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🪟 Context Window Calculator

Plan how much system prompt, user prompt, memory, retrieved context, tool output, and response budget fits inside an LLM context window.

 Runs in your browser
Context budgeting happens locally in your browser. Share links preserve the budget scenario so teammates can reproduce it.
What this tool does
Context Window Calculator estimates whether system instructions, user prompts, memory, retrieved documents, tool outputs, and reserved response tokens fit inside a selected model context limit.
Why it is useful
It helps AI builders budget tokens for agent runs, RAG calls, eval tasks, long prompts, and tool-heavy workflows before they hit context truncation.
How it works
The browser adds each budget section, applies a safety margin, and compares the total against the selected context window.
Best input
Use measured token counts when available. Otherwise paste estimates from a token counter and keep a safety margin for tokenizer variance and hidden provider formatting.
Agent tip
Separate retrieved context from memory and tool output. Those categories grow for different reasons and need different trimming strategies.
Privacy note
Context budget math runs locally in your browser.
Important limitation
Tokenization differs by model, language, and formatting. Treat results as planning estimates unless a model-specific tokenizer is used.
Quick answers
Why reserve a safety margin?
Providers may add hidden formatting, tool schemas, system messages, or safety text. A margin keeps a plan from barely fitting on paper but failing in practice.
Should I use the full context?
Usually no. Leave room for the answer, tool traces, retries, and tokenizer variance.
Common Use Cases
Budget a RAG request
Split a context window between system instructions, retrieved passages, tool output, and the answer before sending a long retrieval call.
Plan agent memory limits
Estimate how much saved memory and tool trace history can stay in context without crowding out the next user request.
Compare model window presets
Switch between 32k, 128k, 200k, and 1M-style windows to see whether a workflow needs trimming or a larger model.
API examples
Call the same deterministic core through Utilito’s compact API router. Send only data you intentionally submit to the server-side endpoint.
Try in API playground →Schema →
Budget a context window with curl
curl
curl -X POST https://utilito.dev/api/run \
  -H "Content-Type: application/json" \
  -d '{"tool_id":"context-window-calculator","input":{"preset":"gpt-4o","systemTokens":4000,"userPromptTokens":6000,"memoryTokens":8000,"retrievedContextTokens":20000,"toolOutputTokens":12000,"responseTokens":4000,"safetyMarginPercent":10}}'
Budget a context window from JavaScript
javascript
const res = await fetch('https://utilito.dev/api/run', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    tool_id: 'context-window-calculator',
    input: { preset: 'gpt-4o', systemTokens: 4000, userPromptTokens: 6000, memoryTokens: 8000, retrievedContextTokens: 20000, toolOutputTokens: 12000, responseTokens: 4000, safetyMarginPercent: 10 }
  })
});
const data = await res.json();
console.log(data.result.output || data.result.report);
Budget a context window from Python
python
import requests
payload = {
    "tool_id": "context-window-calculator",
    "input": {"preset": "gpt-4o", "systemTokens": 4000, "userPromptTokens": 6000, "memoryTokens": 8000, "retrievedContextTokens": 20000, "toolOutputTokens": 12000, "responseTokens": 4000, "safetyMarginPercent": 10},
}
result = requests.post("https://utilito.dev/api/run", json=payload).json()["result"]
print(result.get("output") or result.get("report"))
LLM Token Toolkit workflow
Move from prompt counting to cost estimation, context-window budgeting, and log trimming before spending LLM tokens.
🧮Token Counter💸Prompt Cost Calculator🧹Log Context Trimmer{}JSON Formatter🔐Base64 Encode / Decode🔗URL Encoder / Decoder
View all in Code →