The context window is the amount of information an AI model can ingest at one time while completing tasks. This includes your inputs (prompts, documents, lines of code, etc.) and the AI's responses. Once that limit is reached, older information is forgotten unless it's summarized or re-sent.
This is especially relevant for development teams using AI on large codebases—like during language migrations—where the model may not retain full project context across interactions.
AI tools often use less context than the underlying models' full capacity since space is reserved for chat history, tool calls, and internal operations. This leaves less room for your prompt and files.
| Tool | Standard Context | Expanded Context Options | Tool Limitations |
|---|---|---|---|
| Cursor |
- Code edits: ~10k tokens - Chat sessions: ~20k tokens ("Normal mode") |
- Max Mode: Automatically "upgrades" to the underlying model's full window (e.g.: 200k tokens for Claude 4 Sonnet/GPT-o3, 132k for Grok 3 Beta, 128k for GPT-4o/4.1, etc.) - Cost: Depends entirely on the LLM you choose; some (e.g. GPT-4o) charge more for long-context usage. |
- Once you hit ~10k (cmd-K) or ~20k (chat), Cursor begins pruning or summarizing older context—so it can drop relevant bits if you don't "new‐chat." - Larger model contexts (e.g. 200k) ✔ give more code coverage but ✖ are slower and significantly more expensive. - Hallucinations can occur as you approach the hard limit; Cursor recommends starting a new session. |
| Windsurf |
- Standard Mode (free or paid): Exact token/window limits are not publicly disclosed. Windsurf relies on its "Cascade" indexing engine to pull just the relevant snippets from your repo. - In practice, it can ingest multiple files via its semantic index, but there's no published context window figures. |
- Any LLM you point Windsurf at will drive your max window. For example, if you configure GPT-4o (≥ 128k) or Claude 4 (120k), Windsurf "can" leverage up to that model's token cap. - Cost: Depends entirely on the LLM you choose; some (e.g. GPT-4o) charge more for long-context usage. |
- Opaque Limit: There's no official "standard window" number—you rely on Windsurf's indexing to feed only relevant code. - If the LLM you pointed at has a smaller limit, Cascade will fail to include deeply buried context. - Because they don't publish a hard token limit, it's hard to know when you're "close" to the cap. Performance can degrade without a clear warning. |
| Github Copilot |
- Code completion: ~8k tokens - Copilot Chat (standard): ~4k tokens |
- Copilot Chat w/ GPT-4o: • VS Code Insiders: 128K-token window • VS Code Stable (Pro+): 64K-token window - To unlock 128K, you must be on Copilot Pro+ and running the VS Code Insiders build set to GPT-4o. - Cost: Subscription-based (Copilot Pro or Pro+). Once you're on Pro, long-context (64K/128K with GPT-4o) is included in your flat fee—there's no additional per-token charge. |
- Code completion vs. Chat: The 8k "code" window does not apply to chat—chat is 4k by default. - Expanded Chat (64k) requires GPT-4o in Copilot Chat (Pro+ or Insiders). If you fall back to the "copilot-chat" model, you're back to 4k. - No "cmd + K" style multi-file edits: Even with 64k in chat, you must explicitly provide/pin context—Copilot won't automatically pull files beyond what you've already opened or pinned. |
If you are working with large amounts of context, here are some tips & recommended best practices:
• Split the file into multiple files, either temporarily or through a refactor. This helps you follow the single-responsibility principle.
• Create a code map which includes all class/module names & methods. For each method, define the purpose of the method, parameters, and return values without the code itself to drastically reduce token size. AI can help with this.
• Split your request into multiple steps. Each step should require less context.
• Use Cursor Max mode to get the most context currently possible with an AI coding tool.
While these values do not carry over to your IDE, if you need to process a large amount of context, you can use the models directly. Google's Gemini models provide the largest context windows currently available.
| Model Name | Context Length (tokens) | Approx. lines of code |
|---|---|---|
| GPT-4o (OpenAI) | 128,000 tokens | ~12,800 |
| Claude 4 (Anthropic) | 200,000 tokens | ~20,000 |
| Claude 3.5 (Anthropic) | 200,000 tokens | ~20,000 |
| Gemini 2.5 Pro (Google) | 1,000,000 tokens (2,000,000 planned) | ~100,000 |
| Gemini 1.5 Pro (Google) | 1,000,000 – 2,000,000 tokens | ~100,000-200,000 |
| Llama 3.1 405B (Meta) | 128,000 tokens | ~12,800 |
| Mistral Codestral (Open) | 256,000 tokens | ~25,600 |
Understanding context windows is crucial for maximizing the effectiveness of AI coding tools. By choosing the right tools and implementing proper context management strategies, you can significantly improve your development workflow and AI assistance quality.
Reach out to DevClarity to optimize your AI coding workflow and context management in 30 days.
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