Z.aiZ.ai·text

GLM 5.3

CodeReasoningWeb searchFunction calling
Advertised as glm-5-3glm-5.3z-ai-glm-5-3z-ai/glm-5.3
Quick reference
GLM 5.3 — TLDR
  • 🏢 Z.ai's 2026 flagship reasoning model, successor to GLM-5.2 in the GLM family.
  • 📏 One-million-token context window for long documents and extended agent runs.
  • 🆕 Same base model as GLM-5.2 — all gains from scaled post-training.
  • 🧠 Reinforcement learning across harder executable environments drives the coding gains.
  • 🔧 Built for complex software engineering, terminal work and long-horizon agent tasks.
  • 🎯 Z.ai reports a 50% gain over GLM-5.2 on its in-house Code Bench.
  • 💬 Text in, text out; function calling, structured JSON output, context caching.
  • 🌐 Web search and external tool integration for agentic workflows.
💰 Best price on AntSeed
$0.0050 / $0.016
per 1M · cheapest in / out
📏 Context
1M tokens
🐜 Sellers
15
advertising on AntSeed
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 large language…

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About this model

GLM 5.3 is the newest text model in Z.ai's GLM line, positioned as a large-scale reasoning system for complex software engineering, terminal operations and long-horizon agent work. Z.ai describes it as a flagship update that keeps the one-million-token context window and the text-only input and output of its predecessor.

Architecturally, the release is a post-training update. According to Z.ai's own documentation, GLM 5.3 uses the same base model as GLM 5.2, with every improvement coming from post-training rather than new pretraining. Z.ai attributes the gains to reinforcement learning across more and harder executable environments, including automatically synthesised long-horizon tasks with multi-step dependencies and hidden state, verified by a judge agent that checks each task is solvable. On the provider's reported in-house Z.ai Code Bench, this yields a 50% performance gain over GLM 5.2 in coding.

Compared with earlier generations such as GLM 5.1, GLM 5 and GLM 4.7, the 5.3 release concentrates on agentic durability — runs that span many steps and tool calls — alongside a better balance between answer quality and the number of tokens spent reasoning. Practical features include function calling, external tool use, web search, structured JSON output and context caching for long sessions.

The coding figures cited above are reported by Z.ai itself rather than independently reproduced under a common evaluation harness.

View source on GitHub ↗View model card on HuggingFace ↗
Sources
docs.z.aiGLM-5.3 - Overview - Z.AI DEVELOPER DOCUMENT· docs.z.ai

This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.

Usage on AntSeed
Tokens served
1.21B
input + output
Requests
17,287
settled calls
Buyers
53
distinct, on this model
Sellers used
18
of 15 advertising
Settled
$185.39
gross USDC, this model
Sellers serving GLM 5.3 (15)compare on the network explorer →
SellerReputation↓RoutingInput $/MCached $/MOutput $/MCategoriesAPI

"Best price" and the seller table are live AntSeed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the AntSeed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on AntSeed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.