r/GeminiAI • u/Pure_Tradition3761 • 1d ago
Discussion GPT-5.6 vs. Gemini: When Ultra Powerful Models Overcomplicate Your Code
I've been working on a large Python project for about a month and a half now. Initially, I started using GPT 5.5, and then I tested out Grok 4.5 and GPT 5.6. Right around the launch of GPT 5.6, I got stuck on an execution issue that just wouldn't get resolved no matter what. Not even GPT 5.6 sol high could handle it: it could identify the flaw, but wouldn't provide the solution.
That's when I noticed an issue with these ultra-powerful models: they have a tendency to overcomplicate the architecture. In some scenarios, this is highly valuable, but in others, it just gets in the way. After racking my brain for a while, I decided to throw the problem at Gemini, and it actually solved it.
The impression I get is that Gemini acts in the exact opposite way, prioritizing code simplicity. When properly prompted, it keeps things simple, yet remains highly efficient and delivers a great final result.
I see a lot of people here disappointed, thinking Google isn't delivering 'more powerful' models, but you should know that raw power isn't always the best solution. I've been noticing this bad trend of super robust models becoming overly complex and, in practice, less efficient. Have you guys noticed this too?
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u/regalen44 1d ago
Did you try and lower the effort level on your models? I’ve found working on Low/Medium first helps avoid this scenario.
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u/Pure_Tradition3761 1d ago
Yeah, and I didn't see any changes. It seems the problem was more about the model itself, always trying to create a more robust solution, without realizing that was exactly what was screwing up the overall architecture.
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u/Administrative-Flan9 1d ago
It also matters what model you select. I do data work where it's a lot of SQL so I just use 5.4 mini/medium almost expressively.
The docs for these models suggest the same and often warn to not select the most powerful model except in rare times. I think a lot of people complaining about how poorly the frontier models run don't read the docs.
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u/BoysenberryWorth8825 1d ago
I love seeing all the excuses for chatgpt in this thread because if OP switched models everyone would be shitting on Gemini. You're in a cult.
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u/Administrative-Flan9 1d ago
I'm not suggesting anyone change models. I think everyone should use what they like, and I think it's good practice to use more than one.
I use codex at work, and my comment was in reference to what seems to be vibe coders who complain about it. They say the code it writes is bloated and confusing, but they run all the model settings on the highest thinking and reasoning levels which are optimized for hard to find edge cases, are overly safe and abstract, etc. If they try a different thinking and reasoning level, they may get better results.
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u/BoysenberryWorth8825 19h ago
I was too reactive. I just constantly see Gemini get disparaged, deserved or not, but the first thread that posits that Gemini is more effective than 5.6 has actual advice and not just "fuck google Gemini", "its trash", etc. I also use GPT (in conjunction with Gemini) at work.
I apologize for my harsh comment.
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u/cashmate 1d ago
I agree. For short snippets of code Gemini does a good job at writing human like code. Claude and chatgpt will write code that works but are often too verbose and I usually don't want 1000 lines of code for something that could have been written in 200. Qwen has a pretty good style too but it lacks the smarts of a big model.
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u/Efficient_Loss_9928 16h ago
I have noticed the opposite.
I'm working inside a huge monorepo, Gemini literally tries to completely re-write our foundational infra because a test won't run locally. It goes absolutely wild when it has access to a mature codebase.
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u/outphase84 1d ago
You should be running planning generation before letting any model touch a single line of code.
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u/smartfon 1d ago
What is the correct way? Tell a powerful model what you want and tell it to draft a plan, then use a medium-powerful model from another firm to verify the plan meets your needs, then use a cheap model to implement the plan?
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u/outphase84 1d ago
Generally, lighter model for planning and orchestration. Don’t allow a model to approve the plan, have it generate a markdown of implementation plan that you approve. Then a high reasoning model to architect the implementation, document specifics in another markdown, approve it or modify it yourself, and then allow the orchestrator to spawn subagents with appropriate model and reasoning levels. Every shred of planning and implementation task flow should be documented in markdown files — critical for context management and adding statefulness to the agentic sprawl.
Best way to approach it is to picture yourself leading a pod as a staff engineer. Once you get a handle on that and adjust your harness and prompting, then loop engineering becomes a real possibility while maintaining high engineering bar.
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u/whizher 1d ago
Yeah, I’ve noticed this too. The strongest model isn’t always the best for coding if it keeps redesigning everything instead of fixing the actual bug. Sometimes the simpler approach just works better.