DeepSeek vs. ChatGPT: Which One is Better for Developers?

Can anyone suggest which is the best

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Personally I prefer to steer clear of closed source AI, so the open source models are more appealing to me.

Try the most popular ones and see which you prefer? :smiley:

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I didn’t know about Qwen, Kimi and Mistral. :pray:

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It’s too early to say but in the last week I’ve:
built a CoT system with the help of ChatGPT and a Deep Research system with DeepSeek V3.
I deleted my Qwen account because PAYG didn’t mean to them what it meant to me.
Revived Cohere’s websearch connection for use with the Deep Research (DS) project. We’re going generic :slight_smile:
Shelved the Gemini 2.0 Thinking function because it no longer delivers the CoT.
Topped up my Mistral balance because on balance I’m moving away from US AI models.
:slight_smile:

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Here is something else to play with as a developer, the Zed editor. It lets you pull in files, folders, or active tabs for context. It supports a wide variety of different AI interfaces.

I’ve also started looking at the Gemini 2.0 Flash model. In the context of development work, the huge context size is interesting. Theo had a YT video about it.

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I’ve enjoyed Cline, which integrates best with Anthropic’s Claude 3.5 Sonnet model. It’s very smart, as long as your language, fromework, and desired results are all reasonably common / popular. Sadly, Anthropic is the priciest of the models.

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DeepSeek and ChatGPT both have strengths for developers. DeepSeek is optimized for code generation and completions, especially for specific programming tasks. ChatGPT offers a broader range of AI-assisted coding help, debugging, and explanations with a more conversational approach. If you need precise code generation, DeepSeek might be better, but for general problem-solving and learning, ChatGPT is more versatile.

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For developers, there are benefits to using different types of AI. A fixed price subscription will allow for unlimited reasoning about what you want to accomplish. Using different AI providers and/or models can broaden the scope of your results as each will reason in a different way. Then, a very well integrated pay per token AI can have much greater context for actually implementing changes. I wrote a blog post about my experience doing this with a combination of ChatGPT and Claude Code.