If you’ve ever opened Perplexity’s model menu — Sonar, Claude, GPT-5, Gemini, with or without the “Thinking” setting — and hesitated, you’re not alone.
Many people either leave it on “Best” or pause, unsure whether their request really needs the upgrade. It’s like standing in front of a row of espresso machines and debating which one makes the right cup, when the real decision is about how much caffeine you want for your current state of mind.
Most of the time, the decision isn’t about identifying the absolute best model. It’s about pairing your mental mode with the engine that makes sense for it.
Before you type a prompt, you’re already making choices: which model, which search setting, how much detail to expect. That stack of decisions adds friction. And since Perplexity doesn’t make the router logic obvious, defaulting to “Best” can feel safe. But that option is built to manage efficiency on Perplexity’s side, not to guarantee the best fit for your task.
what’s a results engine?
Every Perplexity answer combines two parts:
Search: the platform gathers fresh, relevant data.
Model synthesis: the selected model (Sonar, Claude, GPT-5, Gemini, etc.) interprets those results to form a response.
Together, that pairing creates what I call the results engine. It’s not just about which model you pick but how search and reasoning work together.
Most chat-based AIs rely on a foundation model trained on static data and add a retrieval layer afterward. Perplexity starts with retrieval and integrates synthesis from the beginning. That design means every response is shaped by real-time data rather than solely by the model’s prior training.
why “best” isn’t what you think
The “Best” setting isn’t choosing the most capable model for your query. It’s optimizing infrastructure.
By default, Perplexity routes most requests to Sonar, its in-house model. Only when the system detects added complexity does it escalate to Claude, GPT-5, or Gemini. For quick lookups, Sonar is fast and solid. For more technical or strategic questions, leaving the choice on “Best” often produces answers that feel thin.
If you need higher quality, it’s better to direct the engine yourself.
a one-question framework
Here’s the filter that removes second-guessing:
“Am I thinking clearly and ready for depth?”
When you’re distracted or just browsing, light settings are enough. When you’re sharp and working through a complex problem, shift to a heavier engine.
That single question links your state of mind to the right tool.
the framework in practice
I match engines to my focus level rather than toggling endlessly:
low focus → search + Sonar
Good for quick lookups, drafts, and surface-level tasks.
Examples: “Summarize today’s AI news.” “List top headlines about competitors.”
medium focus → research mode (Sonar Deep Research)
This variant of LLaMA (AI model) is tuned for organization and structured analysis.
Examples: “Draft a competitor comparison.” “Break down this workflow for a presentation.”
high focus → Claude 4.5, GPT-5, or Gemini
Best for strategy documents, technical breakdowns, and step-by-step reasoning.
Examples: “Write a product strategy outline.” “Audit this process with detailed logic.”
For me, about 70% of work sits at default, 20% in research mode, and 10% in high-focus engines.
quick reference: core models
Sonar — Fast search, quick citations, strong for summaries and snapshots
Claude Sonnet 4.5 — Nuanced writing, polished style, strong code reasoning
Claude Sonnet 4.5 Thinking — Technical audits, step-by-step reasoning, complex problem solving
Google Gemini 2.5 Pro — Multimodal analysis, image/data handling, advanced reasoning
GPT-5 — Big-picture synthesis, expert-level writing, creative ideation
GPT-5 Thinking — Step-by-step logic, deep analysis, risk reasoning
You don’t need to memorize the chart. Just learn which model aligns with the type of task you handle most often. Personally, I’ve leaned heavily on Claude Sonnet 4.5 since its release because it balances speed, nuance, and coding strength.
pitfalls to avoid
Assuming “Best” equals most accurate. It usually means Sonar unless the system escalates.
Testing every model on every query. That cycle wastes time. Choose one or two engines you trust and stick with them.
Skipping “Thinking” versions. These provide transparent, step-by-step logic that’s especially useful for technical or strategic work.
Perplexity doesn’t demand complicated prompting, but it does reward awareness of your own mental state.
your headspace matters more than the model
Most productivity issues start before the prompt: using the wrong tool for your level of focus. This framework ties your mental mode to the right engine and reduces wasted effort.
Next time you open Perplexity, pause and ask:
“What’s my mental mode right now?”
Then pick the engine that matches. Doing this saves time, improves accuracy, and builds confidence in your workflow.
The models will keep evolving, but the core job is unchanged: get results.
Clarity on your own state is the best way to stay efficient without burning out.