>>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)

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.

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

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

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

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.

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.