Context Window Checker
Check if your text fits within the context window of popular AI models. Paste your prompt and see which models can handle it.
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How to use this calculator
Tokens are estimated at 1 token per 4 characters. The reserved output buffer is subtracted from each model's context window to determine if your input will fit while leaving room for the response.
- 1
Paste your full prompt or document text into the text area.
- 2
Set the number of tokens you want to reserve for the model's output (default: 2,000).
- 3
Read the table to see which models can handle your input and what percentage of each context window you are using.
Frequently asked questions
How does the checker estimate tokens?
It counts the characters in the text box and divides by 4, rounding up. There is no real tokenizer behind it, and the same estimate is applied to every model in the list even though each vendor tokenizes differently.
What does "fits" mean in the results?
Estimated tokens plus the reserved output tokens must be less than or equal to the model’s context window. A total exactly equal to the window still counts as fitting. The percentage shown is that total divided by the window, so it includes the reserve.
Where do the context window sizes come from?
They are a fixed table in the code covering ten entries, from 8,192 tokens for the two Llama 3 entries up to 1,000,000 for the two Gemini 1.5 entries. They are figures built into this tool; vendors release new versions and change limits, and hosts may serve a model with a smaller window, so confirm on the provider’s model page.
Does the reserve setting limit the model’s answer length?
No. The reserve is only subtracted in the arithmetic to leave headroom for a reply. To actually cap output you have to set the maximum output tokens parameter in your API call, and providers apply their own output limits separately from the context window.
Is the estimate reliable for code or non-English text?
Not particularly. Characters ÷ 4 is a rule of thumb for English prose, and the checker has no awareness of scripts, code syntax or numbers. For Chinese, Japanese, Korean, Arabic and similar text, or for dense code, the real count can differ substantially, so verify with the provider’s own token counter.
What exactly is counted as a character?
Everything in the text area, including spaces and line breaks, measured as the browser reports the string length. Some characters, such as many emoji, count as two units.
If a model shows "Fits", will my API call definitely work?
Not necessarily. The check covers context length only. Request size limits, rate limits, separate output caps, image or file tokens and provider-side changes to a model’s limits can still cause an error.
Why are both Llama 3 entries listed at 8,192 tokens?
The code stores the same 8,192 figure for the 8B and 70B entries. Other releases and hosted variants of these model families may offer different windows, so check the exact model card for your deployment.
Context window checker: test a prompt against ten built-in model limits
What the check measures
Paste text and the checker counts characters, divides by 4 and rounds up to estimate tokens. It adds the number of output tokens you want to reserve, then compares that total with each context size in its list. A model passes when the total is less than or equal to its window, so a request that lands exactly on the limit still shows as fitting. The percentage shown is total needed ÷ window, which means it includes the reserved output and not only your prompt. For failing models the same figure reads as how many times too large the request is.
Context sizes stored in the code
These are the figures built into this tool. Vendors change windows and release variants, and some hosts serve a model with a smaller limit than the original release, so confirm the number on the provider’s model page before relying on it.
| Model entry | Window |
|---|---|
| GPT-3.5 Turbo | 16,385 |
| GPT-4 Turbo | 128,000 |
| GPT-4o | 128,000 |
| Claude 3 Haiku | 200,000 |
| Claude 3.5 Sonnet | 200,000 |
| Gemini 1.5 Flash | 1,000,000 |
| Gemini 1.5 Pro | 1,000,000 |
| Llama 3 (8B) | 8,192 |
| Llama 3 (70B) | 8,192 |
| Mistral Large | 32,000 |
Worked examples
The test texts were runs of a single ASCII character, so only their length matters. Each row was produced by running the checker.
| Text length | Reserve | Total needed | Selected results |
|---|---|---|---|
| 40,000 characters | 2,000 | 12,000 tokens | GPT-3.5 Turbo fits at 73.2% used; Mistral Large fits at 37.5%; both Llama 3 entries fail at 146.5% needed |
| 100,000 characters | 4,000 | 29,000 tokens | GPT-3.5 Turbo fails at 177.0% needed; Mistral Large fits at 90.6% used; GPT-4o fits at 22.7% |
| 480,000 characters | 8,000 | 128,000 tokens | GPT-4o fits at 100.0% used; Claude 3.5 Sonnet at 64.0%; Mistral Large fails at 400.0% needed |
Why the reserve matters
The third run shows the edge: 120,000 estimated tokens plus an 8,000 reserve comes to exactly 128,000, which the GPT-4o entry accepts at 100.0%. Add a single token and it would flip to a failure. In practice a prompt that fills the whole window leaves no room for a long reply, so set the reserve to the longest answer you expect. The reserve is only arithmetic in this tool; it does not set a maximum-output limit anywhere, and your API call needs its own setting for that.
Limits of the estimate
Characters ÷ 4 is a rule of thumb for English prose. Code, tables, URLs and text in other scripts tokenize differently, and each model family has its own tokenizer, so the same text can have different true counts on different models while this page applies one estimate to all of them. The length is the browser’s string length, so an emoji can count as two. Treat any result at about 90 percent or higher as a cue to measure with the provider’s own token-counting tool before sending the request.
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Results are estimates for informational purposes only and do not constitute professional financial, medical, legal, or technical advice. Read full disclaimer →