Z.ai's open-weight, natively multimodal LLM with 320B parameters (18B active), optimized for coding, agents, and vision at flash cost.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-glm-5-3-flash",
"messages": [{ "role": "user", "content": "Build without permission." }]
}'Overview
GLM 5.3 Flash is Z.ai's open-weight large language model released in August 2026, featuring 320B total parameters with 18B activated. It combines sparse and linear attention for efficiency, supports vision, function calling, and web search, and delivers high performance at low cost for coding and agentic workflows.
On Venice, GLM 5.3 Flash runs with full privacy: zero retention, end-to-end encryption, and execution in a trusted execution environment (TEE). You get uncensored access to its open weights and multimodal capabilities without your prompts being stored or profiled — true sovereignty over your AI interactions.
Agent quickstart
The API is OpenAI-compatible: change the base URL and the model id and existing client code works unchanged.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-glm-5-3-flash",
"stream": true,
"messages": [{ "role": "user", "content": "Draft the release note." }]
}'curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-glm-5-3-flash",
"messages": [{ "role": "user", "content": "Find the rate limits." }],
"tools": [{
"type": "function",
"function": {
"name": "search_docs",
"description": "Search the API documentation.",
"parameters": {
"type": "object",
"properties": { "query": { "type": "string" } },
"required": ["query"]
}
}
}],
"tool_choice": "auto"
}'import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
resp = client.chat.completions.create(
model="e2ee-glm-5-3-flash",
messages=[{"role": "user", "content": "Build without permission."}],
)
print(resp.choices[0].message.content)Specifications
Assessment
Use cases
Prompting
Use explicit spatial descriptions when referencing images (e.g., 'top-left button', 'chart on right').
Break complex tasks into steps and use function calling to delegate subtasks.
Enable web search when up-to-date information is needed beyond the training cutoff.
Use the reasoning mode for complex logic or math-heavy prompts.
Include file paths or repo structures when working on code to improve context accuracy.
Leverage vision input to feed rendered UIs or dashboards for real-time analysis.
Alternatives
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| GLM 5.3 Flash | Cost-efficient multimodal coding | 1M tokens | Yes | $0.16 in · $0.54 out / 1M |
| DeepSeek V4 Flash 0731 | Fast text-only reasoning | 1M tokens | Yes | $0.17 in · $0.35 out / 1M |
| Google Gemma 4 31B Instruct | Lightweight open model | 256K tokens | Yes | $0.12 in · $0.36 out / 1M |
| Claude Opus 5 | High-intelligence reasoning | 1M tokens | No | $6 in · $30 out / 1M |
GLM 5.3 Flash is the best pick for teams needing open, private, multimodal AI for coding and agents at the lowest cost — it beats closed rivals on price and beats open rivals on vision integration.
Pricing
Billed per token on Venice: $0.16 per 1M input tokens and $0.54 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Getting a key
Create a key in API settings. Nothing else is required to start.
Export it as VENICE_API_KEY so the snippets above run unedited.
Point an existing OpenAI client at https://api.venice.ai/api/v1. The scheme is part of the value: an OpenAI client given a bare host does not resolve it.
Pass e2ee-glm-5-3-flash as the model and send the request.
FAQ
GLM 5.3 Flash is an open-weight, natively multimodal LLM from Z.ai with 320B total parameters and 18B active. It supports vision, function calling, web search, and long-context reasoning, optimized for coding and agentic workflows at low cost.
On Venice, it costs $0.16 per 1M input tokens and $0.54 per 1M output tokens, with cached input at $0.04/1M. There are no subscription fees — you pay only for what you use.
It is open weights under the MIT license, so you can self-host and modify it freely. However, it is not free to use via API — pricing applies based on token volume.
Yes, it is the first natively multimodal model in the GLM series, with vision integrated directly into its architecture for tasks like UI analysis, visual coding, and image-based reasoning.
Yes, it supports function calling and tool use, allowing it to interact with APIs, databases, and external systems as part of autonomous workflows.
It supports up to 1,000K tokens of context, enabling it to process extremely long documents, codebases, or conversation histories in a single session.
GLM 5.3 Flash wins if you need vision and multimodal coding; DeepSeek V4 Flash is faster and slightly cheaper for text-only tasks but lacks image input and native visual reasoning.
Yes — on Venice, it runs with zero retention, end-to-end encryption, and in a trusted execution environment (TEE), so your prompts are never stored or profiled.
One key, free to start, no credit card.