How the Context Window Optimizer Works
The Context Window Optimizer is a browser-based planning tool for allocating tokens across the components of an LLM request. Every API call to a language model consumes tokens from a fixed-size context window. That window must accommodate your system prompt, any few-shot examples, the user's input, and the model's generated output. If the total exceeds the window size, the request fails or the model silently truncates content. This tool makes the allocation visible and helps you plan before you hit production.
Select a target model from the dropdown to set the context window size and pricing. Paste your actual system prompt and few-shot examples into the text areas, and the tool estimates their token consumption using a character-based heuristic. Set your expected user input size and desired output length using the numeric inputs. The visual budget bar updates in real time, showing exactly how much of the window each component consumes and how much headroom remains. If your allocation exceeds the window, a warning appears immediately.
Understanding Token Budgets Across Models
Different models offer vastly different context window sizes, and the right choice depends on your use case. Claude 3.5 Sonnet provides 200,000 tokens of context at $3 per million input tokens, making it an excellent general-purpose choice for most workflows. Claude 4 Opus also offers 200,000 tokens but with significantly more capable reasoning at $15 per million input tokens. GPT-4o provides 128,000 tokens at $2.50 per million. Gemini 2.5 Pro leads with 1,000,000 tokens at just $1.25 per million, which makes it the clear choice for bulk document processing where you need to ingest entire codebases or book-length documents in a single pass.
The maximum output length also varies dramatically. Claude 3.5 Sonnet caps output at 8,192 tokens, which is roughly 6,000 words. Claude 4 Opus extends this to 32,000 tokens for longer-form generation. GPT-4o sits at 16,384 tokens. Gemini 2.5 Pro allows up to 65,536 tokens of output. If your workflow involves generating long documents, code files, or detailed reports, the output cap matters as much as the total context window. The optimizer shows both limits so you can plan accordingly.
Chunking Strategies for Long Documents
When a document exceeds your remaining input budget, you need to split it into chunks and process each chunk in a separate request. The choice of chunking strategy significantly impacts quality and cost. Fixed-size chunking is the simplest approach: divide the document into equal-length segments of N tokens each. This works well for structured data like CSV files, log entries, or JSON records where each line is independent. The downside is that fixed-size chunks often split mid-sentence or mid-paragraph, which can confuse the model.
Semantic chunking addresses this by splitting at natural language boundaries. The text is divided at paragraph breaks, heading boundaries, or sentence endings, with each chunk sized to stay under the token limit. This preserves the coherence of each segment, which leads to better model comprehension and higher-quality outputs. The trade-off is that chunk sizes vary, so some chunks may be significantly smaller than the limit, reducing efficiency. For articles, documentation, legal contracts, and any prose-heavy content, semantic chunking is the default recommendation.
Sliding window chunking adds overlap between consecutive chunks. If your chunk size is 4,000 tokens with 200 tokens of overlap, the second chunk starts 3,800 tokens into the document rather than 4,000 tokens in. The overlapping region provides context continuity, which is critical for tasks like summarization chains where each chunk's summary needs to connect logically with the previous one. The cost is higher total token consumption since the overlapping tokens are processed multiple times. Use sliding window when context preservation across boundaries is more important than minimizing API costs.
Token Budget Allocation Best Practices
The system prompt is your highest-leverage allocation. Every token in the system prompt is sent with every single request. A system prompt that is 2,000 tokens costs 2,000 tokens multiplied by every request you make. In a production application handling 10,000 requests per day, that is 20 million tokens per day just for the system prompt. Compressing the system prompt without losing essential instructions is the single most impactful optimization. Techniques include removing polite filler, using structured formats instead of prose, and consolidating overlapping instructions.
Few-shot examples have the same multiplicative cost as the system prompt since they are included in every request. The optimal number of examples depends on the task complexity. For simple classification tasks, zero or one example often suffices because the model already understands the task from the system prompt alone. For complex formatting tasks or domain-specific outputs, two to three carefully crafted examples typically achieve near-maximum quality. Beyond three examples, quality improvements are marginal while costs scale linearly. Each example should demonstrate a distinct aspect of the desired behavior rather than repeating similar patterns.
The user input allocation should match your actual distribution. If 95% of your users send inputs under 1,000 tokens but your edge case is 10,000 tokens, design your system for the common case and implement a separate flow for long inputs. Over-allocating for edge cases wastes headroom on every request. Similarly, the output allocation should match the actual generation length, not the model's maximum. Set the max_tokens parameter to cap generation at your expected length plus a buffer. This prevents both wasted tokens and unexpectedly long outputs that slow down your application.
Cost Optimization at Scale
At production scale, small per-request savings compound into significant monthly cost differences. The cost estimator in this tool lets you model different scenarios. Compare what happens to your monthly bill if you switch from Claude 4 Opus to Claude 3.5 Sonnet for tasks that do not require Opus-level reasoning. Compare the cost of processing a 100,000-token document in one pass with Gemini 2.5 Pro versus splitting it into 25 chunks with Claude 3.5 Sonnet. In many cases, the cheaper option also produces better results because smaller, focused chunks lead to more precise outputs.
Prompt caching, available through Anthropic's API, is a particularly powerful optimization. When you cache the system prompt and few-shot examples, subsequent requests that use the same cached prefix pay only 10% of the normal input token cost for the cached portion. For applications with a stable system prompt and fixed examples, this can reduce input costs by up to 90% for the cached segment. The optimizer does not calculate cached pricing directly, but you can estimate it by reducing the system prompt and example costs by 90% in your mental model. For teams running ClaudKit API workflows, prompt caching integrates directly into the request pipeline.
Cross-model routing is another advanced optimization. Route simple classification requests to the cheapest model and reserve expensive models for complex reasoning. A routing layer adds development complexity but can cut costs by 60-80% for mixed workloads. Use the model comparison table in this tool to identify which model offers the best price-performance ratio for each task type in your pipeline. For teams already using the visual workflow designer, each block in the workflow can target a different model based on the complexity of that step.
Privacy and Local Execution
The Context Window Optimizer runs entirely in your browser. No prompts, examples, or configuration data are sent to any server. Token estimates are calculated client-side using a character-based heuristic. Budget plans exported as JSON are generated and downloaded locally. There are no accounts, no cookies, no analytics, and no server-side processing. Your prompt text, system instructions, and all other content remain private on your device at all times. The complete application is static HTML, CSS, and JavaScript. For teams working with sensitive prompts or proprietary system instructions, this local-only architecture ensures nothing leaves your machine.