# Can we create a GPT on top of the Deepseek api?

**URL:** <https://community.pickaxe.co/t/can-we-create-a-gpt-on-top-of-the-deepseek-api/3343>\
**Category:** Questions\
**Created:** [January 4, 2025, 9:07pm UTC](https://community.pickaxe.co/t/can-we-create-a-gpt-on-top-of-the-deepseek-api/3343 "2025-01-04T21:07:26Z")\
**Posts on this page:** 1\
**Showing post:** 17

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**Author:** ![admin\_mike](https://yyz2.discourse-cdn.com/flex004/user_avatar/community.pickaxe.co/admin_mike/32/283_2.png) [@admin\_mike](https://community.pickaxe.co/u/admin_mike)\
**Post date:** [January 17, 2025, 7:41pm UTC](https://community.pickaxe.co/t/can-we-create-a-gpt-on-top-of-the-deepseek-api/3343/17 "2025-01-17T19:41:49Z")

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Yes there is actually a way to do this @zerodot. Let me explain.

The [way end-user uploads functions](https://community.pickaxe.co/t/user-document-uploads-rag-vs-deep-learning-token-consumption/3383/2) actually allows this.

Here’s a rundown:

- The end-user upload process always looks the **maximum input length** setting of a Pickaxe. This can be 1000 tokens or 100,000 tokens.

- Then it looks at the size of the document. This could be 1000 tokens, 30,000 tokens, or 3,000,000 tokens.

- If the document fits into the maximum input length, then the system dumps the entire document contents into the context conversation. No vector embeddings. If it does not fit, it’s turned into vector embeddings.

The takeaway for Pickaxe users is you can select which process you prefer based on your use case by _increasing the the size of the maximum input length._

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_[View the full topic](https://community.pickaxe.co/t/can-we-create-a-gpt-on-top-of-the-deepseek-api/3343)._