GLM 5V Turbo
Zhipu AI's native multimodal coding model for vision-to-code and agentic workflows.
Overview
What is GLM 5V Turbo
GLM 5V Turbo is Zhipu AI's native multimodal foundation model for vision-based coding and agentic tasks. Released in April 2026, it processes images, video, text, and files to generate code, autonomously explore GUIs, and execute complex workflows through built-in reasoning and tool use.
Running it privately on Venice
On Venice, GLM 5V Turbo runs under an anonymized privacy tier with zero retention of your prompts — no storage, no profiling, no training reuse. You get the full multimodal coding and agentic capability without surrendering sovereignty over your data to Big-Tech infrastructure.
Assessment
Strengths and limitations
- Native multimodal agent architecture built around CogViT, processing images, video, text, and files as core reasoning inputs rather than bolted-on modalities.
- Design-to-code specialist: converts screenshots, mockups, and wireframes into runnable HTML, CSS, and JavaScript with strong layout fidelity.
- Agent-ready toolchain: supports function calling, structured JSON output, reasoning, and web search for autonomous loops and long-horizon planning.
- Efficient MoE inference routes only a subset of parameters per token, keeping latency low while handling complex coding tasks.
- Handles long-context agent workflows and large codebases without losing coherence.
- Closed weights: not open-source, so you cannot self-host, fine-tune, or audit the full model weights.
- Not uncensored: carries standard safety alignment, which may refuse certain edge-case requests.
- Vision scope is coding-centric: excels at UI and design interpretation but is not optimized for open-ended visual creativity or general photorealistic understanding.
- Text-only output: can read images and video but cannot generate them.
- Agent workflows benefit from explicit system prompting to keep long-horizon tasks on track and avoid looping.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- Zhipu AI (Z.ai)
- Released
- April 2026
- Architecture
- Mixture of Experts (MoE)
- Parameters
- 744B total, 40B active per token
- Input modality
- Video, image, text, file
- Output modality
- Text
- Open weights
- No
- Context window
- 200K tokens
- Max output
- 32.768K tokens
- Capabilities
- Vision, Function calling, Reasoning, Web search, Code-optimized
- Privacy on Venice
- Anonymized — prompts not stored
- Available on Venice since
- Apr 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-5v-turbo",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $1.50 per 1M input tokens and $5 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 5V Turbo | Multimodal coding & agents | 200K tokens | No | $1.50 in · $5 out / 1M |
| Claude Sonnet 4.6 | General reasoning & writing | 1M tokens | No | $3.60 in · $18 out / 1M |
| DeepSeek V3.2 | Efficient open coding | 160K tokens | Yes | $0.33 in · $0.48 out / 1M |
| GLM 5.1 | Long-context text & agents | 200K tokens | Yes | $1.10 in · $4.15 out / 1M |
The native vision-to-code choice with tool use and web search on Venice.
Use cases
What it is good for
- 01Frontend development: turning Figma exports, screenshots, or hand-drawn sketches into production code.
- 02Visual debugging and QA: analyzing UI screenshots to spot layout issues or accessibility problems.
- 03Autonomous GUI agents: browsing interfaces, mapping elements, and executing actions via tool use.
- 04Multimodal coding assistants: reading design docs, video demos, and code repos in a single long-context session.
- 05Structured data extraction from mixed document types (images, PDFs, text) using JSON schema output.
Prompting
Getting better results
Upload high-resolution mockups and explicitly label the design system (colors, fonts, spacing) for pixel-perfect code reproduction.
Use structured output mode with a JSON schema when the model needs to return parsed UI hierarchies or component trees.
For agent loops, define tool schemas clearly and set max iteration limits to prevent runaway calls.
Combine web search with vision input when the task requires verifying live design references against current documentation.
FAQ
Frequently asked questions
GLM 5V Turbo is Zhipu AI's native multimodal foundation model released in April 2026. It processes images, video, text, and files to perform design-to-code, GUI automation, and agentic coding workflows with built-in reasoning and tool use.
Venice bills GLM 5V Turbo at $1.50 per 1 million input tokens and $5 per 1 million output tokens. Cached input is $0.30 per 1 million tokens. You pay as you go with credits, with no subscription required.
New Venice accounts receive free credits and a daily allowance, so you can try GLM 5V Turbo without a credit card. Sustained use requires purchasing credits at the per-token rates.
No. GLM 5V Turbo is a closed-weights proprietary model. While Zhipu AI has released open models in the GLM family, this specific variant is not available as open-source weights for self-hosting.
Yes. It supports function calling, structured JSON output, multiple image inputs, video understanding, reasoning, and web search — making it suitable for autonomous agent workflows.
Choose GLM 5V Turbo for vision-based coding, design-to-code, and multimodal agent tasks at a lower price. Choose Claude Sonnet 4.6 if you need a generalist with a 1M token context for long documents and can pay the premium.
No. It accepts images, video, text, and files as inputs, but its output is text-only. It writes code, plans, and structured data rather than producing visual media.
It runs under Venice's anonymized tier: your prompts are not stored, profiled, or used for training. Venice provides zero retention, so your multimodal coding sessions leave no persistent history on the platform.
Run GLM 5V Turbo privately
No prompt logging. No data used for training.