Z.aiZ.ai·💬 Text Generation·New

GLM 5.3 Flash

ReasoningVisionCodeFunction CallingWeb Searchprivate
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Quick reference
GLM 5.3 Flash — TLDR
  • 🏢 Z.ai's efficiency-focused Flash model, released August 2026, weights on Hugging Face
  • 🧠 Mixture-of-experts: 320B total parameters, roughly 18B active per token
  • 🆕 First GLM to combine sparse attention with linear attention
  • 📏 Context window of 1,048,576 tokens
  • 👁️ Handles visual context alongside text, returning text output
  • 🔧 Function calling, web search and configurable reasoning effort
  • 🎯 Built for long-horizon software engineering and sustained agentic loops
  • 📚 Artificial Analysis measures 57 on its Intelligence Index
💰 Pricing
$0.150 / $0.500
per 1M · input / output
📏 Context
1.0M tokens
📅 On Venice since
Aug 21, 2026
6 days ago
Provider

Z.ai, formally Knowledge Atlas Technology Joint Stock Co., Ltd., is a Chinese technology company specializing in artificial intelligence. Previously known internationally as Zhipu AI, the company rebranded to Z.ai in 2025. Its core focus is the GLM family of…

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14 models on Venice
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Since Apr 1, 2024

About this model

GLM 5.3 Flash is Z.ai's efficiency-oriented entry in the GLM-5 line, arriving days after the flagship GLM 5.3 and positioned for coding agents, complex reasoning and production workloads that combine text with visual context. It succeeds earlier compact releases such as GLM 4.7 Flash in the Flash tier.

Architecturally it departs from its predecessors. Z.ai describes a 320-billion-parameter mixture-of-experts network that activates roughly 18 billion parameters per token, and the first model in the GLM series to combine sparse attention with linear attention — a hybrid the company says lowers long-context serving cost while preserving precise long-context behaviour. The catalog context window is 1,048,576 tokens, and reasoning effort is configurable in the API. Function calling and web search are supported.

On generational gains, Z.ai's own model card and launch post report that GLM 5.3 Flash improves on GLM 5.2 across its benchmark suite and real-world workloads at substantially lower serving cost. Its published tables list, as vendor-run results, 63.4 on DeepSWE v1.1 against 46.2 for GLM 5.2; harnesses and context limits differ per test, so these figures are self-reported.

Independently, Artificial Analysis measures the model at 57 on its Intelligence Index. Weights are published on Hugging Face, continuing Z.ai's open-release practice for the GLM family.

This About section is AI-generated from public sources (Claude Opus 5), with no human editing. It may contain inaccuracies — verify critical details against the sources listed above.

Data sources: Venice API · HuggingFace · Wikipedia — enrichment updated 1d ago