AI Context Window Planner

How to Use the AI Context Window Planner

Enter your system prompt length, average user message, desired output length and RAG chunk size. Furthermore, the planner calculates how many chunks fit within each model's context window. It shows the optimal chunking strategy and warns when context limits are exceeded. Additionally, it recommends the most cost-effective model for your specific context requirements.

1. Enter prompt sizesSystem prompt tokens, user message tokens and desired output tokens.
2. Set RAG parametersChunk size in tokens and number of chunks to retrieve.
3. View allocationSee how tokens are distributed across system, context, RAG and output zones.
4. Check model fitSee which models can handle your context requirements and at what cost.
5. Copy planCopy the context allocation plan for your development documentation.

What Is a Context Window?

The context window is the maximum number of tokens an AI model can process in a single request. Furthermore, it includes everything: system prompt, conversation history, retrieved documents (RAG chunks) and the generated output. When input exceeds the context window, the model either truncates silently or returns an error.

Context windows vary dramatically across models. Furthermore, GPT-5 Nano has 16K tokens (approximately 48 pages). Claude Sonnet 4.6 supports 200K tokens (approximately 600 pages). Gemini models offer up to 1 million tokens (approximately 3,000 pages). Additionally, larger context windows cost more per request and may increase latency.

Context Window Sizes (June 2026)

The table below lists context window sizes and costs for major models. Furthermore, context size determines how much information you can provide alongside your prompt. Larger windows enable longer conversations, bigger document analysis and more RAG chunks.

RAG Chunking Strategy

Retrieval-Augmented Generation (RAG) retrieves relevant document chunks and injects them into the prompt. Furthermore, the total tokens consumed equals system prompt plus user query plus all retrieved chunks plus output headroom. Overfilling the context window with too many chunks pushes out output space and degrades response quality.

The optimal chunk size depends on your document type. Furthermore, technical documentation works well at 500 to 800 tokens per chunk. Legal documents need 800 to 1,200 tokens to preserve clause context. Additionally, overlap of 10 to 20 percent between adjacent chunks prevents information from being split at boundaries.

Token Budget Allocation

Divide your context window into four zones. Furthermore, allocate 5 to 10 percent for the system prompt (instructions, persona, format rules). Reserve 20 to 30 percent for output headroom (the model's response). Allocate the remaining 60 to 75 percent for user context and RAG chunks. Additionally, always leave a 5 percent safety margin to prevent truncation.

Long Context vs Chunked Processing

Some tasks require the entire document in context (legal analysis, narrative summarisation). Furthermore, other tasks work better with targeted chunk retrieval (question answering, fact extraction). Long context is simpler but more expensive. Chunked retrieval is cheaper but requires good search infrastructure.

For documents under 50 pages, long context with a 200K model is often the simplest approach. Furthermore, for document collections over 100 pages, RAG with vector search is more cost-effective. Additionally, hybrid approaches retrieve chunks for initial analysis, then use long context for synthesis and final output.

Prompt Caching and Context Efficiency

Prompt caching reduces the effective cost of large system prompts. Furthermore, OpenAI offers 50 to 90 percent discounts on cached input tokens. Anthropic offers 90 percent discounts on cache reads. If your system prompt is 5,000 tokens and repeats on every request, caching saves $13.50 per million requests on Sonnet 4.6. Additionally, cached prompts also reduce latency because the model does not reprocess them.

Design your prompts with caching in mind. Furthermore, place static instructions at the beginning (cacheable) and variable content at the end (changes per request). This maximises the cacheable prefix. Moreover, some providers automatically cache identical prefixes across requests without requiring explicit configuration. Check your provider's documentation for caching behaviour specifics.

References

1. Anthropic: Prompt Caching and Context Management.
2. OpenAI: Prompt Engineering Best Practices.
3. Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS.

Competitor Gap Analysis

No free tool exists that calculates token budgets across multiple models with RAG chunk planning. Furthermore, developers currently estimate context allocation manually or use spreadsheets. This planner automates the calculation and shows model fit instantly.

Common Context Window Mistakes

The most common mistake is forgetting to reserve output tokens. Furthermore, if you fill 95 percent of the context window with input, the model has only 5 percent left for its response. This produces truncated, incomplete answers. Additionally, always reserve at least 20 percent of the context window for output.

Another frequent error is not accounting for conversation growth. Furthermore, multi-turn conversations accumulate tokens with each exchange. A 10-turn conversation with 400 tokens per turn adds 4,000 tokens of history. Additionally, implement sliding window or summarisation strategies to prevent context overflow in long conversations.

Choosing Context Window Size

Match context window to task requirements. Furthermore, simple chatbot responses rarely need more than 16K tokens. Document analysis may require 128K to 200K. Full-book analysis needs 1M tokens (Gemini only). Additionally, larger context windows increase latency and cost. Use the smallest context that covers your use case.

Cost scales linearly with context size. Furthermore, sending 200K tokens on Sonnet 4.6 costs $0.60 per request. The same content on Gemini 2.5 Flash costs $0.06. Moreover, if your task requires large context but cost sensitivity is high, Google's Gemini models offer the best price-to-context ratio by a significant margin.

Frequently Asked Questions

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