GLM 5 Blog
Blog
Read about our latest product features, solutions, and updates.
GLM 5.2 vs Llama 3.1 Nemotron 70B: NVIDIA Fine-Tuned vs Zhipu Frontier
GLM 5.2 vs Llama 3.1 Nemotron 70B — compare benchmark scores, pricing, deployment options, and which model delivers better results for enterprise AI workloads.
GLM 5.2 vs Phi-4: Frontier Scale vs Efficient Small Model
Phi-4 is a 14B-parameter model that runs on a single GPU. GLM 5.2 is a 753B MoE model with 1M-token context and frontier coding benchmarks. Here is when each is the right tool.
GLM 5.2 vs Qwen 3: Chinese Open-Weight AI Models Compared
Qwen 3 235B-A22B costs 73% less per output token than GLM 5.2, but GLM 5.2 has 8x larger context and stronger agentic benchmark results. Here is how these open-weight leaders compare.
GLM 5.2 Web Search: Real-Time Browsing with Tool Use
How to enable real-time web search in GLM 5.2 using function calling — build a web-aware AI assistant with Python code examples and Zhipu's native search tool.
GLM 5.3 on AI Leaderboards: Where It Ranks (AA, Terminal-Bench, CyberGym)
GLM 5.3 leaderboard positions — AA Intelligence Index 60 (8th of 181, tied Kimi K3), Terminal-Bench 3.0 open SOTA 28.3, CyberGym 84.5 best public. Full ranking context.
GLM 5.3 API: Endpoints, Parameters & Python Examples
GLM 5.3 API guide — model ID glm-5.3, endpoints (OpenAI/Anthropic-compatible), thinking parameters (reasoning_effort low/high/max), pricing $1.40/$4.40, and Python code examples.
GLM 5.3 Architecture: Same Base as 5.2, ~743B MoE — Post-Training Explained
GLM 5.3 architecture — same ~743B-753B MoE base model as GLM 5.2, all gains from post-training (IndexShare, SAO, slime), 1M context, 128K output. What's inside and what it means.
GLM 5.3 Artificial Analysis Score: 60 on the Intelligence Index — Explained
GLM 5.3 scores 60 on Artificial Analysis Intelligence Index (max reasoning) — matching Kimi K3, 3 behind Claude Opus 5, 8th of 181 models, at the lowest cost per task ($0.68).
GLM 5.3 Benchmarks: Full Scores vs GLM 5.2, Kimi K3, GPT-5.6 Sol & More
GLM 5.3 benchmark results — Terminal-Bench 3.0 28.3, DeepSWE 66.9, CyberGym 84.5 (SOTA), AutomationBench 48.2. Full official table vs GLM 5.2, Kimi K3, Opus 4.8, GPT-5.6 Sol.
GLM 5.3 Coding Plan: Prices, Credits, Limits & Best Tier
Compare GLM 5.3 Coding Plan Lite, Pro and Max prices, 5-hour and weekly credits, off-peak rules, credit multipliers, and which tier fits your workload.
GLM 5.3 Found a Vulnerability in Cursor: What We Know
GLM 5.3 reportedly found a 'potentially serious vulnerability' in Cursor (SpaceX-acquired) — the first real-world exploit of its cyber capability. What Z.ai said, the trusted-access controls, and what it means.
GLM 5.3 Cybersecurity: CyberGym SOTA, 2,436 Real Vulnerabilities & the Disclosure Ledger
GLM 5.3's emergent cyber capability — CyberGym 84.5 (best public result), 2,436 real-world vulnerabilities found across 269 projects, and why Z.AI holds the weights for safety hardening.
GLM 5.3 DeepSWE v1.1: 66.9 Explained — What the Score Means
GLM 5.3 DeepSWE v1.1 score 66.9 (from 46.2) explained — what DeepSWE tests, the full leaderboard vs Kimi K3, DeepSeek-V4, Opus 4.8, Fable 5, GPT-5.6 Sol, and why it matters for real engineering.
How Good Is GLM 5.3 Flash for Building Android Apps?
A practical framework for using GLM 5.3 Flash on Android app work, from Kotlin and Compose changes to emulator checks and review.
GLM 5.3 Flash Benchmarks: Vendor Claims vs. Independent Numbers
GLM 5.3 Flash benchmarks decoded: 84.3 on Terminal-Bench 2.1, 63.4 DeepSWE, 57 on Artificial Analysis. Which numbers are first-party and which survive scrutiny.
GLM 5.3 Flash on DGX Spark: Does It Fit, and How Fast Is It?
Running GLM 5.3 Flash on NVIDIA DGX Spark: 328 GB of FP8 weights vs 128 GB per unit. What fits, what needs quantizing, and community-reported throughput on 2x Sparks.
Which Harness Is Best for GLM 5.3 Flash? A Practical Choice Guide
Choose a coding harness for GLM 5.3 Flash by checking official support, tool use, context handling, and the quality of your own coding loop.
GLM 5.3 Flash on OpenRouter: Model ID, Provider Prices & API Setup
GLM 5.3 Flash on OpenRouter: model ID z-ai/glm-5.3-flash, ten providers with a 2x price spread, 1M context, and curl + Python setup with provider routing rules.
GLM 5.3 Flash Parameters and Size: 320B-A18B, 328 GB, MIT Weights
GLM 5.3 Flash has 320B total parameters, 18B active, 45 layers and 288 experts. Full architecture, real model size on disk, the Hugging Face and GitHub repos, and what MIT permits.
GLM 5.3 Flash Pricing: Real Cost per Token, per Provider (2026)
GLM 5.3 Flash pricing decoded: $0.15/$0.50 list, a temporary 50% launch discount, and a 10-provider price spread. Worked cost examples and the discount trap.
GLM 5.3 Flash on Reddit: What the Community Actually Found
GLM 5.3 Flash on Reddit — how r/LocalLLaMA fingerprinted Ox Alpha before the reveal, what self-hosters found, and which community claims hold up against official data.
GLM 5.3 Flash vs DeepSeek V4 Flash: Which Cheap Open Model Wins?
GLM 5.3 Flash vs DeepSeek V4 Flash compared on price, speed, intelligence, multimodality and self-hosting. 320B-A18B vs 284B-A13B, both MIT, both 1M context.
How to Get GLM 5.3 FlashX: Subscription Plans, Zcode, OpenCode, and API Access
GLM-5.3-FlashX is a documented Z.ai API model code at $0.37/$1.25 per 1M tokens. The same official page says it is not on the Coding Plan yet. How API, Zcode, and OpenCode actually differ.
Can You Run GLM 5.3 FlashX Locally? GGUF, Ollama, and the Weights Reality
There is no official GLM-5.3-FlashX checkpoint. Local installs use GLM-5.3-Flash weights (a ~226 GB safetensors snapshot), not a GGUF or Ollama tag for the hosted fast tier.
GLM 5.3 FlashX vs DeepSeek V4.1 Flash: Which Fast-Tier Model Wins?
GLM-5.3-FlashX is Z.ai's fast serving tier at $0.37/$1.25 per 1M tokens. DeepSeek-V4.1-Flash is a 552B model with peak $0.30/$1.20 and half-price off-peak. A decision, not a fake shared leaderboard.
What Is GLM-5.3-FlashX? Z.ai's High-Speed Flash Variant, Explained
GLM-5.3-FlashX is the high-speed variant of GLM-5.3-Flash: up to 200 tokens/s claimed, $0.37/$1.25 per 1M tokens, same 320B-A18B architecture with a 1M context window. Claimed vs. measured speed, the price premium math, and when FlashX is worth it over Flash.
GLM 5.3 Free
GLM 5.3 is Z.ai's newest flagship model — 1M-token context, 128K max output, and always-on reasoning. Try GLM 5.3 free in your browser today, with confirmed facts separated from unconfirmed details.
GLM 5.3 Local Deployment: Hardware Requirements & What to Prepare
GLM 5.3 local deployment guide — ~753B MoE model, VRAM estimates, SGLang/vLLM setup, quantization options, and a preparation checklist before weights drop on HuggingFace.
How to Migrate from GLM 5.2 to GLM 5.3: Thinking Parameters & Breaking Changes
GLM 5.3 migration guide — the required thinking.enabled change, reasoning_effort (low/high/max), what breaks from GLM 5.2, and a step-by-step cutover checklist for API and Coding Plan users.
GLM 5.3 Ollama：本地运行指南与硬件要求（权重发布后可用）
GLM 5.3 能在 Ollama 上跑吗？目前 Ollama 最新只有 GLM 5.2——GLM 5.3 权重 8 月底才开源。本文说明等待时间、硬件要求（约 743B MoE）与发布后如何在 Ollama 部署。