TGetMoreTokens
● Runs locally in your browser

AI Context Window Checker

Paste a prompt, document, transcript, or code sample and see which leading AI model context windows can fit the planned request.

Paste the content you want to fit
Estimated input0
Total planned tokens2,000
Private: text remains in your browser.Plan headroom: system/tool context may add tokens.Pricing data verified —
Model fit

Which context windows can hold it?

The status includes your estimated input plus the expected output amount above.

ModelContextMax outputStatusEstimated remaining

Application frameworks can prepend system messages, tool definitions, memory, retrieved documents, or other hidden context. Leave operational headroom instead of targeting 100% utilization.

Planning

A context window is a budget, not a target.

Even when the raw text fits, real applications often add instructions, tool schemas, retrieval context, and conversational history.

For production workloads, measure provider-reported usage and reserve enough space for the output you actually need.

Input + outputThe checker considers both when evaluating a request.
Output capsA model may have a context window larger than its maximum generation limit.
Estimate cost nextTake the token result to the API Cost Calculator.
What 100K tokens is~75,000 words, 150 pages, and the chunk size that stays under OpenAI's 272K surcharge: How Many Words Is 100K Tokens?.
Context windows explainedInput and output share one budget — window sizes, output caps and what it costs to fill one: What Is a Context Window?.
What a token isThe unit behind every estimate — ~4 characters, 0.75 words: What Is an AI Token?
Sending scraped HTML?Measure what cleaning it saves: HTML Token Savings.
1M tokens in words750,000 words, 1,500 pages — and why only 5 of 8 models accept it in one request: How Many Words Is 1M Tokens?
Trim the promptFind repeated instructions, markup and filler: Prompt Weight Analyzer
Stop paying twiceChat re-sends its history every turn — 96.6% of input tokens on a 20-turn conversation: Conversation Cost Growth Calculator.
The full cost playbookSeven levers ranked by return, from re-sent history to a 22.7x model spread: How to Reduce LLM API Costs.
Input vs outputWhy the reply costs 5.0x–8.3x more per token than the prompt, and the break-even ratio: Input vs Output Tokens.
A 200-page PDF133,333 tokens, and the one model where it nearly does not fit: Can a 200-Page PDF Fit in an LLM?.
How pricing worksPer-1M rates, two counters and the formula behind every number on this site: How LLM Token Pricing Works.
Flat plan vs meteredWhere a monthly subscription stops being cheaper — break-even requests per day on all 8 models: API vs Subscription Break-even.
Retrieval-shaped costsChunk size, top-k and overlap priced per query, and what one more retrieved chunk costs: RAG Context Cost Calculator.
Agent loopsWhy an 8-step agent sends 66,400 input tokens, 75.9% of it re-read history: Agent Loop Cost Calculator.
Price directionIs token pricing going up or down? The eight-model basket index reads 87.1 against its 2026-08-09 baseline — down 12.9%, with both cuts scheduled to lapse: LLM Pricing Index.
A fourth providerDeepSeek prices input three ways — a cache-hit rate, a cache-miss rate and a peak/off-peak clock — at $0.30/$1.20 per 1M tokens on flash at peak, exactly half off-peak, with a 384,000-token output cap: DeepSeek API Pricing.
FAQ

Context-window questions

What is a context window?

It is the token capacity a model can work with for a request, subject to the provider's specific input and output rules.

Should I use the full context limit?

Usually not. Leave headroom for system instructions, tools, retrieval, conversation history, and output.

Is the token estimate exact?

No. The current static version estimates across model families rather than running each provider's exact tokenizer.