[siscia]

I am getting quite deep into coding with AI and cost of tokens is a bit of an issue indeed.

Trivial issue because it saves me A LOT of time, but it could be an issue for new people testing it.

I would love to test this approach. Are you guys fine tuning for each codebase?

[samatdav]

Yes, we fine-tune for each codebase. Now we are focusing on larger enterprise codebases that would: 1. benefit from the fine-tuning the most. 2. have the budget to pay us for the service. For smaller projects that are price-sensitive we are probably not a good fit at this point.

[manishsharan]

>>cost of tokens is a bit of an issue indeed

Their cost is $0.7 per 1M token.

DeepSeek is $0.14 / 1M tokens ( cache miss)

[siscia]

DeepSeek is an amazing product but has few issues:

1. Data is used for training

2. Context window is rather small and doesn't fit as well large codebase

I keep saying this over and over in all the content I create, the valu of coding with AI will come from working on big, complex, legacy codebases. Not from flashy demo where you create a to-do app.

For that you need solid models with big context and private inference.

[MacsHeadroom]

DeepSeek is open source and has a context length of 128k tokens.

[siscia]

Commercial service have a context of 64k tokens, which I find quite limiting.

https://api-docs.deepseek.com/quick_start/pricing

Running it locally is quite a bit beyond the scope of being productive while coding with AI.

Beside that 128k is still significantly less than Claude

[elashri]

Shouldn't we be comparing with other open source model? In particular since this is about llama3.3 then they have the exact context limit which is 128k [1]. Also

[1] https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct

[siscia]

Why?

Whenever using a model to be more effective as a developer I don't particularly care if the model is open source or closed source.

I would love to use open source models as well, but the convenience to just plug an API against some endpoints in unbeatable.