What's the best AI model for coding?

I really liked working with Gemini 3 on all things related to UX/UI but when it comes to logic Opus 4.5 is the king.

This is how I created @NMTV

We’ve been using CC Opus 4.5 and it’s been solid for how we work. It really helps when we’re dealing with heavier thinking like product logic, edge cases, or those moments where you’re trying to understand what breaks if one decision changes. It’s not the fastest, but it saves us from making expensive mistakes.

For quick iteration or speed stuff, I understand why people lean toward faster models. For us, we don’t fight it. If the task is mechanical, tests, small refactors, glue code, we switch to something faster. Using Opus for that is a waste.

Opus 4.5 is the king, but expensive. GPT 5.2 is probably the 2nd best on this list. Sonnet 4.5 is great still, but getting old.

Claude (Sonnet 3.5/3.6) for speed, Opus for complex architecture decisions. I've shipped 4 products as a weekend vibe coder over the last 3 months using almost exclusively Claude Code.

The thing nobody mentions: the model matters less than your prompting discipline. Clear specs, small scoped tasks, reviewing every output. I write maybe 30% of the code myself but review 100%. The security gap is real though - AI-generated code loves to skip input validation and auth edge cases.

For pure speed: Sonnet. For getting things right the first time on tricky stuff: Opus. GPT is fine but Claude just "gets" code structure better in my experience.

Been experimenting with different models for a conversational AI project I'm building. Here's what I've learned:

Context management is everything. The "best" model really depends on your use case:

- Complex refactoring? Opus 4.5 hands down. Worth the cost when you need deep reasoning.

- Quick iterations/prototyping? Sonnet 4.5 hits the sweet spot - fast enough to stay in flow, smart enough to handle most tasks.

- Frontend/UI work? Gemini Pro surprised me with speed and quality.

The real game-changer isn't just the model though - it's how you structure your prompts and manage context. I've found that keeping a clean git history and feeding the model focused diffs (not entire repos) makes a huge difference regardless of which model you use.

Also learned the hard way: don't let context bloat past 40-50%. Quality drops fast after that.

Still leaning toward Sonnet for coding — feels the most reliable for complex logic so far 👍

For coding I notice that Opus may be the best option but it seems to not appear in this poll list

Sonnet is my daily driver too but the cost conversation is real, especially when you're running it inside agents that call the model repeatedly on every task. What I've found actually helps more than switching models is being aggressive about context management: a tight structure.md, session-scoped feature specs, and clean commits between stages.

The output quality difference between Sonnet and Opus matters way less when you're not feeding the model half your codebase every call. For debugging specifically, Opus is noticeably better at holding the full error chain in mind across a long diff, Sonnet sometimes loses the thread mid-fix.

Voted for Sonnet 4.5 Been testing different models for a few months now. Here's what I've noticed:

• Sonnet 4.5 hits the sweet spot between speed and accuracy for coding tasks

• GPT-5.2 is powerful but slower and more expensive

• Gemini 3 is improving fast but still catching up on complex codebases

The 71% vote makes sense - it's not just hype. Sonnet actually delivers for day-to-day development work. Curious what others think about the cost-performance tradeoff. Are you sticking with one model or switching based on task complexity?

I will go for sonnet 4.5 its just really good + budget friendly bt sometimes i use Gemini 3 flash or pro with kilo code - it does the job for me sometimes...

Another factor people miss is that model pricing changes frequently. A model that was expensive 3 months ago might have dropped in price, or a new cheaper option appeared. We use aicosts.ai to track our actual per-model spend over time so we know when to re-evaluate. The best model for coding changes more often than people think.