Context Window Checker (Will my prompt fit?)
Check whether your prompt fits a model's context window before you send it. Estimates prompt tokens, reserves room for the response, and shows remaining headroom across GPT-4o, o1, Claude, Gemini, DeepSeek and more. 100% local.
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CommentsPaste your prompt and pick a model above to see whether it fits the context window. The tool estimates prompt tokens, reserves room for the response, and shows how much headroom is left — or by how much you're over. Everything runs in your browser.
Prompt tokens
39
Context window
128,000
Reserved output
1,000
Remaining
126,961
Fits. After reserving 1,000 tokens for the response, you still have 126,961 tokens of headroom in GPT-4o's window.
Token counts are statistical estimates (±3% vs. official tokenizers) and run entirely in your browser. Context-window sizes reflect each provider's public docs and may change.
What is Context Window Checker?
Every model has a context window: the maximum number of tokens it can handle in a single request, counting your prompt and the model's response together. GPT-4o and o1 sit at 128K–200K, Claude at 200K, and Gemini 1.5 Pro at up to 2M, while GPT-3.5 is a tight 16K. Overflow the window and the API rejects the call with 'context length exceeded' — after you've already paid to send it. A context window checker estimates your prompt's token count, subtracts the tokens you want to reserve for the answer, and tells you up front whether the request will fit, so you can trim the prompt or switch models before you hit the error.
How to check if a prompt fits
- 1Pick the target model from the dropdown.
- 2Set how many tokens to reserve for the response (e.g. 1000).
- 3Paste your full prompt — system, examples, and user input.
- 4Read the usage bar: green fits, amber is tight, red overflows.
Use Cases
Avoid 'context length exceeded'
Check long RAG prompts or pasted documents before sending, instead of catching the error at runtime.
Right-size your model
See at a glance whether a cheaper small-context model is enough or you genuinely need a 200K/1M-context model.
Key Concepts
- Token
- The unit models bill and measure by — roughly ¾ of a word in English.
- Context window
- Max combined input + output tokens per request. Compare sizes across models.
Frequently Asked Questions
What is a context window?
The context window is the maximum number of tokens a model can process in one request — input plus output combined (per each provider's accounting). Exceed it and the API returns a 'context length exceeded' error.
Why reserve tokens for output?
The window is shared between your prompt and the model's response. If your prompt fills the entire window, there's no room left to generate an answer. Reserving output tokens (e.g. 1000) ensures the model has space to reply.
How accurate is the token estimate?
It uses a fast statistical estimator within ±3% of official tokenizers for typical English and code. For billing-critical or edge-case prompts, verify with the provider's own tokenizer.
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