Context Window Usage
See how much of a model’s context window your prompt uses and how many tokens remain.
Formula last reviewed 4 August 2026 · How we verify our calculators
Context used
- Tokens remaining
- 5,00,000
Updates live as you type
Frequently asked questions
It is the maximum number of tokens a model can consider at once — your prompt plus its response. Exceeding it truncates input or causes errors, so usage must stay within the limit.
Usage % = prompt tokens ÷ context window limit × 100. With 500K tokens used of a 1M window, you are at 50% with 500K remaining.
Yes. The window holds both input and generated output, so leave headroom for the reply — filling the window with prompt leaves no room to answer.
Very long contexts can raise latency and cost and may dilute the model’s focus. Keeping prompts lean often improves both quality and price.
Half your window gone before the model even replies
500,000 prompt tokens against a 1,000,000-token context window puts usage at exactly 50% — 500,000 ÷ 1,000,000 × 100 — leaving 500,000 tokens of headroom for the response and any further conversation turns. Push the same prompt to 950,000 tokens on that identical window, and only 50,000 tokens remain — likely too little room for a substantial reply, which is precisely the situation this calculator exists to catch before a request fails or gets silently truncated mid-generation.
Why the window has to hold more than just your prompt
A context window covers input and output together, not input alone — every model has to reserve room within that same limit for whatever it generates back to you. That single fact is the one most people miss: filling the window entirely with prompt leaves literally no space for a response, regardless of how capable the model is.
What happens well before you actually hit the limit
Hitting the hard cap isn't the only cost of a long context. Very long prompts can raise both latency and cost, and in some cases dilute a model's focus on what actually matters in the request — so keeping prompts lean by trimming boilerplate, summarising conversation history, or retrieving only the relevant chunks in a RAG pipeline often improves both answer quality and price simultaneously, well before the limit itself becomes a problem.
Where this fits alongside the cost calculators
Use this to plan large-document workflows, RAG pipelines and long conversations, and to know when to summarise or split input rather than discovering the limit mid-task. Context usage and API cost are separate concerns that both scale off the same token counts — pair this with the Claude, OpenAI or Gemini cost calculators once you know your token volume, to see what that same context actually costs to run.