Hi there I am student from FAU Erlangen Germany, recently me and my team are working on voice ai system and we are using GPT-4.1 mini modal. we have bunch of flows or we can say call reasons and system prompt is really big.
what should we do ? any guid or help would be nice.
Let’s look at GPT-5 models, where you just avoid this for all total input, also considering that a conversation need “history” re-sent:
Input tokens exceed the configured limit of 272,000 tokens
GPT-5 does not have audio input/output, so you don’t need to consider here that spoken audio to an OpenAI model consumes about 5x as much as its transcript.
Then go to GPT-4.1 series, also text only (plus images): 1,047,576 context window, however, OpenAI is not meeting the promise for many users: {‘message’: “This model’s maximum context length is 300000 tokens...
The maximum you can send in one request is thus also limited by your API tier: tier 1, and you only get 30000 tokens per minute, meaning less than 30000 in one request otherwise you’ll be blocked by the rate limiter.
For reference, this length of input is not even in the ballpark of “system prompt is really big”. This is…
That 272,000-token context window is huge by human standards. To give you some analogies (assuming 1 token ≈ 4 characters of English text ≈ 0.75 words on average):
Books and Literature
~200,000 words → that’s about the length of War and Peace by Tolstoy, or Harry Potter and the Order of the Phoenix.
So you could fit an entire long novel into one prompt.
Academic Papers
A typical research paper: 8,000–10,000 words.
You could fit 20–25 full academic papers into one context.
News Articles
News article length: ~800–1,000 words.
That’s 200–250 articles in one go.
Shakespeare’s Works
Complete works of Shakespeare: ~900,000 words.
272k tokens ≈ 200k words → about 22% of all of Shakespeare’s works at once.
Business Documents
Standard contract or report: ~5,000 words.
You could load 40 such contracts or reports simultaneously.
Code
1 token ≈ 3–4 characters of code.
A medium-sized GitHub project (say, 600k characters of code) fits easily.
That’s the size of a large codebase for a SaaS backend.
Analogy summary:
272k tokens is like asking someone to remember a whole novel, 20+ research papers, or a few hundred news articles all at once—and then reason across them.