Every "which LLM is best" benchmark misses the same thing: in production, you're not picking a winner, you're picking a toolbox. After shipping AI-backed features with Claude, OpenAI, and Gemini across a SaaS product and a Chrome extension, I've stopped thinking about them as competitors and started thinking about them as specialists.
This isn't a leaderboard. It's my actual default per use case, and why.
Claude: long-context reasoning and code-shaped tasks
Claude is my default when the prompt is long, the reasoning is chained, or the output has structure I care about. Summarizing a 30-page spec, generating code that has to match existing patterns, running agentic flows with tool calls - this is where I reach first.
Two things make it stick: it handles long contexts without quietly losing the middle, and it's less likely to "helpfully" invent API shapes that don't exist. That matters when the output feeds back into code.
Trade-offs: it's not the fastest to first token, and for short, simple classification tasks I can get the same quality cheaper elsewhere.
OpenAI: speed-sensitive paths and structured outputs
OpenAI is my default when latency is a user-visible problem. Autocomplete-style UX, inline suggestions, anything where the user is waiting and watching tokens stream. The ecosystem is also the most mature - structured outputs, function calling, and SDKs are boring in the good way.
It's also what I reach for when I need a reliable "fill this JSON schema" response with no excuses. Less prompt-engineering, fewer retries, more predictable billing.
Trade-offs: on very long contexts it gets pricier fast, and for open-ended reasoning I still prefer Claude's shape.
Gemini: massive context and multimodal input
Gemini earns its slot on two axes. First, the context window is silly-large, so "here's the entire codebase, answer questions about it" workflows are genuinely cheap. Second, multimodal - I've used it for extracting structured data from screenshots and PDFs in Chrome extension flows, and the quality has been the one that surprised me.
Trade-offs: for pure code-shaped output I still nudge back to Claude, and the SDK ergonomics are slightly rougher than OpenAI's.
A minimal routing pattern
In production I don't hardcode a provider anywhere user-facing. A tiny router keeps model choice swappable:
tstype Task = "code" | "classify" | "longdoc" | "extract"; const routes: Record<Task, () => LLM> = { code: () => claude("sonnet"), classify: () => openai("gpt-fast"), longdoc: () => gemini("pro"), extract: () => gemini("pro"), }; export const run = (task: Task, input: string) => routes[task]().complete(input);
Four lines of routing, one swap if a provider ships something better next week. No feature team has to care which model backs their call.
Model choice is a dependency, not a religion
The mistake I made early on was picking a favorite and bending every feature to fit it. That ages badly. Models improve unevenly - Claude jumps ahead on code, OpenAI ships a faster tier, Gemini cuts prices on long context - and your product shouldn't need a rewrite each time.
Treat model choice like you'd treat a database driver or a payment provider: encapsulate it, pick per job, and make it boring to swap. The best production AI stack I've built isn't the one using the "best" model. It's the one where I could stop caring which model I'm using and ship the feature.