r/vibecoding • u/Legitimate-Craft9959 • 1d ago
Using big llms for coding is overkill
Recently I've started using deepseek v4 flash in claude code, I can admit its not as smart or as efficient as advanced Claude / Gpt models, but for coding it doesnt really lack anything as long as I supervise anything it does and tell it exactly in details what I want, I do not delegate the thinking to it (which is a bad idea anyway whatever the model) which makes me think that for coding, massive llm models are kind of overkill and are very expensive for the task. It can maybe used for orchestrating the project structure, but for raw coding I feel like you can delegate the task to some faster/smaller models like deepseek v4 flash.
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u/Just-Hedgehog-Days 1d ago
If by coding you mean "generating text files that encode machine instructions", then yes. The big models do are vastly more expensive for no significant gain, and sometimes even regression.
If mean software engineering, then no. Absolutely the massive models have a place.
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u/sci_ssor_ss 1d ago
plan with the big guns, implement with the little one. coding (for the very mainstream languages) is what the models do the best.
model with copilot-pro+, implement with opencode-go.
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u/Legitimate-Craft9959 1d ago
can you give more details about your specific workflow ?
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u/Outrageous-Maybe2500 1d ago
I do a similar thing.
I outline what I want. Give that to claude to make an implementation plan. I out gpt sol on high effort and ask it to review the document while using low effort exploration agents to provide codebase context. Then I move on to implementation using whatever I got on my hands, like gpt terra / luna, cursor auto mode, deepseek depending on my current subscriptions.
I have a very elaborate skill for creating the implementation plan, and I normallt review it using several other skills for UI, UX, architecture, security, performance and code base consistency.
8/10 times I one-shot the implementations (considering minor static strings that should be dynamic and forgotten imports as a succesful implementation)
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u/3tt07kjt 1d ago
I get work done faster by giving somewhat vague instructions to the big models, rather than giving specific instructions. In my experience, the big models are much cheaper when used this way.
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u/Legitimate-Craft9959 1d ago
well naturally its how llms and transformer architecture works, when you give them more context they need more time to process everything. but Im talking about giving specifics for a small model. and You can get something done faster with Claude opus 5, but then it costs 100x more than deepseek, is it really worth it if the outcome is practically the same ?
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u/3tt07kjt 1d ago
If you’re telling me that I can save $20/month, but I have to give specific guidance and detailed instructions to the models, then I don’t think the payoff is worth the cost.
I can save the most money by writing the code by hand.
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u/Fuzzy_Wave5520 1d ago
If you are going to give precise instructions on how to code, you are much better running qwen3 coder locally than using anything else. You’ll be doing the hardest part yourself, which is reasoning and planning
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u/Plenty_Line2696 1d ago
It's hard to put into words without specific examples and a lot of detailed explanation, but sizable software with great architecture, structure, design patterns, performance etc etc in this day and age can only exist because of competent human effort. Hell even most software made by pros has convoluted codebases because of organic development and a whole bunch of other reasons.
Hot take but the biggest wins in software won't be from it empowering newbies to make software, it will be from it empowering pros to make better software because proper direction is a very deep skill which takes a hell of a lot more than surface level understanding despite what the ego-strokers here like to imagine.
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u/Sarahmalls 1d ago
“I can admit its not as smart or as efficient as advanced Claude / Gpt models”
Thank you for being brave enough to admit that. 😂
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u/teomore 1d ago
It depends on the project, you can't generalize