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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

Visual representation of the context window usage in Cline

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.

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Model Comparison

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LLM Model Comparison for Cline (Feb 2025)

ModelInput Cost*Output Cost*Context WindowBest For
Claude 3.5 Sonnet$3.00$15.00200KBest code implementation & tool use
DeepSeek R1$0.55$2.19128KPlanning & reasoning champion
DeepSeek V3$0.14$0.28128KValue code implementation
o3-mini$1.10$4.40200KFlexible use, strong planning
Gemini Flash 2.0$0.00$0.001MStrong all-rounder
Gemini 1.5 Pro$0.00$0.002MLarge context processing

*Costs per million tokens

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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

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Best Models by Mode (Plan or Act)

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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

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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

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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 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.

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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.