Your Whole Repo in One Prompt — DeepSeek V4 Made 1M-Token Context Cheap

Most AI forgets. Give it a big project and it reads a slice, then guesses at the rest — which is why it invents functions that do not exist and misses the file that actually matters. The fix is a bigger context window: enough room to hold the whole thing at once. The problem has always been that big context is expensive.

DeepSeek V4 changed the math. In the reel I loaded an entire real codebase into one prompt and asked a question you can only answer if you have seen all of it. Here are the actual numbers.

What DeepSeek V4 actually is

DeepSeek V4 is open-sourced (MIT), with two variants: V4-Pro (1.6T total / 49B active parameters) and V4-Flash (284B / 13B active). Both offer a 1,048,576-token context window — that is the “1 million tokens” figure, exactly. It gets there cheaply using DeepSeek Sparse Attention, which shrinks the memory cost of long context dramatically.

The real test: FastAPI’s entire codebase in one prompt

I measured this on the FastAPI repo:

• 1,136 files → 731,607 tokens.
• That fits inside the 1,048,576-token window with about 30% to spare.
• At DeepSeek’s published API price, reading all of it once costs roughly ₹10 (about $0.10).

For reference, V4-Flash input is $0.14 per million tokens on a cache miss (and a tiny $0.0028 on a cache hit); output is $0.28 per million. So a three-quarter-million-token prompt lands around ten cents. You can hand it the whole project and then ask your question, instead of feeding it fragments and hoping.

# the shape of it
1,136 files  →  731,607 tokens  →  fits 1,048,576-token window  →  ≈ ₹10 to read once

How to do it yourself

1. Get a DeepSeek API key from the platform.
2. Concatenate your codebase into one text blob (a quick script that walks the repo and prints each file with its path header is enough).
3. Send it as the context, then ask a question that requires the whole picture — “which two files define conflicting behavior?”, “trace how a request reaches the database”. A small-context model literally cannot answer these; a 1M-token one can.
4. Watch the token count and the cost — both are printed by the API response.

The honest limits

• 1 million tokens is large, not infinite. Very big monorepos still overflow it — 731k was FastAPI; your enterprise repo might not fit. It is a finite window, not unlimited memory.
• It runs on a hosted endpoint here. This demo uses DeepSeek’s API, not a model on your laptop. The weights are open, but running a 1.6T model locally is not a laptop job.
• Prices change. The ₹10 figure is from published pricing at the time of testing and a ~95.7 USD→INR rate. Check current rates before you quote it.

The takeaway for a student: the era where “the AI didn’t see my whole project” was an excuse is ending. For about the cost of a chai, an AI can now read your entire codebase before it answers.