docs: Add 5 new provider guides and update model selection guide (#5716)

Co-authored-by: pashpashpash <[email protected]>

docs/docs.json

@@ -57,13 +57,18 @@
 			{
 				"group": "Getting Started",
 				"pages": [
-					"getting-started/for-new-coders",
-					"getting-started/installing-cline",
-					"getting-started/installing-dev-essentials",
+					"getting-started/what-is-cline",
 					"getting-started/model-selection-guide",
+					"getting-started/installing-cline",
 					"getting-started/task-management",
 					"getting-started/understanding-context-management",
-					"getting-started/what-is-cline"
+					{
+						"group": "For New Coders",
+						"pages": [
+							"getting-started/for-new-coders",
+							"getting-started/installing-dev-essentials"
+						]
+					}
 				]
 			},
 			{
@@ -166,7 +171,11 @@
 					"provider-config/xai-grok",
 					"provider-config/mistral-ai",
 					"provider-config/deepseek",
-					"provider-config/fireworks-ai",
+					"provider-config/groq",
+					"provider-config/cerebras",
+					"provider-config/doubao",
+					"provider-config/fireworks",
+					"provider-config/zai",
 					"provider-config/ollama",
 					"provider-config/openai",
 					"provider-config/openai-compatible",
@@ -206,6 +215,13 @@
 			"discord": "https://discord.gg/cline"
 		}
 	},
+	"anchors": [
+		{
+			"name": "What is Cline",
+			"icon": "house",
+			"url": "getting-started/what-is-cline"
+		}
+	],
 	"search": {
 		"prompt": "Search Cline documentation..."
 	},

docs/features/auto-compact.mdx

@@ -5,6 +5,13 @@ sidebarTitle: "Auto Compact"
 
 When your conversation approaches the model's context window limit, Cline automatically summarizes it to free up space and keep working. 
 
+<Frame>
+	<img
+		src="https://storage.googleapis.com/cline_public_images/docs/assets/condensing.png"
+		alt="Auto-compact feature condensing conversation context"
+	/>
+</Frame>
+
 ## How It Works
 
 Cline monitors token usage during your conversation. When you're getting close to the limit, he:
@@ -65,4 +72,4 @@ Auto Compact uses advanced LLM-based summarization which we've found works signi
 
 <Note>
 When using other models, Cline automatically falls back to the standard rule-based context truncation method, even if Auto Compact is enabled in settings.
-</Note>
+</Note>

docs/features/drag-and-drop.mdx

@@ -5,10 +5,28 @@ sidebarTitle: "Drag & Drop"
 
 Dragging and dropping files into Cline is a quick way to add images, code, and other files to your conversations.
 
+<Frame>
+	<img
+		src="https://storage.googleapis.com/cline_public_images/docs/assets/dran-n-drop.gif"
+		alt="Dragging and dropping files into Cline chat"
+	/>
+</Frame>
+
 <Note>Due to VS Code quirks, to drag and drop files into the Cline chat input, you need to hold `Shift` while dragging.</Note>
 
 Dragging and dropping workspace files into Cline will automatically create a [file mention](/features/at-mentions/file-mentions). This allows you to reference the file in your conversation without needing to type out the path.
 
+### Dragging from Finder/File Explorer
+
+You can drag files directly from your system's file manager into Cline:
+
+<Frame>
+	<img
+		src="https://storage.googleapis.com/cline_public_images/docs/assets/drag-n-drop-finder.gif"
+		alt="Dragging files from Finder into Cline"
+	/>
+</Frame>
+
 ### Supported File Types
 
 Cline supports dragging external images, pdfs, csv, excel, and other text files from your file system, as well as files from your workspace.

docs/features/focus-chain.mdx

@@ -5,6 +5,13 @@ sidebarTitle: "Focus Chain"
 
 Focus Chain is a task management enhancement feature in Cline that provides automatic todo list management with real-time progress tracking throughout your tasks. 
 
+<Frame>
+	<img
+		src="https://storage.googleapis.com/cline_public_images/docs/assets/2dos.gif"
+		alt="Focus Chain todo list management with real-time progress tracking"
+	/>
+</Frame>
+
 This enables Cline to work on long-horizon tasks, seamlessly managing the context sent to LLMs, and keeping Cline on track across many context window resets.
 
 <Tip>

docs/features/slash-commands/deep-planning.mdx

@@ -5,6 +5,13 @@ sidebarTitle: "/deep-planning"
 
 `/deep-planning` transforms Cline into a meticulous architect who investigates your codebase, asks clarifying questions, and creates a comprehensive implementation plan before writing a single line of code. 
 
+<Frame>
+	<img
+		src="https://storage.googleapis.com/cline_public_images/docs/assets/deep-planning.png"
+		alt="Deep Planning command in action showing investigation and planning process"
+	/>
+</Frame>
+
 When you use `/deep-planning`, Cline follows a four-step process that mirrors how senior developers approach complex features: thorough investigation, discussion & clarification of requirements, detailed planning, and structured task creation with progress tracking.
 
 ## The Four-Step Process

docs/getting-started/for-new-coders.mdx

@@ -3,9 +3,9 @@ title: "For New Coders"
 description: "Welcome to Cline, your AI-powered coding companion! This guide will help you quickly set up your development environment and begin your coding journey with ease."
 ---
 
-> 💡 **Tip:** If you're completely new to coding, take your time with each step. There's no rush — Cline is here to guide you!
+> **Tip:** If you're completely new to coding, take your time with each step. There's no rush — Cline is here to guide you!
 
-### 🚀 Getting Started
+### Getting Started
 
 Before you jump into coding, make sure you have these essentials ready:
 
@@ -15,9 +15,9 @@ A popular, free, and powerful code editor.
 
 -   [<u>Download VS Code</u>](https://code.visualstudio.com/)
 
-📺 **Recommended YouTube Tutorial:** [<u>How to Install VS Code</u>](https://www.youtube.com/watch?v=MlIzFUI1QGA)
+**Recommended YouTube Tutorial:** [<u>How to Install VS Code</u>](https://www.youtube.com/watch?v=MlIzFUI1QGA)
 
-> ✅ **Pro Tip:** Install VS Code in your Applications folder (macOS) or Program Files (Windows) for easy access from your dock or start menu.
+> **Pro Tip:** Install VS Code in your Applications folder (macOS) or Program Files (Windows) for easy access from your dock or start menu.
 
 #### 2. **Organize Your Projects**
 
@@ -31,7 +31,7 @@ Inside your `Cline` folder, structure projects clearly:
 -   `Documents/Cline/workout-app` _(e.g., for a fitness tracking app)_
 -   `Documents/Cline/portfolio-website` _(e.g., to showcase your work)_
 
-> 💡 **Tip:** Keeping your projects organized from the start will save you time and confusion later!
+> **Tip:** Keeping your projects organized from the start will save you time and confusion later!
 
 #### 3. **Install the Cline VS Code Extension**
 
@@ -39,9 +39,9 @@ Enhance your coding workflow by installing the Cline extension directly within V
 
 -   Get Started with Cline Extension Tutorial
 
-📺 **Recommended YouTube Tutorial:** [<u>How To Install Extensions in VS Code</u>](https://www.youtube.com/watch?v=E7trgwZa-mk)
+**Recommended YouTube Tutorial:** [<u>How To Install Extensions in VS Code</u>](https://www.youtube.com/watch?v=E7trgwZa-mk)
 
-> ✅ **Pro Tip:** After installing, reload VS Code to ensure the extension is activated properly.
+> **Pro Tip:** After installing, reload VS Code to ensure the extension is activated properly.
 
 #### 4. **Essential Development Tools**
 
@@ -51,9 +51,9 @@ Basic software required for coding efficiently:
 -   Node.js
 -   Git
 
-👉 [<u>Follow our detailed guide on Installing Essential Development Tools with step-by-step help from Cline.</u>](https://docs.cline.bot/getting-started/installing-dev-essentials#installing-dev-essentials)
+[<u>Follow our detailed guide on Installing Essential Development Tools with step-by-step help from Cline.</u>](https://docs.cline.bot/getting-started/installing-dev-essentials#installing-dev-essentials)
 
-📺 **Recommended YouTube Tutorials for Manual Installation:**
+**Recommended YouTube Tutorials for Manual Installation:**
 
 -   **For macOS:**
     -   [<u>Install Homebrew on Mac</u>](https://www.youtube.com/watch?v=hwGNgVbqasc)
@@ -63,6 +63,6 @@ Basic software required for coding efficiently:
     -   [<u>Install Git on Windows 10/11 (2024)</u>](https://www.youtube.com/watch?v=yjxv1HuRQy0)
     -   [<u>Install Node.js in Windows 10/11</u>](https://www.youtube.com/watch?v=uCgAuOYpJd0)
 
-> ⚠️ **Note:** If you run into permission issues during installation, try running your terminal or command prompt as an administrator.
+> **Note:** If you run into permission issues during installation, try running your terminal or command prompt as an administrator.
 
-🎉 You're all set! Dive in and start coding smarter and faster with **Cline**.
+You're all set! Dive in and start coding smarter and faster with **Cline**.

docs/getting-started/installing-cline.mdx

@@ -9,13 +9,13 @@ description: "Cline is a VS Code extension that brings AI-powered coding assista
 -   **VS Code Marketplace (Recommended):** Fastest method for standard VS Code and Cursor users.
 -   **Open VSX Registry:** For VS Code-compatible editors like VSCodium.
 
-### 🛠️ VS Code Marketplace: Step-by-Step Setup
+### VS Code Marketplace: Step-by-Step Setup
 
 Follow these steps to get Cline up and running:
 
 1.  **Open VS Code:** Launch the VS Code application.
 
-    > ⚠️ **Note:** If VS Code shows "Running extensions might...", click "Allow".
+    > **Note:** If VS Code shows "Running extensions might...", click "Allow".
 
 2.  **Open Your Cline Folder:** In VS Code, open the Cline folder you created in Documents.
 3.  **Navigate to Extensions:** Click on the Extensions icon in the Activity Bar on the side of VS Code (`Ctrl + Shift + X` or `Cmd + Shift + X`).
@@ -34,9 +34,9 @@ Follow these steps to get Cline up and running:
     - Or, use the command palette (`Ctrl/Cmd + Shift + P`) and type "Cline: Open In New Tab" for a better view.
 3. **Troubleshooting:** If you don't see the Cline icon, try restarting VS Code.
 
-> ✅ **Pro Tip:** You should see the Cline chat window appear in your VS Code editor!
+> **Pro Tip:** You should see the Cline chat window appear in your VS Code editor!
 
-### 🌐 Open VSX Registry
+### Open VSX Registry
 
 For VS Code-compatible editors without Marketplace access (like VSCodium and Windsurf):
 
@@ -46,7 +46,7 @@ For VS Code-compatible editors without Marketplace access (like VSCodium and Win
 4. Select "Cline" by saoudrizwan and click **Install**.
 5. Reload if prompted.
 
-### 👤 Creating Your Cline Account
+### Creating Your Cline Account
 
 Now that you have Cline installed, let's get you set up with your account:
 
@@ -61,7 +61,7 @@ Now that you have Cline installed, let's get you set up with your account:
     - Google Gemini 2.0 Flash
     - And more — all through your Cline account.
 
-### 💻 Your First Interaction with Cline
+### Your First Interaction with Cline
 
 You're ready to start building! Copy and paste this prompt into the Cline chat window:
 
@@ -69,15 +69,15 @@ You're ready to start building! Copy and paste this prompt into the Cline chat w
 Hey Cline! Could you help me create a new project folder called "hello-world" in my Cline directory and make a simple webpage that says "Hello World" in big blue text?
 ```
 
-> ✅ **Pro Tip:** Cline will help you create the project folder and set up your first webpage!
+> **Pro Tip:** Cline will help you create the project folder and set up your first webpage!
 
-### 🧩 Tips for Working with Cline
+### Tips for Working with Cline
 
 -   **Ask Questions:** If you're unsure about something, ask Cline!
 -   **Use Screenshots:** Cline can understand images — show him what you're working on.
 -   **Copy and Paste Errors:** Share error messages in the chat for solutions.
 -   **Speak Plainly:** Use your own words — Cline will translate them into code.
 
-### 🫂 Still Struggling?
+### Still Struggling?
 
 Join our Discord community and engage with our team and other Cline users directly.

docs/getting-started/installing-dev-essentials.mdx

@@ -6,7 +6,7 @@ description: >-
     guided way.
 ---
 
-### 🧰 The Essential Tools
+### The Essential Tools
 
 Here are the core tools you'll need for development:
 
@@ -17,9 +17,9 @@ Here are the core tools you'll need for development:
     -   Chocolatey for Windows
     -   apt/yum for Linux
 
-> 💡 **Tip:** These tools are the foundation of your developer toolkit. Installing them properly will set you up for success!
+> **Tip:** These tools are the foundation of your developer toolkit. Installing them properly will set you up for success!
 
-### 🚀 Let Cline Install Everything
+### Let Cline Install Everything
 
 Copy one of these prompts based on your operating system and paste it into **Cline**:
 
@@ -41,9 +41,9 @@ Hello Cline! I need help setting up my Windows PC for software development. Coul
 Hello Cline! I need help setting up my Linux system for software development. Could you please help me install the essential development tools like Node.js, Git, and any other core utilities that are commonly needed for coding? I'd like you to guide me through the process step-by-step.
 ```
 
-> ✅ **Pro Tip:** Cline will show you each command before running it. You stay in control the entire time!
+> **Pro Tip:** Cline will show you each command before running it. You stay in control the entire time!
 
-### 🔍 What Will Happen
+### What Will Happen
 
 Cline will guide you through the following steps:
 
@@ -52,9 +52,9 @@ Cline will guide you through the following steps:
 3. Showing you the exact command before it runs (you approve each step!)
 4. Verifying each installation is successful
 
-> ⚠️ **Note:** You might need to enter your computer's password for some installations. This is normal!
+> **Note:** You might need to enter your computer's password for some installations. This is normal!
 
-### 💡 Why These Tools Are Important
+### Why These Tools Are Important
 
 -   **Node.js & npm:**
     -   Build websites with frameworks like React or Next.js
@@ -68,15 +68,15 @@ Cline will guide you through the following steps:
     -   Quickly install and update development tools
     -   Keep your environment organized and up to date
 
-### 🧩 Notes
+### Notes
 
-> 💡 **Tip:** The installation process is interactive — Cline will guide you step by step!
+> **Tip:** The installation process is interactive — Cline will guide you step by step!
 
 -   All commands are shown to you for approval before they run.
 -   If you run into any issues, Cline will help troubleshoot them.
 -   You may need to enter your computer's password for certain steps.
 
-### 🧑‍💻 Additional Tips for New Coders
+### Additional Tips for New Coders
 
 #### Understanding the Terminal
 

docs/getting-started/model-selection-guide.mdx

@@ -1,126 +1,79 @@
 ---
 title: "Model Selection Guide"
-description: "Last updated: Feb 5, 2025."
+description: "Last updated: August 20, 2025."
 ---
 
-## Understanding Context Windows
-
-Think of a context window as your AI assistant's working memory - similar to RAM in a computer. It determines how much information the model can "remember" and process at once during your conversation. This includes:
-
--   Your code files and conversations
--   The assistant's responses
--   Any documentation or additional context provided
-
-Context windows are measured in tokens (roughly 3/4 of a word in English). Different models have different context window sizes:
-
--   Claude 3.5 Sonnet: 200K tokens
--   DeepSeek Models: 128K tokens
--   Gemini Flash 2.0: 1M tokens
--   Gemini 1.5 Pro: 2M tokens
-
-When you reach the limit of your context window, older information needs to be removed to make room for new information - just like clearing RAM to run new programs. This is why sometimes AI assistants might seem to "forget" earlier parts of your conversation.
-
-Cline helps you manage this limitation with its Context Window Progress Bar, which shows:
-
--   Input tokens (what you've sent to the model)
--   Output tokens (what the model has generated)
--   A visual representation of how much of your context window you've used
--   The total capacity for your chosen model
-
-<Frame caption="Visual representation of the context window usage in Cline">
-	<img
-		src="https://storage.googleapis.com/cline_public_images/docs/assets/image%20(11).png"
-		alt="Context window progress bar example"
-	/>
-</Frame>
-
-This visibility helps you work more effectively with Cline by letting you know when you might need to start fresh or break tasks into smaller chunks.
-
-### Model Comparison
-
-## LLM Model Comparison for Cline (Feb 2025)
-
-| Model             | Input Cost\* | Output Cost\* | Context Window | Best For                            |
-| ----------------- | ------------ | ------------- | -------------- | ----------------------------------- |
-| Claude 3.5 Sonnet | $3.00        | $15.00        | 200K           | Best code implementation & tool use |
-| DeepSeek R1       | $0.55        | $2.19         | 128K           | Planning & reasoning champion       |
-| DeepSeek V3       | $0.14        | $0.28         | 128K           | Value code implementation           |
-| o3-mini           | $1.10        | $4.40         | 200K           | Flexible use, strong planning       |
-| Gemini Flash 2.0  | $0.00        | $0.00         | 1M             | Strong all-rounder                  |
-| Gemini 1.5 Pro    | $0.00        | $0.00         | 2M             | Large context processing            |
-
-\*Costs per million tokens
-
-### Top Picks for 2025
-
-1. **Claude 3.5 Sonnet**
-    - Best overall code implementation
-    - Most reliable tool usage
-    - Expensive but worth it for critical code
-2. **DeepSeek R1**
-    - Exceptional planning & reasoning
-    - Great value pricing
-3. **o3-mini**
-    - Strong for planning with adjustable reasoning
-    - Three reasoning modes for different needs
-    - Requires OpenAI Tier 3 API access
-    - 200K context window
-4. **DeepSeek V3**
-    - Reliable code implementation
-    - Great for daily coding
-    - Cost-effective for implementation
-5. **Gemini Flash 2.0**
-    - Massive 1M context window
-    - Improved speed and performance
-    - Good all-around capabilities
-
-### Best Models by Mode (Plan or Act)
-
-#### Planning
-
-1. **DeepSeek R1**
-    - Best reasoning capabilities in class
-    - Excellent at breaking down complex tasks
-    - Strong math/algorithm planning
-    - MoE architecture helps with reasoning
-2. **o3-mini (high reasoning)**
-    - Three reasoning levels:
-        - High: Complex planning
-        - Medium: Daily tasks
-        - Low: Quick ideas
-    - 200K context helps with large projects
-3. **Gemini Flash 2.0**
-    - Massive context window for complex planning
-    - Strong reasoning capabilities
-    - Good with multi-step tasks
-
-#### Acting (coding)
-
-1. **Claude 3.5 Sonnet**
-    - Best code quality
-    - Most reliable with Cline tools
-    - Worth the premium for critical code
-2. **DeepSeek V3**
-    - Nearly Sonnet-level code quality
-    - Better API stability than R1
-    - Great for daily coding
-    - Strong tool usage
-3. **Gemini 1.5 Pro**
-    - 2M context window
-    - Good with complex codebases
-    - Reliable API
-    - Strong multi-file understanding
-
-### A Note on Local Models
-
-While running models locally might seem appealing for cost savings, we currently don't recommend any local models for use with Cline. [Local models are significantly less reliable](https://docs.cline.bot/running-models-locally/read-me-first) at using Cline's essential tools and typically retain only 1-26% of the original model's capabilities. The full cloud version of DeepSeek-R1, for example, is 671B parameters - local versions are drastically simplified copies that struggle with complex tasks and tool usage. Even with high-end hardware (RTX 3070+, 32GB+ RAM), you'll experience slower responses, less reliable tool execution, and reduced capabilities. For the best development experience, we recommend sticking with the cloud models listed above.
-
-### Key Takeaways
-
-1. **Plan vs Act Matters**: Choose models based on task type
-2. **Real Performance > Benchmarks**: Focus on actual Cline performance
-3. **Mix & Match**: Use different models for planning and implementation
-4. **Cost vs Quality**: Premium models worth it for critical code
-5. **Keep Backups**: Have alternatives ready for API issues
-
-_\*Note: Based on real usage patterns and community feedback rather than just benchmarks. Your experience may vary. This is not an exhaustive list of all the models available for use within Cline._
+New models drop constantly, so this guide focuses on what's working well with Cline right now. We'll keep it updated as the landscape shifts.
+
+## Current Top Models
+
+| Model | Context Window | Input Price* | Output Price* | Best For |
+|-------|---------------|--------------|---------------|----------|
+| **Claude Sonnet 4** | 1M tokens | $3-6 | $15-22.50 | Reliable tool usage, complex codebases |
+| **Qwen3 Coder** | 256K tokens | $0.20 | $0.80 | Coding tasks, open source flexibility |
+| **Gemini 2.5 Pro** | 1M+ tokens | TBD | TBD | Large codebases, document analysis |
+| **GPT-5** | 400K tokens | $1.25 | $10 | Latest OpenAI tech, three modes |
+
+*Per million tokens
+
+## Budget Options
+
+| Model | Context Window | Input Price* | Output Price* | Notes |
+|-------|---------------|--------------|---------------|-------|
+| **DeepSeek V3** | 128K tokens | $0.14 | $0.28 | Great value for daily coding |
+| **DeepSeek R1** | 128K tokens | $0.55 | $2.19 | Budget reasoning champion |
+| **Qwen3 32B** | 128K tokens | Varies | Varies | Open source, multiple providers |
+| **Z AI GLM 4.5** | 128K tokens | TBD | TBD | MIT licensed, hybrid reasoning |
+
+*Per million tokens
+
+
+## Context Window Guide
+
+| Size | Word Count | Use Case |
+|------|------------|----------|
+| 32K tokens | ~24,000 words | Single files, small projects |
+| 128K tokens | ~96,000 words | Most coding projects |
+| 200K tokens | ~150,000 words | Large codebases |
+| 400K+ tokens | ~300,000+ words | Entire applications |
+
+**Performance note**: Most models start dropping in quality around 400-500K tokens, even if they claim higher limits.
+
+## Open Source vs Closed Source
+
+### Open Source Advantages
+- **Multiple providers** compete to host them
+- **Cheaper pricing** due to competition  
+- **Provider choice** - switch if one goes down
+- **Faster innovation** cycles
+
+### Open Source Models Available
+- **Qwen3 Coder** (Apache 2.0)
+- **Z AI GLM 4.5** (MIT)
+- **Kimi K2** (Open source)
+- **DeepSeek series** (Various licenses)
+
+## Quick Decision Matrix
+
+| If you want... | Use this |
+|----------------|----------|
+| Something that just works | Claude Sonnet 4 |
+| To save money | DeepSeek V3 or Qwen3 variants |
+| Huge context windows | Gemini 2.5 Pro or Claude Sonnet 4 |
+| Open source | Qwen3 Coder, Z AI GLM 4.5, or Kimi K2 |
+| Latest tech | GPT-5 |
+| Speed | Qwen3 Coder on Cerebras (fastest available) |
+
+## What Others Are Using
+
+Check [OpenRouter's Cline usage stats](https://openrouter.ai/apps?url=https%3A%2F%2Fcline.bot%2F) to see real usage patterns from the community.
+
+## Context Management
+
+Cline automatically handles context limits with [auto-compact](/features/auto-compact). When you approach your model's limit, Cline summarizes the conversation to keep working. You don't need to micromanage this.
+
+## The Bottom Line
+
+Start with **Claude Sonnet 4** if you want reliability. Experiment with **open source options** once you're comfortable to find the best fit for your workflow and budget.
+
+The landscape moves fast - these recommendations reflect what's working now, but keep an eye on new releases.

docs/getting-started/understanding-context-management.mdx

@@ -3,7 +3,7 @@ title: "Context Management"
 description: "Context is key to getting the most out of Cline"
 ---
 
-> 💡 **Quick Reference**
+> **Quick Reference**
 >
 > -   Context = The information Cline knows about your project
 > -   Context Window = How much information Cline can hold at once
@@ -38,7 +38,7 @@ Cline actively builds context in two ways:
     - Guide focus areas
     - Share design thoughts and requirements
 
-💡 **Key Point**: Cline isn't passive - it actively seeks to understand your project. You can either let it explore or guide its focus, especially in [Plan](https://docs.cline.bot/features/plan-and-act) mode.
+**Key Point**: Cline isn't passive - it actively seeks to understand your project. You can either let it explore or guide its focus, especially in [Plan Mode](/features/plan-and-act).
 
 ### Context & Context Windows
 
@@ -53,13 +53,13 @@ Think of context like a whiteboard you and Cline share:
 -   **Context Window** is the size of the whiteboard itself:
     -   Measured in tokens (1 token ≈ 3/4 of an English word)
     -   Each model has a fixed size:
-        -   Claude 3.5 Sonnet: 200,000 tokens
-        -   DeepSeek: 64,000 tokens
+        -   Claude Sonnet 4: 1,000,000 tokens
+        -   Qwen3 Coder: 256,000 tokens
+        -   Gemini 2.5 Pro: 1,000,000+ tokens
+        -   GPT-5: 400,000 tokens
     -   When the whiteboard is full, Cline automatically summarizes the conversation to free up space
-        -   [Learn about Automatic Context Summarization](/features/automatic-context-summarization)
-        -   [How Cline manages context under the hood](https://cline.bot/blog/understanding-the-new-context-window-progress-bar-in-cline)
 
-⚠️ **Important**: Having a large context window (like Claude's 200k tokens) doesn't mean you should fill it completely. Just like a cluttered whiteboard, too much information can make it harder to focus on what's important.
+**Important**: Having a large context window doesn't mean you should fill it completely. Models start degrading around 400-500K tokens even if they claim higher limits. Just like a cluttered whiteboard, too much information can make it harder to focus on what's important.
 
 ## Understanding the Context Window Progress Bar
 
@@ -77,7 +77,7 @@ Cline provides a visual way to monitor your context window usage through a progr
 -   ↑ shows input tokens (what you've sent to the LLM)
 -   ↓ shows output tokens (what the LLM has generated)
 -   The progress bar visualizes how much of your context window you've used
--   The total shows your model's maximum capacity (e.g., 200k for Claude 3.5-Sonnet)
+-   The total shows your model's maximum capacity (e.g., 1M for Claude Sonnet 4)
 
 ### When to Watch the Bar
 
@@ -86,28 +86,33 @@ Cline provides a visual way to monitor your context window usage through a progr
 -   Before starting complex tasks
 -   When Cline seems to lose context
 
-💡 **Tip**: With [Automatic Context Summarization](/features/automatic-context-summarization), Cline can now handle long conversations automatically. When combined with [Focus Chain](/features/focus-chain), you can work on complex projects that span multiple context windows without losing progress.
+**Tip**: With [Auto Compact](/features/auto-compact), Cline can now handle long conversations automatically. When combined with [Focus Chain](/features/focus-chain), you can work on complex projects that span multiple context windows without losing progress.
 
 ## Automatic Context Management
 
-Cline now includes intelligent features to manage context automatically:
+Cline includes intelligent features to manage context automatically:
 
-### Automatic Context Summarization
+### Default Settings You Should Keep On
 
-When your conversation approaches the context window limit, Cline automatically:
-- Creates a comprehensive summary of the conversation
-- Preserves all essential technical details and decisions
-- Seamlessly continues work without interruption
-- Maintains full task continuity
+**Focus Chain** - Enabled by default in v3.25. Cline generates a todo list at task start and keeps it in context so the thread doesn't drift. You can edit the markdown to add or reorder steps and Cline will adapt. [Learn more about Focus Chain](/features/focus-chain).
 
-This means you can work on larger projects without manually managing context. [Learn more about Automatic Context Summarization](/features/automatic-context-summarization).
+**Auto Compact** - Always on. As the context window reaches its limit, Cline creates a comprehensive summary, replaces the bloated history, and continues where it left off. Decisions, code changes, and state are preserved. [Learn more about Auto Compact](/features/auto-compact).
 
-### Focus Chain Integration
+## Advanced Context Tools
 
-When combined with [Focus Chain](/features/focus-chain), Cline can:
-- Maintain todo lists across context resets
-- Track progress through multiple summarizations
-- Keep working on long-horizon tasks without losing direction
+When you need more control over context management:
+
+### Deep Planning (`/deep-planning`)
+For substantial features, refactors, or integrations. Cline investigates your codebase, asks targeted questions, then writes `implementation_plan.md`. It creates a fresh task with distilled, high-value context. [Learn more about Deep Planning](/features/slash-commands/deep-planning).
+
+### New Task (`/newtask`)
+At natural transition points, packages only what matters into a fresh task. Clean slate for implementation after research, or crisp handoff between teammates. [Learn more about New Task](/features/slash-commands/new-task).
+
+### Smol (`/smol`)
+Compress the conversation in place to keep momentum. Ideal during debugging or exploratory work when you don't want to break flow. [Learn more about Smol](/features/slash-commands/smol).
+
+### Memory Bank + .clinerules
+For non-trivial projects. The Memory Bank captures project knowledge as Markdown in your repo. `.clinerules` are version-controlled instructions that align Cline's behavior with your team. [Learn more about Memory Bank](/prompting/cline-memory-bank) and [Cline Rules](/features/cline-rules).
 
 ## Working with Context Files
 
@@ -115,12 +120,12 @@ Context files help maintain understanding across sessions. They serve as documen
 
 #### Approaches to Context Files
 
-1. **Evergreen Project Context (i.e.** [**Memory Bank**](https://docs.cline.bot/prompting/cline-memory-bank)**)**
+1. **Evergreen Project Context (Memory Bank)**
     - Living documentation that evolves with your project
     - Updated as architecture and patterns emerge
     - Example: The Memory Bank pattern maintains files like `techContext.md` and `systemPatterns.md`
     - Useful for long-running projects and teams
-2. **Task-Specific Context (i.e.** [**Structured Approach**](https://cline.bot/blog/building-advanced-software-with-cline-a-structured-approach)**)**
+2. **Task-Specific Context**
 
     - Created for specific implementation tasks
     - Document requirements, constraints, and decisions
@@ -173,9 +178,19 @@ Context files help maintain understanding across sessions. They serve as documen
     - Use Plan mode for complex discussions
     - Start fresh sessions when needed
 3. **Team Projects**
-    - Share common context files (consider using [.clinerules](https://docs.cline.bot/features/cline-rules) files in project roots)
+    - Share common context files (consider using [.clinerules](/features/cline-rules) files in project roots)
     - Document architectural decisions
     - Maintain consistent patterns
     - Keep documentation current
 
-Remember: The goal is to help Cline maintain consistent understanding of your project across sessions.
+## Bonus Context Tips
+
+- You can @ links and have the webpage's context added to Cline (docs, blogs, etc.)
+- Utilize MCP servers to pull in context from your external knowledge bases
+- Screenshots can be used as context for models that support image inputs
+
+## The Bottom Line
+
+Cline already does a lot of context work for you - [Focus Chain](/features/focus-chain), [Auto Compact](/features/auto-compact), and the planning flow are designed to keep the thread intact across long horizons. The goal is to help Cline maintain consistent understanding of your project across sessions.
+
+Remember: The goal is to keep only what matters in view, at every step.

docs/getting-started/what-is-cline.mdx

@@ -3,6 +3,70 @@ title: "What is Cline?"
 description: "An introduction to Cline, your AI-powered development assistant in VS Code."
 ---
 
-Cline is an AI development assistant which integrates with Microsoft Visual Studio Code. It provides an interface between your IDE and LLMs facilitating code development, increasing productivity and lowering the barrier to entry for new coders. Depending on permissions, Cline can read/write files, execute commands, use your web browser, and expand its capabilities with Model Context Protocol servers.
+Cline is an open source AI coding agent that brings frontier AI models directly to your VS Code editor. Unlike autocomplete tools, Cline is a true coding agent that can understand entire codebases, plan complex changes, and execute multi-step tasks.
 
-What makes Cline distinctive is its thoughtful approach to code generation and its extensive integration capabilities. Rather than simply generating code snippets, Cline collaborates with developers by planning solutions step-by-step, maintaining awareness of the entire development environment, and requiring explicit approval for all changes. It can understand large codebases, accelerate onboarding for new engineers, and connect with hundreds of tools through its Model Context Protocol Marketplace, enabling everything from streamlined project deployments to automated incident response—all through natural language commands.
+## Open Source AI Coding, Uncompromised
+
+Cline gives you direct, transparent access to frontier AI with no limits, no surprises, and no model ecosystem lock-in. See every decision. Choose any model. Control your costs.
+
+### Complete Transparency
+
+Watch in real-time as Cline reads files, considers approaches, and proposes changes. Every decision is visible, every edit reviewable before it's made. This isn't just "explainable AI" - it's complete transparency.
+
+### Your Models, Your Control
+
+Use Claude for complex reasoning, Gemini for massive contexts, or Qwen3 Coder for efficiency. Switch instantly as new models launch. Your API keys, your choice. No gatekeeping innovation.
+
+### Built for Real Engineering
+
+Cline can:
+- **Read and write files** across your entire codebase
+- **Execute terminal commands** and debug errors
+- **Plan complex features** before writing code
+- **Connect to external systems** through MCP servers
+- **Understand large codebases** with intelligent context management
+
+## Plan & Act Mode
+
+Cline explores your codebase and works with you to create comprehensive plans before writing a single line of code, ensuring it understands the full context of your project.
+
+**Plan Mode** for complex tasks - Cline explores, asks questions, and creates detailed implementation plans.
+
+**Act Mode** for execution - Cline implements the plan with full transparency and control.
+
+## Zero Trust by Design
+
+Your code never touches our servers. Cline runs entirely client-side with your API keys, making it the only option for enterprises with strict security requirements.
+
+**Open source** means your security team can review every line. See exactly how Cline works, what it sends to AI providers, and how decisions are made.
+
+## Key Features
+
+### Focus Chain
+Automatic todo list management with real-time progress tracking throughout your tasks. Keeps Cline on track across long projects.
+
+### Auto Compact
+When conversations get long, Cline automatically summarizes to preserve context while freeing up space to continue working.
+
+### Deep Planning
+For complex features, Cline investigates your codebase, asks clarifying questions, and creates comprehensive implementation plans.
+
+### MCP Integration
+Connect to databases, APIs, and documentation through the Model Context Protocol. Cline becomes your bridge to any external system.
+
+### .clinerules
+Define project-specific instructions that Cline follows including coding standards, architecture patterns, or team conventions.
+
+## Why Developers Choose Cline
+
+**100% Open Source** - Every line of code on GitHub. 48k+ stars from developers who've read it, improved it, and trust it with their work.
+
+**No Inference Games** - We don't profit from AI usage. While others limit context or route to cheaper models, we give you unrestricted access to any model's full capabilities.
+
+**Future-Proof by Design** - New model released? Use it immediately. Cline works with any AI provider, any model.
+
+**True Visibility** - See every file read, every decision considered, every token used.
+
+## Getting Started
+
+Ready to experience AI coding without limits? [Install Cline](/getting-started/installing-cline) and start with our [Model Selection Guide](/getting-started/model-selection-guide) to choose the right AI model for your needs.

docs/package-lock.json

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docs/provider-config/cerebras.mdx

@@ -0,0 +1,96 @@
+---
+title: "Cerebras"
+description: "Learn how to configure and use Cerebras's ultra-fast inference with Cline. Experience up to 2,600 tokens per second with wafer-scale chip architecture and real-time reasoning models."
+---
+
+Cerebras delivers the world's fastest AI inference through their revolutionary wafer-scale chip architecture. Unlike traditional GPUs that shuttle model weights from external memory, Cerebras stores entire models on-chip, eliminating bandwidth bottlenecks and achieving speeds up to 2,600 tokens per second—often 20x faster than GPUs.
+
+**Website:** [https://cloud.cerebras.ai/](https://cloud.cerebras.ai/)
+
+### Getting an API Key
+
+1.  **Sign Up/Sign In:** Go to [Cerebras Cloud](https://cloud.cerebras.ai/) and create an account or sign in.
+2.  **Navigate to API Keys:** Access the API keys section in your dashboard.
+3.  **Create a Key:** Generate a new API key. Give it a descriptive name (e.g., "Cline").
+4.  **Copy the Key:** Copy the API key immediately. Store it securely.
+
+### Supported Models
+
+Cline supports the following Cerebras models:
+
+-   `qwen-3-coder-480b-free` (Free tier) - High-performance coding model at no cost
+-   `qwen-3-coder-480b` - Flagship 480B parameter coding model
+-   `qwen-3-235b-a22b-instruct-2507` - Advanced instruction-following model
+-   `qwen-3-235b-a22b-thinking-2507` - Reasoning model with step-by-step thinking
+-   `llama-3.3-70b` - Meta's Llama 3.3 model optimized for speed
+-   `qwen-3-32b` - Compact yet powerful model for general tasks
+
+### Configuration in Cline
+
+1.  **Open Cline Settings:** Click the settings icon (⚙️) in the Cline panel.
+2.  **Select Provider:** Choose "Cerebras" from the "API Provider" dropdown.
+3.  **Enter API Key:** Paste your Cerebras API key into the "Cerebras API Key" field.
+4.  **Select Model:** Choose your desired model from the "Model" dropdown.
+5.  **(Optional) Custom Base URL:** Most users won't need to adjust this setting.
+
+### Cerebras's Wafer-Scale Advantage
+
+Cerebras has fundamentally reimagined AI hardware architecture to solve the inference speed problem:
+
+#### Wafer-Scale Architecture
+Traditional GPUs use separate chips for compute and memory, forcing them to constantly shuttle model weights back and forth. Cerebras built the world's largest AI chip—a wafer-scale engine that stores entire models on-chip. No external memory, no bandwidth bottlenecks, no waiting.
+
+#### Revolutionary Speed
+- **Up to 2,600 tokens per second** - often 20x faster than GPUs
+- **Single-second reasoning** - what used to take minutes now happens instantly
+- **Real-time applications** - reasoning models become practical for interactive use
+- **No bandwidth limits** - entire models stored on-chip eliminate memory bottlenecks
+
+#### The Cerebras Scaling Law
+Cerebras discovered that **faster inference enables smarter AI**. Modern reasoning models generate thousands of tokens as "internal monologue" before answering. On traditional hardware, this takes too long for real-time use. Cerebras makes reasoning models fast enough for everyday applications.
+
+#### Quality Without Compromise
+Unlike other speed optimizations that sacrifice accuracy, Cerebras maintains full model quality while delivering unprecedented speed. You get the intelligence of frontier models with the responsiveness of lightweight ones.
+
+Learn more about Cerebras's technology in their blog posts:
+- [The Cerebras Scaling Law: Faster Inference Is Smarter AI](https://www.cerebras.ai/blog/the-cerebras-scaling-law-faster-inference-is-smarter-ai)
+- [Introducing Cerebras Code](https://www.cerebras.ai/blog/introducing-cerebras-code)
+
+### Cerebras Code Plans
+
+Cerebras offers specialized plans for developers:
+
+#### Code Pro ($50/month)
+- Access to Qwen3-Coder with fast, high-context completions
+- Up to 24 million tokens per day
+- Ideal for indie developers and weekend projects
+- 3-4 hours of uninterrupted coding per day
+
+#### Code Max ($200/month)
+- Heavy coding workflow support
+- Up to 120 million tokens per day
+- Perfect for full-time development and multi-agent systems
+- No weekly limits, no IDE lock-in
+
+### Special Features
+
+#### Free Tier
+The `qwen-3-coder-480b-free` model provides access to high-performance inference at no cost—unique among speed-focused providers.
+
+#### Real-Time Reasoning
+Reasoning models like `qwen-3-235b-a22b-thinking-2507` can complete complex multi-step reasoning in under a second, making them practical for interactive development workflows.
+
+#### Coding Specialization
+Qwen3-Coder models are specifically optimized for programming tasks, delivering performance comparable to Claude Sonnet 4 and GPT-4.1 in coding benchmarks.
+
+#### No IDE Lock-In
+Works with any OpenAI-compatible tool—Cursor, Continue.dev, Cline, or any other editor that supports OpenAI endpoints.
+
+### Tips and Notes
+
+-   **Speed Advantage:** Cerebras excels at making reasoning models practical for real-time use. Perfect for agentic workflows that require multiple LLM calls.
+-   **Free Tier:** Start with the free model to experience Cerebras speed before upgrading to paid plans.
+-   **Context Windows:** Models support context windows ranging from 64K to 128K tokens for including substantial code context.
+-   **Rate Limits:** Generous rate limits designed for development workflows. Check your dashboard for current limits.
+-   **Pricing:** Competitive pricing with significant speed advantages. Visit [Cerebras Cloud](https://cloud.cerebras.ai/) for current rates.
+-   **Real-Time Applications:** Ideal for applications where AI response time matters—code generation, debugging, and interactive development.

docs/provider-config/doubao.mdx

@@ -0,0 +1,87 @@
+---
+title: "Doubao"
+description: "Learn how to configure and use ByteDance's Doubao AI models with Cline. Experience advanced reasoning, multimodal capabilities, and cost-effective inference with Chinese language optimization."
+---
+
+Doubao is ByteDance's flagship AI model series, featuring innovative sparse Mixture-of-Experts (MoE) architecture that delivers performance equivalent to much larger models while maintaining cost efficiency. With over 13 million users and advanced multimodal capabilities, Doubao offers competitive alternatives to Western AI systems with particular strength in Chinese language processing.
+
+**Website:** [https://www.volcengine.com/](https://www.volcengine.com/)
+
+### Getting an API Key
+
+1.  **Sign Up/Sign In:** Visit the [Volcano Engine Console](https://console.volcengine.com/). Create an account or sign in.
+2.  **Navigate to Model Service:** Access the AI model service section in the console.
+3.  **Create API Key:** Generate a new API key for the Doubao service.
+4.  **Copy the Key:** Copy the API key immediately and store it securely. You may not be able to view it again.
+
+### Supported Models
+
+Cline supports the following Doubao models:
+
+-   `doubao-seed-1-6-250615` (Default) - General purpose model with balanced performance
+-   `doubao-seed-1-6-thinking-250715` - Enhanced reasoning model with step-by-step thinking
+-   `doubao-seed-1-6-flash-250715` - Speed-optimized model for fast inference
+
+All models feature:
+-   **128,000 token context window** for extensive document processing
+-   **32,768 max output tokens** for comprehensive responses
+-   **Image input support** for multimodal applications
+-   **Prompt caching** with 80% discount on cached reads
+
+### Configuration in Cline
+
+1.  **Open Cline Settings:** Click the settings icon (⚙️) in the Cline panel.
+2.  **Select Provider:** Choose "Doubao" from the "API Provider" dropdown.
+3.  **Enter API Key:** Paste your Doubao API key into the "Doubao API Key" field.
+4.  **Select Model:** Choose your desired model from the "Model" dropdown.
+
+**Note:** Doubao uses the base URL `https://ark.cn-beijing.volces.com/api/v3` and servers are located in Beijing, China.
+
+### ByteDance's AI Innovation
+
+Doubao represents ByteDance's strategic entry into the AI model space with several key innovations:
+
+#### Sparse Mixture-of-Experts Architecture
+Doubao 1.5 Pro employs an innovative sparse MoE framework where 20 billion activated parameters deliver performance equivalent to a 140-billion-parameter dense model. This architecture significantly reduces operational costs while maintaining high performance standards.
+
+#### Extended Context Processing
+With context windows ranging from 32,000 to 256,000 tokens, Doubao excels at processing long-form content including legal documents, academic research, market reports, and creative content generation.
+
+#### Multimodal Excellence
+- **Advanced Visual Processing:** Enhanced visual reasoning, document recognition, and fine-grained information understanding
+- **Integrated Speech:** Seamless speech and text token integration with superior emotional continuity
+- **Document Analysis:** Comprehensive document summarization and content processing capabilities
+
+#### Chinese Language Optimization
+Doubao was specifically trained for Chinese language fluency and cultural relevance, providing significant advantages for Chinese-speaking users and applications requiring deep cultural context understanding.
+
+#### Cost Efficiency
+Doubao maintains pricing approximately **half the cost of comparable OpenAI offerings**, making advanced AI more accessible while establishing competitive market positioning.
+
+### Special Features
+
+#### Reasoning Models
+The `doubao-seed-1-6-thinking-250715` model offers enhanced reasoning capabilities with step-by-step thinking processes, making it ideal for complex problem-solving tasks.
+
+#### Multimodal Capabilities
+Unlike traditional cascaded approaches, Doubao integrates speech and text processing seamlessly, enabling more natural voice interactions and comprehensive document analysis.
+
+#### Prompt Caching
+All models support prompt caching with significant cost savings (80% discount on cached reads), making repeated queries more economical.
+
+#### ByteDance Ecosystem Integration
+Doubao integrates vertically with ByteDance properties including TikTok (Douyin), Toutiao, and Feishu, enabling seamless workflow integration across the ecosystem.
+
+### Performance and Benchmarks
+
+Doubao-1.5 Pro-AS1 Preview has demonstrated superior performance compared to OpenAI's O1-preview on specific benchmarks, including surpassing O1 models on AIME tests. The model continues to improve through reinforcement learning, with performance expected to enhance over time.
+
+### Tips and Notes
+
+-   **Regional Advantage:** Optimized for Chinese language and cultural contexts, making it ideal for Chinese-speaking users and markets.
+-   **Cost Effectiveness:** Approximately 50% lower cost than comparable Western AI models while maintaining competitive performance.
+-   **Context Windows:** Large context windows (up to 256K tokens) enable processing of extensive documents and codebases.
+-   **Multimodal Applications:** Strong visual and speech processing capabilities make it suitable for diverse multimedia applications.
+-   **Server Location:** Servers located in Beijing, China - consider latency implications for global users.
+-   **Ecosystem Benefits:** Integration with ByteDance services provides additional workflow advantages for users of TikTok, Toutiao, and Feishu.
+-   **Pricing:** Check the Volcano Engine console for current pricing information and regional availability.

docs/provider-config/fireworks.mdx

@@ -0,0 +1,131 @@
+---
+title: "Fireworks AI"
+description: "Learn how to configure and use Fireworks AI's lightning-fast inference platform with Cline. Experience up to 4x faster inference speeds with optimized models and competitive pricing."
+---
+
+Fireworks AI is a leading infrastructure platform for generative AI that focuses on delivering exceptional performance through optimized inference capabilities. With up to 4x faster inference speeds than alternative platforms and support for over 40 different AI models, Fireworks eliminates the operational complexity of running AI models at scale.
+
+**Website:** [https://fireworks.ai/](https://fireworks.ai/)
+
+### Getting an API Key
+
+1.  **Sign Up/Sign In:** Go to [Fireworks AI](https://fireworks.ai/) and create an account or sign in.
+2.  **Navigate to API Keys:** Access the API keys section in your dashboard.
+3.  **Create a Key:** Generate a new API key. Give it a descriptive name (e.g., "Cline").
+4.  **Copy the Key:** Copy the API key immediately. Store it securely.
+
+### Supported Models
+
+Fireworks AI supports a wide variety of models across different categories. Popular models include:
+
+**Text Generation Models:**
+-   Llama 3.1 series (8B, 70B, 405B)
+-   Mixtral 8x7B and 8x22B
+-   Qwen 2.5 series
+-   DeepSeek models with reasoning capabilities
+-   Code Llama models for programming tasks
+
+**Vision Models:**
+-   Llama 3.2 Vision models
+-   Qwen 2-VL models
+
+**Embedding Models:**
+-   Various text embedding models for semantic search
+
+The platform curates, optimizes, and deploys models with custom kernels and inference optimizations for maximum performance.
+
+### Configuration in Cline
+
+1.  **Open Cline Settings:** Click the settings icon (⚙️) in the Cline panel.
+2.  **Select Provider:** Choose "Fireworks" from the "API Provider" dropdown.
+3.  **Enter API Key:** Paste your Fireworks API key into the "Fireworks API Key" field.
+4.  **Enter Model ID:** Specify the model you want to use (e.g., "accounts/fireworks/models/llama-v3p1-70b-instruct").
+5.  **Configure Tokens:** Optionally set max completion tokens and context window size.
+
+### Fireworks AI's Performance Focus
+
+Fireworks AI's competitive advantages center on performance optimization and developer experience:
+
+#### Lightning-Fast Inference
+- **Up to 4x faster inference** than alternative platforms
+- **250% higher throughput** compared to open source inference engines
+- **50% faster speed** with significantly reduced latency
+- **6x lower cost** than HuggingFace Endpoints with 2.5x generation speed
+
+#### Advanced Optimization Technology
+- **Custom kernels** and inference optimizations increase throughput per GPU
+- **Multi-LoRA architecture** enables efficient resource sharing
+- **Hundreds of fine-tuned model variants** can run on shared base model infrastructure
+- **Asset-light model** focuses on optimization software rather than expensive GPU ownership
+
+#### Comprehensive Model Support
+- **40+ different AI models** curated and optimized for performance
+- **Multiple GPU types** supported: A100, H100, H200, B200, AMD MI300X
+- **Pay-per-GPU-second billing** with no extra charges for start-up times
+- **OpenAI API compatibility** for seamless integration
+
+### Pricing Structure
+
+Fireworks AI uses a usage-based pricing model with competitive rates:
+
+#### Text and Vision Models (2025)
+| Parameter Count | Price per 1M Input Tokens |
+|---|---|
+| Less than 4B parameters | $0.10 |
+| 4B - 16B parameters | $0.20 |
+| More than 16B parameters | $0.90 |
+| MoE 0B - 56B parameters | $0.50 |
+
+#### Fine-Tuning Services
+| Base Model Size | Price per 1M Training Tokens |
+|---|---|
+| Up to 16B parameters | $0.50 |
+| 16.1B - 80B parameters | $3.00 |
+| DeepSeek R1 / V3 | $10.00 |
+
+#### Dedicated Deployments
+| GPU Type | Price per Hour |
+|---|---|
+| A100 80GB | $2.90 |
+| H100 80GB | $5.80 |
+| H200 141GB | $6.99 |
+| B200 180GB | $11.99 |
+| AMD MI300X | $4.99 |
+
+### Special Features
+
+#### Fine-Tuning Capabilities
+Fireworks offers sophisticated fine-tuning services accessible through CLI interface, supporting JSON-formatted data from databases like MongoDB Atlas. Fine-tuned models cost the same as base models for inference.
+
+#### Developer Experience
+- **Browser playground** for direct model interaction
+- **REST API** with OpenAI compatibility
+- **Comprehensive cookbook** with ready-to-use recipes
+- **Multiple deployment options** from serverless to dedicated GPUs
+
+#### Enterprise Features
+- **HIPAA and SOC 2 Type II compliance** for regulated industries
+- **Self-serve onboarding** for developers
+- **Enterprise sales** for larger deployments
+- **Post-paid billing options** and Business tier
+
+#### Reasoning Model Support
+Advanced support for reasoning models with `<think>` tag processing and reasoning content extraction, making complex multi-step reasoning practical for real-time applications.
+
+### Performance Advantages
+
+Fireworks AI's optimization delivers measurable improvements:
+- **250% higher throughput** vs open source engines
+- **50% faster speed** with reduced latency
+- **6x cost reduction** compared to alternatives
+- **2.5x generation speed** improvement per request
+
+### Tips and Notes
+
+-   **Model Selection:** Choose models based on your specific use case - smaller models for speed, larger models for complex reasoning.
+-   **Performance Focus:** Fireworks excels at making AI inference fast and cost-effective through advanced optimizations.
+-   **Fine-Tuning:** Leverage fine-tuning capabilities to improve model accuracy with your proprietary data.
+-   **Compliance:** HIPAA and SOC 2 Type II compliance enables use in regulated industries.
+-   **Pricing Model:** Usage-based pricing scales with your success rather than traditional seat-based models.
+-   **Developer Resources:** Extensive documentation and cookbook recipes accelerate implementation.
+-   **GPU Options:** Multiple GPU types available for dedicated deployments based on performance needs.

docs/provider-config/groq.mdx

@@ -0,0 +1,80 @@
+---
+title: "Groq"
+description: "Learn how to configure and use Groq's lightning-fast inference with Cline. Access models from OpenAI, Meta, DeepSeek, and more on Groq's purpose-built LPU architecture."
+---
+
+Groq provides ultra-fast AI inference through their custom LPU™ (Language Processing Unit) architecture, purpose-built for inference rather than adapted from training hardware. Groq hosts open-source models from various providers including OpenAI, Meta, DeepSeek, Moonshot AI, and others.
+
+**Website:** [https://groq.com/](https://groq.com/)
+
+### Getting an API Key
+
+1.  **Sign Up/Sign In:** Go to [Groq](https://groq.com/) and create an account or sign in.
+2.  **Navigate to Console:** Go to the [Groq Console](https://console.groq.com/) to access your dashboard.
+3.  **Create a Key:** Navigate to the API Keys section and create a new API key. Give your key a descriptive name (e.g., "Cline").
+4.  **Copy the Key:** Copy the API key immediately. You will not be able to see it again. Store it securely.
+
+### Supported Models
+
+Cline supports the following Groq models:
+
+-   `llama-3.3-70b-versatile` (Meta) - Balanced performance with 131K context
+-   `llama-3.1-8b-instant` (Meta) - Fast inference with 131K context  
+-   `openai/gpt-oss-120b` (OpenAI) - Featured flagship model with 131K context
+-   `openai/gpt-oss-20b` (OpenAI) - Featured compact model with 131K context
+-   `moonshotai/kimi-k2-instruct` (Moonshot AI) - 1 trillion parameter model with prompt caching
+-   `deepseek-r1-distill-llama-70b` (DeepSeek/Meta) - Reasoning-optimized model
+-   `qwen/qwen3-32b` (Alibaba Cloud) - Enhanced for Q&A tasks
+-   `meta-llama/llama-4-maverick-17b-128e-instruct` (Meta) - Latest Llama 4 variant
+-   `meta-llama/llama-4-scout-17b-16e-instruct` (Meta) - Latest Llama 4 variant
+
+### Configuration in Cline
+
+1.  **Open Cline Settings:** Click the settings icon (⚙️) in the Cline panel.
+2.  **Select Provider:** Choose "Groq" from the "API Provider" dropdown.
+3.  **Enter API Key:** Paste your Groq API key into the "Groq API Key" field.
+4.  **Select Model:** Choose your desired model from the "Model" dropdown.
+
+### Groq's Speed Revolution
+
+Groq's LPU architecture delivers several key advantages over traditional GPU-based inference:
+
+#### LPU Architecture
+Unlike GPUs that are adapted from training workloads, Groq's LPU is purpose-built for inference. This eliminates architectural bottlenecks that create latency in traditional systems.
+
+#### Unmatched Speed
+- **Sub-millisecond latency** that stays consistent across traffic, regions, and workloads
+- **Static scheduling** with pre-computed execution graphs eliminates runtime coordination delays
+- **Tensor parallelism** optimized for low-latency single responses rather than high-throughput batching
+
+#### Quality Without Tradeoffs
+- **TruePoint numerics** reduce precision only in areas that don't affect accuracy
+- **100-bit intermediate accumulation** ensures lossless computation
+- **Strategic precision control** maintains quality while achieving 2-4× speedup over BF16
+
+#### Memory Architecture
+- **SRAM as primary storage** (not cache) with hundreds of megabytes on-chip
+- **Eliminates DRAM/HBM latency** that plagues traditional accelerators
+- **Enables true tensor parallelism** by splitting layers across multiple chips
+
+Learn more about Groq's technology in their [LPU architecture blog post](https://groq.com/blog/inside-the-lpu-deconstructing-groq-speed).
+
+### Special Features
+
+#### Prompt Caching
+The Kimi K2 model supports prompt caching, which can significantly reduce costs and latency for repeated prompts.
+
+#### Vision Support
+Select models support image inputs and vision capabilities. Check the model details in the Groq Console for specific capabilities.
+
+#### Reasoning Models
+Some models like DeepSeek variants offer enhanced reasoning capabilities with step-by-step thought processes.
+
+### Tips and Notes
+
+-   **Model Selection:** Choose models based on your specific use case and performance requirements.
+-   **Speed Advantage:** Groq excels at single-request latency rather than high-throughput batch processing.
+-   **OSS Model Provider:** Groq hosts open-source models from multiple providers (OpenAI, Meta, DeepSeek, etc.) on their fast infrastructure.
+-   **Context Windows:** Most models offer large context windows (up to 131K tokens) for including substantial code and context.
+-   **Pricing:** Groq offers competitive pricing with their speed advantages. Check the [Groq Pricing](https://groq.com/pricing) page for current rates.
+-   **Rate Limits:** Groq has generous rate limits, but check their documentation for current limits based on your usage tier.

docs/provider-config/zai.mdx

@@ -0,0 +1,124 @@
+---
+title: "Z AI (Zhipu AI)"
+description: "Learn how to configure and use Z AI's GLM-4.5 models with Cline. Experience advanced hybrid reasoning, agentic capabilities, and open-source excellence with regional optimization."
+---
+
+Z AI (formerly Zhipu AI) offers the groundbreaking GLM-4.5 series, featuring hybrid reasoning capabilities and agentic AI design. Released in July 2025, these models excel in unified reasoning, coding, and intelligent agent applications while maintaining open-source accessibility under MIT license.
+
+**Website:** [https://z.ai/model-api](https://z.ai/model-api) (International) | [https://open.bigmodel.cn/](https://open.bigmodel.cn/) (China)
+
+### Getting an API Key
+
+#### International Users
+1.  **Sign Up/Sign In:** Go to [https://z.ai/model-api](https://z.ai/model-api). Create an account or sign in.
+2.  **Navigate to API Keys:** Access your account dashboard and find the API keys section.
+3.  **Create a Key:** Generate a new API key for your application.
+4.  **Copy the Key:** Copy the API key immediately and store it securely.
+
+#### China Mainland Users
+1.  **Sign Up/Sign In:** Go to [https://open.bigmodel.cn/](https://open.bigmodel.cn/). Create an account or sign in.
+2.  **Navigate to API Keys:** Access your account dashboard and find the API keys section.
+3.  **Create a Key:** Generate a new API key for your application.
+4.  **Copy the Key:** Copy the API key immediately and store it securely.
+
+### Supported Models
+
+Z AI provides different model catalogs based on your selected region:
+
+#### GLM-4.5 Series
+-   **GLM-4.5** - Flagship model with 355B total parameters, 32B active parameters
+-   **GLM-4.5-Air** - Compact model with 106B total parameters, 12B active parameters
+
+#### GLM-4.5 Hybrid Reasoning Models
+-   **GLM-4.5 (Thinking Mode)** - Advanced reasoning with step-by-step analysis
+-   **GLM-4.5-Air (Thinking Mode)** - Efficient reasoning for mainstream hardware
+
+All models feature:
+-   **128,000 token context window** for extensive document processing
+-   **Mixture of Experts (MoE) architecture** for optimal performance
+-   **Agent-native design** integrating reasoning, coding, and tool usage
+-   **Open-source availability** under MIT license
+
+### Configuration in Cline
+
+1.  **Open Cline Settings:** Click the settings icon (⚙️) in the Cline panel.
+2.  **Select Provider:** Choose "Z AI" from the "API Provider" dropdown.
+3.  **Select Region:** Choose your region:
+    -   "International" for global access
+    -   "China" for mainland China access
+4.  **Enter API Key:** Paste your Z AI API key into the "Z AI API Key" field.
+5.  **Select Model:** Choose your desired model from the "Model" dropdown.
+
+### Z AI's Hybrid Intelligence
+
+Z AI's GLM-4.5 series introduces revolutionary capabilities that set it apart from conventional language models:
+
+#### Hybrid Reasoning Architecture
+GLM-4.5 operates in two distinct modes:
+- **Thinking Mode:** Designed for complex reasoning tasks and tool usage, engaging in deeper analytical processes
+- **Non-Thinking Mode:** Provides immediate responses for straightforward queries, optimizing efficiency
+
+This dual-mode architecture represents an "agent-native" design philosophy that adapts processing intensity based on query complexity.
+
+#### Exceptional Performance
+GLM-4.5 achieves a comprehensive score of **63.2** across 12 benchmarks spanning agentic tasks, reasoning, and coding challenges, securing **3rd place** among all proprietary and open-source models. GLM-4.5-Air maintains competitive performance with a score of **59.8** while delivering superior efficiency.
+
+#### Mixture of Experts Excellence
+The sophisticated MoE architecture optimizes performance while maintaining computational efficiency:
+- **GLM-4.5:** 355B total parameters with 32B active parameters
+- **GLM-4.5-Air:** 106B total parameters with 12B active parameters
+
+#### Extended Context Capabilities
+The 128,000-token context window enables comprehensive understanding of lengthy documents and codebases, with real-world testing confirming effective processing of nearly 2,000-line codebases while maintaining remarkable performance.
+
+#### Open-Source Leadership
+Released under MIT license, GLM-4.5 provides researchers and developers with access to state-of-the-art capabilities without proprietary restrictions, including base models, hybrid reasoning versions, and optimized FP8 variants.
+
+### Regional Optimization
+
+#### API Endpoints
+- **International:** Uses `https://api.z.ai/api/paas/v4`
+- **China:** Uses `https://open.bigmodel.cn/api/paas/v4`
+
+#### Model Availability
+The region setting determines both API endpoint and available models, with automatic filtering to ensure compatibility with your selected region.
+
+### Special Features
+
+#### Agentic Capabilities
+GLM-4.5's unified architecture makes it particularly suitable for complex intelligent agent applications requiring integrated reasoning, coding, and tool utilization capabilities.
+
+#### Comprehensive Benchmarking
+Performance evaluation encompasses:
+- **3 agentic task benchmarks**
+- **7 reasoning benchmarks** 
+- **2 coding benchmarks**
+
+This comprehensive assessment demonstrates versatility across diverse AI applications.
+
+#### Developer Integration
+Models support integration through multiple frameworks:
+- **transformers**
+- **vLLM**
+- **SGLang**
+
+Complete with dedicated model code, tool parser, and reasoning parser implementations.
+
+### Performance Comparisons
+
+#### vs Claude 4 Sonnet
+GLM-4.5 shows competitive performance in agentic coding and reasoning tasks, though Claude Sonnet 4 maintains advantages in coding success rates and autonomous multi-feature application development.
+
+#### vs GPT-4.5
+GLM-4.5 ranks competitively in reasoning and agent benchmarks, with GPT-4.5 generally leading in raw task accuracy on professional benchmarks like MMLU and AIME.
+
+### Tips and Notes
+
+-   **Region Selection:** Choose the appropriate region for optimal performance and compliance with local regulations.
+-   **Model Selection:** GLM-4.5 for maximum performance, GLM-4.5-Air for efficiency and mainstream hardware compatibility.
+-   **Context Advantage:** Large 128K context window enables processing of substantial codebases and documents.
+-   **Open Source Benefits:** MIT license enables both commercial use and secondary development.
+-   **Agentic Applications:** Particularly strong for applications requiring reasoning, coding, and tool usage integration.
+-   **Hybrid Reasoning:** Use Thinking Mode for complex problems, Non-Thinking Mode for simple queries.
+-   **API Compatibility:** OpenAI-compatible API provides streaming responses and usage reporting.
+-   **Framework Support:** Multiple integration options available for different deployment scenarios.

src/core/controller/models/getSapAiCoreModels.ts

@@ -1,7 +1,7 @@
 import axios from "axios"
-import { Controller } from ".."
-import { SapAiCoreModelsRequest } from "@/shared/proto/cline/models"
 import { StringArray } from "@/shared/proto/cline/common"
+import { SapAiCoreModelsRequest } from "@/shared/proto/cline/models"
+import { Controller } from ".."
 
 interface Token {
 	access_token: string

webview-ui/src/components/settings/SapAiCoreModelPicker.tsx

@@ -1,6 +1,6 @@
+import { sapAiCoreModels } from "@shared/api"
 import { VSCodeDropdown, VSCodeOption } from "@vscode/webview-ui-toolkit/react"
 import React, { memo, useMemo } from "react"
-import { sapAiCoreModels } from "@shared/api"
 import { DropdownContainer } from "./common/ModelSelector"
 
 export const SAP_AI_CORE_MODEL_PICKER_Z_INDEX = 1_000
@@ -71,7 +71,7 @@ const SapAiCoreModelPicker: React.FC<SapAiCoreModelPickerProps> = ({
 		if (categorizedModels.deployed.length > 0) {
 			// Add section separator (disabled option)
 			options.push(
-				<VSCodeOption key="deployed-header" value="" disabled>
+				<VSCodeOption disabled key="deployed-header" value="">
 					── Deployed Models ──
 				</VSCodeOption>,
 			)
@@ -89,14 +89,14 @@ const SapAiCoreModelPicker: React.FC<SapAiCoreModelPickerProps> = ({
 		if (categorizedModels.supported.length > 0) {
 			// Add section separator (disabled option)
 			options.push(
-				<VSCodeOption key="supported-header" value="" disabled>
+				<VSCodeOption disabled key="supported-header" value="">
 					── Not Deployed Models ──
 				</VSCodeOption>,
 			)
 
 			categorizedModels.supported.forEach((model) => {
 				options.push(
-					<VSCodeOption key={model.id} value={model.id} style={{ opacity: 0.6 }}>
+					<VSCodeOption key={model.id} style={{ opacity: 0.6 }} value={model.id}>
 						{model.id}
 					</VSCodeOption>,
 				)
@@ -113,9 +113,9 @@ const SapAiCoreModelPicker: React.FC<SapAiCoreModelPickerProps> = ({
 			</label>
 			<VSCodeDropdown
 				id="sap-ai-core-model-dropdown"
-				value={selectedModelId}
 				onChange={handleModelChange}
-				style={{ width: "100%" }}>
+				style={{ width: "100%" }}
+				value={selectedModelId}>
 				{renderOptions()}
 			</VSCodeDropdown>
 		</DropdownContainer>

webview-ui/src/components/settings/__tests__/SapAiCoreModelPicker.spec.tsx

@@ -1,7 +1,7 @@
 import { render, screen } from "@testing-library/react"
-import { describe, it, expect, vi } from "vitest"
-import SapAiCoreModelPicker from "../SapAiCoreModelPicker"
+import { describe, expect, it, vi } from "vitest"
 import { ExtensionStateContextProvider } from "@/context/ExtensionStateContext"
+import SapAiCoreModelPicker from "../SapAiCoreModelPicker"
 
 // Mock the shared API models
 vi.mock("@shared/api", async (importOriginal) => {
@@ -64,9 +64,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet", "gpt-4o"]}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -82,7 +82,7 @@ describe("SapAiCoreModelPicker Component", () => {
 	it("renders with default placeholder", () => {
 		render(
 			<ExtensionStateContextProvider>
-				<SapAiCoreModelPicker sapAiCoreDeployedModels={[]} selectedModelId="" onModelChange={mockOnModelChange} />
+				<SapAiCoreModelPicker onModelChange={mockOnModelChange} sapAiCoreDeployedModels={[]} selectedModelId="" />
 			</ExtensionStateContextProvider>,
 		)
 
@@ -94,10 +94,10 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
-					sapAiCoreDeployedModels={[]}
-					selectedModelId=""
 					onModelChange={mockOnModelChange}
 					placeholder="Choose SAP AI Core model..."
+					sapAiCoreDeployedModels={[]}
+					selectedModelId=""
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -110,9 +110,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet", "gpt-4o"]}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -132,9 +132,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet"]}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -154,9 +154,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet", "gpt-4o"]}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -174,9 +174,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet", "gpt-4o"]}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -196,9 +196,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet"]}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -217,9 +217,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		const { rerender } = render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet", "gpt-4o"]}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -231,9 +231,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		rerender(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet", "gpt-4o"]}
 					selectedModelId="gpt-4o"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -244,7 +244,7 @@ describe("SapAiCoreModelPicker Component", () => {
 	it("handles empty deployed models array", () => {
 		render(
 			<ExtensionStateContextProvider>
-				<SapAiCoreModelPicker sapAiCoreDeployedModels={[]} selectedModelId="" onModelChange={mockOnModelChange} />
+				<SapAiCoreModelPicker onModelChange={mockOnModelChange} sapAiCoreDeployedModels={[]} selectedModelId="" />
 			</ExtensionStateContextProvider>,
 		)
 
@@ -268,9 +268,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={allSupportedModels}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -294,9 +294,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet", "unsupported-model"]}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -313,9 +313,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet"]}
 					selectedModelId="anthropic--claude-3.5-sonnet"
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)
@@ -335,9 +335,9 @@ describe("SapAiCoreModelPicker Component", () => {
 		render(
 			<ExtensionStateContextProvider>
 				<SapAiCoreModelPicker
+					onModelChange={mockOnModelChange}
 					sapAiCoreDeployedModels={["anthropic--claude-3.5-sonnet"]}
 					selectedModelId=""
-					onModelChange={mockOnModelChange}
 				/>
 			</ExtensionStateContextProvider>,
 		)