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Documentation Index

Fetch the complete documentation index at: https://docs.siliconflow.com/llms.txt

Use this file to discover all available pages before exploring further.

Continue is an open-source AI code assistant for VS Code and JetBrains that keeps you in flow while you code: Ask questions about your codebase with Chat, make targeted edits without switching context with Edit, get inline suggestions as you type with Autocomplete, and hand off complex development tasks to Agent — all without leaving your IDE. By integrating SiliconFlow APIs into Continue, you can get access to 200+ open-source models, including frontier models like DeepSeek V4 series, GLM-5.1, and Kimi K2.6 — with cache support for faster and more cost-efficient inference. All accessible through one single API key. This guide will walk you through connecting SiliconFlow to Continue in VS Code, from obtaining your API key to configuring chat models, setting up Tab autocomplete, and verifying everything works end-to-end.

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Step 1: Prerequisites

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Get Your SiliconFlow API Key

Before you begin, ensure you have a valid SiliconFlow account:
  • Register a SiliconFlow account at https://cloud.siliconflow.com/. You can sign up with Google or GitHub.
  • Log in and navigate to API Keys in the dashboard.
  • Click 🔑Create API Key and add a name to your key to help you identify it later, then confirm to create it.
  • Click the API key to copy it automatically.
⚠️ Note: Keep your API key secure; you will need it for the configuration in Step 3.
Continue1

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Step 2: Install Continue

Open VS Code, click the Extensions icon in the left Activity Bar (Ctrl+Shift+X / Cmd+Shift+X), search for “Continue”, and click Install.
💡 You can also open the extension page directly from the Visual Studio Marketplace, clicking Install there will redirect you to VS Code, where you’ll complete the installation the same way.
Continue2

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Step 3: Configure SiliconFlow APIs

Once Continue is installed, open the configuration file to set up your SiliconFlow models:
  1. Click the Continue icon in the Activity Bar on the left
  2. Click the settings icon in the top right corner of the Continue panel
  3. In the left sidebar of settings, select Config
  4. Click the settings icon next to Local Config and your config.yaml will open automatically
Continue3
Contiue4

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Configure your chat models

Add your SiliconFlow models to the models list in config.yaml. You can configure multiple models and API keys in a single file. The example below sets up four models: deepseek-ai/DeepSeek-V4-Flash, deepseek-ai/DeepSeek-V4-Pro, zai-org/GLM-5.1, moonshotai/Kimi-K2.6. 
name: Local Config
version: 1.0.0
schema: v1
models:
  - name: deepseek-ai/DeepSeek-V4-Flash
    provider: siliconflow
    model: deepseek-ai/DeepSeek-V4-Flash
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"
  - name: deepseek-ai/DeepSeek-V4-Pro
    provider: siliconflow
    model: deepseek-ai/DeepSeek-V4-Pro
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"
  - name: zai-org/GLM-5.1
    provider: siliconflow
    model: zai-org/GLM-5.1
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"
  - name: moonshotai/Kimi-K2.6
    provider: siliconflow
    model: moonshotai/Kimi-K2.6
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"

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Not sure which model to pick?

ModelBest forKey highlights
DeepSeek-V4-FlashFast, cost-effective coding chat / large codebase1M context · 3 reasoning modes · best value in the V4 series
DeepSeek-V4-ProComplex reasoning / large codebase1M context · #1 open-source on math, STEM & competitive coding · approaches Opus 4.6
GLM-5.1Long-horizon agentic tasks58.4 on SWE-Bench Pro · long-horizon execution · iterative self-improvement
Kimi-K2.6Long-horizon tasks / Frontend generation / multi-agentAgent swarm architecture · long-horizon coding · prompt-to-frontend generation

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Cache More, Spend Less

All four models above are hosted on SiliconFlow with context caching enabled. SiliconFlow consistently delivers high cache hit rates across models — for example, according to OpenRouter’s provider performance data, SiliconFlow achieves the highest cache hit rate among all GLM-5.1 providers at 88.6%, significantly ahead of the competition. For you as a developer, this translates directly to faster response times and lower inference costs on repeated contexts. Looking for more models? Browse the full model library at SiliconFlow Models or stay up to date with the latest releases on the SiliconFlow Blog.
Continue5

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Configure Tab Autocomplete

To enable GitHub Copilot-style Tab completion, add the autocomplete model to your models list with roles: - autocomplete. We recommend Qwen/Qwen3-Coder-30B-A3B-Instruct for its fast response time — as a MoE model that activates only 3B parameters per token, it delivers low-latency completions without sacrificing code quality. 
  - name: Qwen/Qwen3-Coder-30B-A3B-Instruct
    provider: openai
    model: Qwen/Qwen3-Coder-30B-A3B-Instruct
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"
    roles:
      - autocomplete
    autocompleteOptions:
      debounceDelay: 250
      maxPromptTokens: 1024
Note: Avoid using large thinking-mode models for autocomplete — they generate more slowly and are not suited for real-time completion scenarios.

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Complete config.yaml Example

To get started quickly, replace your existing config.yaml with the full template below.
Note: Replace all instances of YOUR_SILICONFLOW_API_KEY with your actual API key before saving. 
name: Local Config
version: 1.0.0
schema: v1
models:
  - name: deepseek-ai/DeepSeek-V4-Flash
    provider: siliconflow
    model: deepseek-ai/DeepSeek-V4-Flash
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"
  - name: deepseek-ai/DeepSeek-V4-Pro
    provider: siliconflow
    model: deepseek-ai/DeepSeek-V4-Pro
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"
  - name: zai-org/GLM-5.1
    provider: siliconflow
    model: zai-org/GLM-5.1
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"
  - name: moonshotai/Kimi-K2.6
    provider: siliconflow
    model: moonshotai/Kimi-K2.6
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"
  - name: Qwen/Qwen3-Coder-30B-A3B-Instruct
    provider: openai
    model: Qwen/Qwen3-Coder-30B-A3B-Instruct
    apiKey: "YOUR_SILICONFLOW_API_KEY"
    apiBase: "https://api.siliconflow.com/v1"
    roles:
      - autocomplete
    autocompleteOptions:
      debounceDelay: 250
      maxPromptTokens: 1024

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Step 4: Verify Your Setup

  1. Save and reload
Save config.yaml. Continue reloads configuration automatically in most cases. If changes don’t take effect, restart your IDE.
  1. Test chat
Open the Continue chat panel. All models defined in your config will appear in the model selector — pick any one, send a test message such as "Hello", and confirm you receive a response.
  1. Test autocomplete
Open any code file (e.g., a Python file), type a snippet likedef quick_sort(arr):, and pause for a moment. If a grey inline suggestion appears after your cursor, Tab autocomplete is working correctly.

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Already Using OpenRouter?

If you already have an OpenRouter account, you can connect your SiliconFlow API key via BYOK. Once connected:
  • Requests draw from your SiliconFlow balance first
  • Billing and rate limits stay in your SiliconFlow account
  • OpenRouter’s fallback routing still works to improve reliability
Bonus: OpenRouter waives platform fees on your first 1M BYOK requests per month.
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To set it up in Continue with OpenRouter API Keys:
  1. Open the Continue panel and click the settings icon in the top right corner
  2. In the left sidebar, select Models
  3. Click the + icon next to Models
  4. Select OpenRouter as the provider
  5. Choose your desired model and enter your OpenRouter API key
Continue7
Continue8

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Resources

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Continue

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OpenRouter

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SiliconFlow