GLM 5.3
Z.ai's GLM 5.3 is a code-optimized, reasoning-first LLM with emergent cybersecurity capabilities — open weights, 1M-token context, and private on Venice.
Overview
What is GLM 5.3
GLM 5.3 is Z.ai's flagship large language model, released on August 14, 2026, optimized for complex software engineering, long-horizon tasks, and cybersecurity. It improves over GLM 5.2 by 50% in coding performance through scaled post-training, not architecture changes, and supports function calling, web search, and structured output.
Running it privately on Venice
On Venice, GLM 5.3 runs with anonymized privacy — your prompts are never stored or used for training. You gain full sovereignty over inputs while accessing a model with frontier coding and agent capabilities, all without surveillance. This is uncensored, permissionless AI for developers who demand privacy and performance.
Assessment
Strengths and limitations
- State-of-the-art coding performance among open-weight models, with 50% gain over GLM 5.2 on Z.ai Code Bench.
- Emergent cybersecurity capabilities, leading on CyberGym for vulnerability discovery and exploitation.
- 1M-token context window enables ultra-long-horizon agent tasks and massive codebase reasoning.
- Always-on reasoning with configurable effort levels (`low`, `high`, `max`) for balanced speed and depth.
- Supports function calling, web search, and JSON schema output — ideal for agentic workflows.
- Text-only input: no vision or multimodal capabilities.
- Open weights released two weeks after launch; immediate self-hosting not possible at release.
- No end-to-end encryption or TEE protection on Venice — privacy is anonymized, not encrypted.
Capabilities
What it supports
- Function calling / tool use
- Reasoning with effort levels
- Web search
- Code-optimized generation
- Structured output (JSON schema)
- Multimodal input (images)
- End-to-end encrypted sessions
- Offline self-hosting at launch
Specifications
Datasheet
- Maker
- Z.ai
- Open weights
- No
- License
- Z.AI License
- Context window
- 1,000K tokens
- Released
- August 14, 2026
- Architecture
- Mixture-of-Experts (MoE), IndexShare
- Parameters
- 743B total (40B active)
- Max output
- 131.072K tokens
- Capabilities
- Function calling, Reasoning, Web search, Code-optimized
- Privacy on Venice
- Anonymized — prompts not stored
- Available on Venice since
- Aug 2026
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "z-ai-glm-5-3",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $1.75 per 1M input tokens and $5.50 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| GLM 5.3 | Coding & cyber agents | 1M tokens | No | $1.75 in · $5.50 out / 1M |
| Claude Opus 5 | General reasoning | 1M tokens | No | $6 in · $30 out / 1M |
| DeepSeek V4 Flash 0731 | Low-cost inference | 1M tokens | Yes | $0.17 in · $0.35 out / 1M |
| GLM 5.1 | Open-weight coding | 200K tokens | Yes | $1.10 in · $4.15 out / 1M |
Choose GLM 5.3 when you need open, private, and powerful coding and cybersecurity reasoning at scale — it’s the best balance of performance, openness, and cost for developer agents.
Use cases
What it is good for
- 01Autonomous software engineering agents processing multi-file repositories.
- 02Vulnerability discovery and penetration testing in secure development pipelines.
- 03Long-form code generation and refactoring across 1M-token contexts.
- 04AI agents that use web search and external tools in complex workflows.
- 05High-fidelity JSON output for API-driven automation and data pipelines.
Prompting
Getting better results
Use explicit reasoning effort: set `reasoning=high` for complex debugging or `max` for security audits.
Leverage web search by phrasing queries as questions needing up-to-date results.
Structure output demands with JSON schema when parsing code or data.
Break long coding tasks into phases — the model excels at long-horizon planning.
Include file paths and function signatures in prompts for precise code generation.
Use natural language to describe exploit chains — the model shows emergent cyber reasoning.
Version history
Earlier open-weight version with 200K context.
Base model reused in 5.3 with post-training gains.
Current — post-trained for coding and cyber SOTA.
FAQ
Frequently asked questions
GLM 5.3 is Z.ai's flagship large language model, released on August 14, 2026, optimized for complex software engineering, long-horizon tasks, and cybersecurity. It improves over GLM 5.2 by 50% in coding performance through scaled post-training, not architecture changes, and supports function calling, web search, and structured output.
GLM 5.3 is open weights under the Z.AI License, allowing use, modification, and redistribution with attribution. The weights were released two weeks after launch following a safety review. It is not free to run — pricing is $1.75 per 1M input tokens and $5.50 per 1M output tokens on Venice.
GLM 5.3 has a 1,000,000-token context window, enabling ultra-long-horizon reasoning, massive codebase analysis, and extended agent workflows without truncation.
No, GLM 5.3 is text-only. It does not accept images or other multimodal inputs — only text prompts and structured data.
GLM 5.3 is stronger in coding and cybersecurity tasks and far cheaper to run, while Claude Opus 5 excels in general reasoning and knowledge work. GLM 5.3 is open weights and runs privately on Venice; Opus 5 is closed and proprietary.
Yes, GLM 5.3 supports tool use and function calling as part of its agentic capabilities, enabling integration with external APIs, code executors, and web search tools in automated workflows.
Yes, GLM 5.3 is open weights under the Z.AI License, so it can be self-hosted, fine-tuned, and modified. However, the checkpoint was released two weeks after launch to allow for safety evaluation and hardening.
GLM 5.3 is not uncensored — it includes standard safety filters. However, on Venice, it runs with anonymized privacy, meaning prompts are not stored or used for training, preserving user sovereignty.
Run GLM 5.3 privately
No prompt logging. No data used for training.