How I Use Different AI Models for Different Development Tasks

Most developers I know pick one AI tool and use it for everything. Same model, same interface, same approach — whether they're debugging a flaky test or designing a system from scratch. That's like using a chef's knife to open cans. It works, but you're missing the point.

I'm a frontend developer working primarily with React and TypeScript, expanding into Go on the backend. Over the past year, I've tried most of the popular AI coding tools — Copilot, GPT Codex, Cursor, and Claude in various forms. I've settled on a workflow built around Claude's model family, not out of loyalty, but because it lets me intentionally match the model to the task. This post is about how I think about that matching.

The mental model

Think of AI models as a team, not a single assistant. You wouldn't ask a senior architect to rename variables, and you wouldn't ask a junior to design your database schema. Models work the same way:

• Opus — the senior engineer. Slow, expensive, thinks deeply. Use it when the problem is ambiguous or the stakes are high.
• Sonnet — the reliable mid-level. Fast enough, smart enough, good default for most implementation work.
• Haiku — the intern with lightning reflexes. Near-instant, dirt-cheap, perfect for mechanical tasks where you just need correct output fast.

The skill isn't choosing the "best" model. It's knowing when to reach for which one.

Planning and architecture — Opus in chat

When I start something new — a feature, a system design, a refactor plan — I open Claude chat with Opus and just... think out loud. No code yet. I describe the problem, the constraints, the existing codebase patterns. Opus is genuinely good at pushing back on half-baked ideas before they become half-baked code.

I used this heavily when designing a layered Go backend for a side project. I knew I wanted handlers, services, and stores separated cleanly, but I wasn't sure how to wire dependencies without a DI framework. Two conversations with Opus saved me from three wrong architectures.

The key: don't ask Opus to write code in chat. Ask it to think. The code comes later.

Implementation — Claude Code with opusplan

This is where I spend most of my time. Claude Code with opusplan is the sweet spot: Opus handles the planning phase (reading the codebase, understanding the task, deciding what to change), then Sonnet takes over for the actual code generation.

My typical flow:

1. Hand Claude a task with context — a ticket description, relevant file paths, maybe a Figma reference.
2. Let it plan the approach. If the plan touches more files than I expect, I stop it and narrow the scope.
3. Implementation, type-checking, tests, commit — one session.

One thing I learned the hard way: always review the plan before letting it execute. Claude has a tendency to over-scope. You ask it to update a form component and it decides to refactor the entire hook system while it's there. Catching this at the plan stage saves real time.

Quick fixes and small tasks — Sonnet

Not everything needs Opus-level reasoning. Renaming props, adding a missing type, fixing a lint error, writing a simple utility function — Sonnet handles these in seconds, and the cost difference adds up fast.

In Claude Code, I'll drop to Sonnet (or use fast mode) when I'm in "grind mode" — the plan is set, the architecture is clear, and I just need to crank through implementation details.

Mechanical work — Haiku

This is the model most developers overlook, and it's a mistake. Haiku is almost instant and practically free, which makes it perfect for tasks that are repetitive and well-defined:

• Translations. I maintain my portfolio in English and German. Haiku translates UI strings accurately and fast — I just review and commit.
• Generating test boilerplate. Once I've written one test for a pattern, Haiku can replicate the structure for similar cases without burning Opus tokens.
• Formatting and transformations. Converting data shapes, generating TypeScript types from JSON examples, creating mock data.
• Pre-commit sanity checks. Run your diff through Haiku as a quick "does anything obviously wrong jump out?" pass before you push.

The mental shift: stop thinking of Haiku as "the dumb model" and start thinking of it as "the fast model." For well-specified tasks, speed matters more than reasoning depth.

Learning new concepts — Opus in chat, again

When I dove into LLM internals — tokenization, embeddings, attention mechanisms — I used Opus in long chat sessions. Same when I studied Bitcoin's proof-of-work and the Lightning Network. Same when learning Go fundamentals.

The pattern: I ask Opus to explain something, then immediately ask follow-up questions that connect it to what I already know. "How does this compare to how React handles X?" or "What's the Go equivalent of this TypeScript pattern?" Opus handles these cross-domain connections well. Sonnet tends to give more textbook answers.

For learning, the investment in Opus pays off because misunderstanding a foundational concept costs you far more time downstream.

Where other tools fell short — for me

I want to be fair here. These tools are good, and they might work better for your workflow.

GitHub Copilot was my first AI coding tool. The inline autocomplete is still best-in-class for small completions — finishing a function signature, suggesting a variable name. But it doesn't handle multi-file changes well, and it can't reason about architecture. I think of it as a very smart tab-completion, not a coding partner.

GPT Codex was impressive when it launched, but I found Claude's code quality consistently higher for TypeScript and React — fewer hallucinated APIs, better understanding of modern patterns (hooks, server components, etc.). That gap may have closed since I last tried it.

Cursor is the closest competitor to Claude Code in my experience. The IDE integration is nice, and the context management is good. I moved away from it mainly because Claude Code's terminal-based workflow fits better with how I already work — I live in the terminal. If you're an IDE person, Cursor might click better for you.

The real takeaway

The point of this post isn't "use Claude." It's this: treating AI model selection as an engineering decision — not a brand preference — makes you meaningfully more productive.

Every time you send a trivial task to Opus, you're wasting time and money. Every time you ask Haiku to architect a system, you're getting mediocre results and blaming "AI." The model is rarely the problem. The mismatch between model and task usually is.

Pick your tools intentionally. Match the model to the job. And don't be afraid to switch mid-session when the nature of the work changes.

---

I'm giving a talk at webinale 2026 on Human-Centered AI in frontend development — how to build workflows where AI supports your decisions rather than replacing them. If you're interested in this topic, come say hi.

Further reading

If the "match the tool to the task" framing resonates, ChemaJose Maria Valera Reales makes a complementary case in his four-part AI series. Part 1 — AI Gives You Speed, Not Quality — argues that velocity without ownership ends in unmaintainable code, no matter which model you pick. Good companion read: I'm writing about which model to use, he's writing about how to stay accountable once you pick one.