GLM-4.6 Now on SiliconFlow: Advanced Agentic, Reasoning and Coding Capabilities

TL;DR: GLM-4.6 is now live on SiliconFlow! This upgrade expands the context window to 205K tokens, delivers stronger coding and reasoning performance, and excels in agentic tasks with tools like Claude Code, Cline, Roo Code, and Kilo Code. Also, with improved writing quality and better human alignment, GLM-4.6 is a powerful all-rounder for developers and advanced AI applications. Try the GLM-4.6 API on SiliconFlow today!

SiliconFlow is excited to bring you GLM-4.6, the latest version of Z.ai's flagship model. Building on the strong foundation of GLM-4.5, this update focuses on long-context reasoning, agent integration, and coding efficiency, offering smoother performance for complex workflows and intelligent applications. Whether you're building intelligent agents, developing complex applications, or analyzing long-form content, GLM-4.6 delivers the reasoning depth, context length, and reliability to power your most demanding workflows.

Through SiliconFlow's GLM-4.6 API, you can expect:

• Cost-effective Pricing: GLM-4.6 $0.5/M tokens (input) and $1.9/M tokens (output).

• 205K Context Window: Perfect for long documents, complex reasoning, and extended agentic tasks.

• Proven Real-World Performance: Across 74 coding tasks in Claude Code, GLM-4.6 outperformed Claude Sonnet 4 in practical performance.

Key Features & Benchmark Performance

Compared with GLM-4.5, the latest version of Z.ai's flagship model GLM-4.6 brings several key improvements:

• Longer context window: The context window has been expanded from 131K to 205K tokens, enabling the model to handle more complex agentic tasks.

• Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code, Cline, Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.

• Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.

• More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.

• Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.

Also, GLM-4.6 was evaluated across eight public benchmarks covering reasoning, coding, and agentic intelligence. Results show clear and consistent improvements over GLM-4.5 — and competitive advantages compared with top-tier models like DeepSeek-V3.1-Terminus and Claude Sonnet 4.

Beyond benchmarks, real-world experience matters more. GLM-4.6 was evaluated through an extended CC-Bench, where human evaluators completed multi-turn tasks in isolated Docker environments covering front-end development, tool building, data analysis, testing, and algorithm.

The model achieves near-parity with Claude Sonnet 4 (48.6% win rate), clearly outperforming other open-source baselines, while using ~15% fewer tokens than GLM-4.5 — demonstrating improvements in both capability and efficiency.

Use SiliconFlow's GLM-4.6 API

First, let's see how GLM-4.6 performs on SiliconFlow, we used the GLM-4.6 API in Claude Code to complete the following task:

“Build a responsive web page for a ‘Daily AI News’ dashboard. The page should include a top navigation bar, a main card showing today’s news title and summary, and a dark/light theme toggle. Use only HTML, CSS, and vanilla JavaScript.”

Under the same prompt, GLM-4.6 generated a more polished and interactive web page — the navigation buttons trigger functional pop-up windows, and the overall layout appears more visually refined. In contrast, GLM-4.5 produced a simpler, static page with limited interactivity and no working pop-ups.

What GLM-4.6 Generated:

What GLM-4.5 Generated:

Now let's explore multiple ways to leverage this powerful model.

Claude Code

Now, you can easily integrate SiliconFlow's GLM-4.6 API into Claude Code.

Step 1: Get Your SiliconFlow API Key

1. 1. Log in to your SiliconFlow dashboard.

2. 2. Navigate to API Keys section.

3. 3. Generate a new API key for GLM-4.6 access.

4. 4. Copy and secure your API key.

Step 2: Configure Environment Variables

Open your terminal and set the following environment variables:

export ANTHROPIC_BASE_URL="https://api.siliconflow.com/v1/chat/completions"export ANTHROPIC_MODEL="zai-org/GLM-4.6"  # You can modify this to use other models as neededexport ANTHROPIC_API_KEY="YOUR_SILICONFLOW_API_KEY" # Please replace with your actual API Key

Step 3: Start Using Claude Code with GLM-4.6

Navigate to your project directory and launch Claude Code:

cd

Claude Code will now use GLM-4.6 via SiliconFlow's API service for all your coding assistance needs!

What's more, you can also access SiliconFlow's GLM-4.6 model through gen-cli and Cline.

Gen-CLI

Gen-CLI is based on the open-source Gemini-CLI and is now available on GitHub. Install using the following steps:

1. 1. Ensure your system has Node.js 18+ installed.

2. 2. Set the API key environment variable:

export SILICONFLOW_API_KEY="YOUR_API_KEY"

1. 3. Run Gen-CLI:

Via npx:

npx https://github.com/gen-cli/gen-cli

Or install via npm:

npm install -g @gen-cli/gen-cli
gen

Get Started Immediately

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

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

import requestsurl = "https://api.siliconflow.com/v1/chat/completions"payload = {    "model": "zai-org/GLM-4.6",    "messages": [        {            "role": "user",            "content": "tell me a story"        }    ]}headers = {    "Authorization": "Bearer <token>",    "Content-Type": "application/json"}response = requests.request("POST", url, json=payload, headers=headers)print(response.text)

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