GLM-4.6V Now on SiliconFlow: Native Multimodal Tool Use Meets SoTA Visual Intelligence

TL;DR: GLM-4.6V, Z.ai's latest multimodal large language model, is now available on SiliconFlow. Featuring a 131K multimodal context window and native function calling integration, it delivers SoTA performance in visual understanding and reasoning — seamlessly bridging the gap between "visual perception" and "executable action". The GLM-4.6V series provides a unified technical foundation for multimodal agents in real-world business scenarios. Try GLM-4.6V now and level up your multimodal agents with SiliconFlow APIs.

We are thrilled to announce GLM-4.6V, Z.ai's latest multimodal foundation model designed for cloud and enterprise-grade scenarios, is now available on SiliconFlow. It integrates native multimodal function calling capability and excels in long-context visual reasoning, directly closing the loop from perception to understanding to execution.

Now, through SiliconFlow's GLM-4.6V API, you can expect:

• Budget-friendly Pricing: GLM-4.6V $0.30/M tokens (input) and $0.90/M tokens (output)

• 131K Context Window: Enables processing lengthy industry reports, extensive slide decks, or long-form video content

• Seamless Integration: Instantly deploy via SiliconFlow's OpenAI-compatible API, or plug into your existing agentic frameworks, automation tools, or workflows.

Whether you are building agents, workflows, or tools for:

• Rich-Text Content Creation: Convert papers, reports, and slides into polished posts for social media and knowledge bases

• Design-to-Code Automation: Upload screenshots/designs for pixel-level HTML/CSS/JS code generation

• Business Document Processing: Process reports to extract metrics and synthesize comparative tables

• Video Content Operations: Summarize, tag, and extract insights at scale

Through SiliconFlow's production-ready API, you can leverage GLM-4.6V to power your multimodal agents in minutes — no cost concerns, no engineering overhead.

Let's dive into the key capabilities with live demos from the SiliconFlow Platform.

Key Features & Benchmark Performance

In most LLM pipelines, tool calling is still text-only: even for image or document tasks, everything must be converted into text first, then back again. This process potentially leads to information loss and increases system complexity. GLM-4.6V changes this with native multimodal tool calling capability:

• Multimodal Input: Images, UI screenshots, and document pages can be passed directly as tool arguments, avoiding manual text conversion and preserving layout and visual cues.

• Multimodal Output: The model can directly interpret tool results such as search pages, charts, rendered web screenshots, or product images, and feed them back into its reasoning and final response.

By closing the loop from perception → understanding → execution, GLM-4.6V supports the following key features:

• Rich-Text Content Understanding and Creation: Accurately understands complex text, charts, tables, and formulas, then autonomously invokes visual tools to crop key visuals during generation, and audits image quality to compose publication-ready content perfect for social media & knowledge bases.

• Visual Web Search: Recognizes search intent and autonomously triggers appropriate search tools, then comprehends and aligns the mixed visual-textual results to identify relevant information, and finally performs reasoning to deliver structured, visually-rich answers.

• Frontend Replication & Visual Interaction: Achieves pixel-level replication by identifying layouts, components, and color schemes from screenshots to generate high-fidelity HTML/CSS/JS code, then lets you refine it interactively—just circle an element and tell it what you want, like "make this button bigger and change it to green."

• Long-Context Understanding: Processes ~150 pages of documents, 200 slides, or a one-hour video in a single pass with its 131K context window, enabling tasks like analyzing financial reports or summarizing an entire football match while pinpointing specific goal events and timestamps.

For example, when uploading two financial reports filled with numbers, tables and charts, GLM-4.6V shows outstanding visual understanding and reasoning performance. It really understood the tables and charts, reasoned over the numbers, and surfaced actionable insights on revenue growth, profitability, and market positioning.

SiliconFlow Playground supports text & image inputs. Use API service for other input types.

GLM-4.6V has also been evaluated across 20+ mainstream multimodal benchmarks including MMBench, MathVista, and OCRBench, achieving SoTA performance among open-source models. It matches or outperforms comparable-scale models like Qwen3-VL-235B, Kimi-VL-A3B-Thinking-2506, and Step3-321B in key capabilities: multimodal understanding, multimodal agentic tasks, and long-context processing.

Techniques

GLM-4.6V sets the technical foundation for multimodal agents in real-world business scenarios. To achieve this performance, GLM-4.6V introduces a comprehensive suite of innovations:

• Model architecture & long-sequence modeling: GLM-4.6V is continually pre-trained on long-context image–text data, with visual–language compression alignment (inspired by Glyph) to better couple visual encoding with linguistic semantics.

• Multimodal world knowledge: A billion-scale multimodal perception and world-knowledge corpus was introduced to enhance both basic visual understanding and the accuracy and completeness of cross-modal QA.

• Agentic data & MCP extensions: Through large-scale synthetic agentic training, GLM-4.6V extends Model Context Protocol (MCP) with URL-based multimodal handling and end-to-end interleaved text–image output using a “Draft → Image Selection → Final Polish” workflow.

• RL for multimodal agents: Tool-calling behaviors are integrated into a unified RL objective, and a visual feedback loop (building on UI2Code^N) lets the model use rendered results to self-correct its code and actions, pushing toward self-improving multimodal agents.

Get Started Immediately

1. Explore: Try GLM-4.6V in the SiliconFlow playground.

2. Integrate: Use our OpenAI-compatible API. Explore the full API specifications in the SiliconFlow API documentation.

import requests

url = "https://api.siliconflow.com/v1/chat/completions"

payload = {
    "model": "zai-org/GLM-4.6V",
    "messages": [
        {
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "detail": "auto",
                        "url": "https://tse4.mm.bing.net/th/id/OIP.mDDGH4uc_a7tmLFLJvKXrQHaEo?rs=1&pid=ImgDetMain&o=7&rm=3"
                    }
                },
                {
                    "type": "text",
                    "text": "What is in the picture?"
                }
            ],
            "role": "user"
        }
    ],
    "stream": True,
    "temperature": 1
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.request("POST", url, json=payload, headers=headers)

print(response.text)

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