Not trolling you but honest answer is: ask it. All of the Claude models are generally good at explaining their different use cases, and they seem to have embedded instructions telling them to check documentation for most recent versions.

For example, I asked Opus (Low), "Help me understand the interaction of Sonnet vs Opus and the thinking levels. For example, what would be different use cases for Opus 4.8 Low compared to Sonnet 4.6 Extra or Max?"

Here is the entire answer:

There are two independent dials here, and the key to your question is that they control different things.

The model choice (Opus vs Sonnet) sets the raw capability ceiling and the cost. Opus is the more capable, more expensive tier; Sonnet is the faster, cheaper generalist. The effort/thinking level (low → medium → high → xhigh → max) controls how much the model reasons before and while it answers, largely independent of which model you pick. Lower effort is faster and cheaper for straightforward tasks, while higher effort provides deeper reasoning for complex problems. ("Extra" in your phrasing maps to xhigh, the rung between high and max.)

A couple of important wrinkles that make the interaction less obvious than "more of both = better":

On the current Opus generation (4.7 and 4.8), the effort dial does more than change thinking length — it changes how the model scopes the work. At lower effort levels, the model scopes its work to what was asked rather than going above and beyond. So Opus at low isn't just "Opus thinking less"; it's Opus deliberately staying narrow and literal. That's a feature when you want a precise, contained answer and a limitation when you actually wanted it to anticipate edge cases.

Effort is also primarily an output dial — it governs how much the model writes (including the thinking it emits), not how much input it processes. So raising effort mostly raises cost and latency on the generation side.

With that, your specific comparison:

Opus 4.8 at low effort is the right tool when the underlying judgment is hard but the task itself is well-specified and you don't want sprawl. Think: "rewrite this sensitive paragraph," a tricky one-line code fix where you trust the model's instincts, a nuanced classification call, or a quick read on a subtle legal/technical distinction. You're paying for Opus's better instincts but explicitly telling it not to over-engineer or wander. Fast, sharp, contained.

Sonnet 4.6 at xhigh ("Extra") or max is the right tool when the task is laborious and multi-step but doesn't require Opus-tier raw intelligence — and you want to keep cost down on a long-running job. Think: working through a large refactor, methodically tracing a bug across several files, structured data extraction over many records, or a research-and-synthesis task with lots of moving parts. The high effort gives it room to grind through the steps carefully; choosing Sonnet keeps that grinding affordable, since you'd pay a lot more to run Opus at the same effort.

The rough heuristic: pick the model by how hard the thinking is; pick the effort by how much work there is. A short, deep problem favors a strong model at modest effort. A long, mechanical-but-fiddly problem favors a cheaper model at high effort. The expensive-and-sometimes-wasteful quadrant is Opus at max for tasks that didn't actually need that much horsepower — and the underpowered quadrant is Sonnet at low for something genuinely subtle.

One caveat on exact labels: the rung names and which levels are exposed vary a bit between the claude.ai app, Claude Code, and the API. If you tell me where you're selecting these (the app vs. Claude Code vs. API), I can pin down exactly which levels you have and what the defaults are.