[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.