE, HLE, and other agentic evaluation suites.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://huggingface.co/zai-org/GLM-5.3-Flash>

## Community discussion

Top comments from developers on daily.dev.

**@geniusatwork** · 3 upvotes

> Qwen3.8-Flash and GLM-5.3-Flash on the same day 🤗

**@nerdalytics** · 2 upvotes

> Ox Alpha is quite cheap, but it's not a great model.
>
>
> Started a worktree+branch, after compacting the session this model started to edit files in main branch.
>
>
> I have a fork and workered on issue, I asked the model to check how far behind we are compared to origin/HEAD. It only checked my fork. Also ignored details like the repo URL of the upstream repo, and kept looking for CI/CD feedback on my fork.
>
>
> It fumbled with Rust code in a Tauri+Svelte project, and kept calling my clippy settings pedantic and aggressive. The Svelte code wasn't great either. Pasting a screenshot with edited...

**@agustinbarrientos** · 0 upvotes

> What peak VRAM and tokens per second did each official serving path reach? Did any path fit hardware that the active-parameter count made look unrealistic?

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---

Tags: [#llm](https://daily.dev/tags/llm), [#deep-learning](https://daily.dev/tags/deep-learning), [#vllm](https://daily.dev/tags/vllm)

[View this post on daily.dev](https://daily.dev/posts/glm-5-3-flash-bh9uwyinw)

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