Perplexity: live web search with cited sources

Perplexity answers questions with real-time web search instead of frozen training memory, and it backs every reply with links to its sources. On Genosai it runs with a 120,000-token context window, and one request costs 1.5 credits. Choose it when fresh, verifiable facts matter more than polished creative writing.

Updated: July 7, 2026

• Real-time web search — Searches the live web at request time and answers from current pages instead of outdated training memory.
• Cited sources — Every claim carries numbered citations with URLs so you can verify each statement yourself.
• Fast answers — A lightweight model optimized for speed, ideal for question-and-answer and reference tasks without deep analysis.
• 120,000-token context — Fits long questions, dialogue history, and retrieved page snippets in one reasoning window.
• Low-cost tariff — One request costs 1.5 credits, a cheap way to get verifiable answers with sources at scale.

Contents

• What is Perplexity
• Capabilities
• Examples prompt and response
• How to use on Genosai
• Prompts
• How it compares
• Limitations and tips
• FAQ

What is Perplexity

Perplexity is a new kind of text model that answers not from frozen training knowledge but by first searching the live web and building its reply from what it finds. The key difference from a regular language model is that, before generating, it runs a real-time web search, selects relevant sources, and inserts numbered citations into the answer. You get not only text but the addresses of the pages each figure or claim came from.

This approach solves the main pain of classic models: stale data. Where an ordinary model may not know recent events, Perplexity relies on what the web returns right now, including fresh news, updated prices, latest releases, and current statistics. That is why it powers reference scenarios where accuracy and freshness matter more than style.

Another side effect of live search is fewer fabrications. A classic model that does not know an answer sometimes confidently generates a plausible but false fact. Perplexity instead grounds its reply in specific pages and shows where the information came from, which makes it easier to control. You immediately see which source each statement rests on and can discard a dubious point. This does not remove the need for verification, but it noticeably raises trust in the answer on fact-based tasks.

Perplexity is the lightweight, fast member of the family, tuned for speed and low cost. It handles focused question-and-answer tasks well: explain a term briefly, gather three or four points on a topic, or produce a short reference with links. For deeper multi-step research with more sources, the catalog offers the senior version, Perplexity Pro, which cites roughly twice as many sources per search on average.

Capabilities

Live search and citation

Perplexity's main capability is real-time web search. The model composes search queries, reads the results, filters and re-ranks them, then builds an answer from what it found. Every meaningful fact is tagged with a footnote link, and you see the source URLs at the end. This makes answers verifiable: you do not have to take the model at its word, you can open the original page.

Fresh data instead of stale memory

Because the model reaches the web at request time, it answers from the current state of the world. That is essential for questions about prices, news, recent research, and changes to laws and services. Where an ordinary model replies from year-old data, Perplexity pulls what is available today.

Speed and low cost

Perplexity is designed as a fast model for streaming scenarios: support chatbots, embedded hints, and reference widgets. On Genosai a single request costs just 1.5 credits, which makes it an inexpensive tool for regular fact-based answers. The 120,000-token context window fits a detailed question, dialogue history, and retrieved page snippets.

Flexible tuning

The Genosai studio exposes temperature and top_p controls, a system-prompt field, and a response-length limit. Lowering temperature yields drier, more predictable references; raising it gives freer phrasing. The system prompt helps set a role and format, for example asking the model to answer as a list with mandatory citations.

Response formats and use cases

Perplexity handles a short reference, a bulleted list of points, or a coherent paragraph with inline source links equally well. That makes it easy to embed across scenarios: a reference widget on a site, a support-desk assistant, a marketer's helper for gathering fresh market data, or a journalist's tool for quick fact-checking. Because every answer arrives with links, it works well as a starting point for deeper manual verification. A common pattern is to ask a question, get a set of sources, then refine the query by narrowing the topic or adding a period and region. Over a few iterations you assemble a compact, verifiable set of facts without manually combing through search results.

Examples prompt and response

Below are real Perplexity answers with live search and cited sources. Note the numbered footnotes and how the model attaches links to each point.

Prompt: What are the main e-commerce trends being discussed in 2026? Give 3–4 points with source links.

Perplexity response:
The main e-commerce trends being discussed in 2026 are **AI-driven agentic commerce**, **hyper-personalization**, and the rise of **social commerce with unified platforms**.

Here are 3–4 key points with source links:

1. **Agentic Commerce and AI Agents**: AI is shifting from simple customer support to actively driving revenue by acting as autonomous "agents" that help customers discover and buy products without human intervention.
   Source: [Akeneo – 5 Trends That Will Shape the 2026 eCommerce Landscape](https://www.akeneo.com/blog/2026-ecommerce-trends/) [1] | [Search Engine Land – Top 4 ecommerce trends for 2026](https://searchengineland.com/guide/top-ecommerce-trends-2026) [3]

_Generated on Genosai.io with the Perplexity model._

Prompt: Briefly: what is a marketplace buy-box and why does it matter for online sellers?

Perplexity response:
A **marketplace buy-box** (most commonly called the **Buy Box** on platforms like Amazon and Walmart) is the **featured offer section** on a product detail page that contains the primary **"Add to Cart"** and **"Buy Now"** buttons, where the marketplace's algorithm displays **only one seller's offer** as the default purchase option when multiple sellers list the same item [1][2][4].

It matters critically for online sellers because **over 80% of all marketplace sales** (and even more on mobile) go through this box, meaning customers typically buy from the featured seller **without even seeing alternative options** [1][3][4].

_Generated on Genosai.io with the Perplexity model._

How to use on Genosai

1. Open Genosai.io and sign in; new accounts get starter credits to begin.
2. Go to the text-model studio and pick Perplexity from the model list.
3. State your question as specifically as possible and, if needed, ask for a list answer with source links.
4. Optionally expand the parameters and set temperature, response length, and a system prompt for your format.
5. Send the request, which costs 1.5 credits, and wait for the streamed answer with numbered citations.
6. Check the sources via the links and refine your question to deepen or narrow the topic.

Prompts

Collect the 4 top news items on "electric vehicles in Europe" from the last month. One sentence and a source link per item.

What is a carbon footprint in plain words? Give a definition and 3 facts with current figures and source links.

Compare two mobile phone plans using current operator data and cite a source for every number.

Find the latest changes to product labeling rules in 2026 and list them with source links.

Briefly explain what is happening with a chosen currency rate right now and provide a link to a current source.

Gather a list of 5 research papers on "sleep and productivity" with short takeaways and source links.

Answer a customer question about product returns based on current rules, and attach links to the sources.

Generation cost

On Genosai Perplexity is billed by tokens — you pay for the actual size of the prompt and the answer, so a short question is cheaper than a long analysis. A typical request costs roughly 1.5 credits. Because the model queries the live web, a request with search costs more than a plain one. The final amount depends on prompt length and answer size.

Starter credits after sign-up let you try Perplexity for free, and top-ups work with local cards without a VPN. See current rates and balance on the Pricing page.

How it compares

Perplexity is strong where you need a fresh, verifiable fact with a link, but it trails classic models on creativity and long-form writing. Here is how it lines up against catalog neighbors.

If a query needs not just facts but multi-step analysis with many sources, take Perplexity Pro. For creative and coding tasks without a web dependency, GPT-5.4 and Claude Sonnet 4.6 fit better. You can compare every model in the shared model catalog.

A practical rule of thumb: if the question contains words like "now", "latest", "recent", "current", or "in 2026", Perplexity almost always wins, because live search is what you need. If the question is about logic, style, long text, or code where web data is irrelevant, a general-purpose model answers better. Many users keep Perplexity and a general model side by side and switch between them depending on what matters most in the task at hand.

Limitations and tips

Perplexity's main limitation follows from its strength: it is tuned for facts and search, not creativity. For creative writing, long free-form prose, poetry, role-play, and complex programming, classic language models usually deliver better results. If a task does not need fresh data from the web, the live-search advantage is wasted, and a general-purpose model is the smarter pick.

Answer quality depends heavily on how you phrase the question. Ask for specifics: state the period, region, and answer format, and explicitly request source links, so the model picks the right pages and citations. Remember that sources on the web can contradict each other, so cross-check several links on important topics rather than trusting a single point.

Mind the platform limits: on Genosai Perplexity does not process images or files, it is a text model for search and answers. Context is capped at 120,000 tokens, so break very long documents into parts. For deeper research with more citations, switch to Perplexity Pro; for tasks without web search, use the general-purpose models in the catalog.

FAQ

How is Perplexity different from a regular language model?

A regular model answers from the data it was trained on and may miss recent events. Perplexity searches the live web before replying and cites its sources. That makes it strong on fresh facts, prices, and news.

Does Perplexity show the sources for an answer?

Yes. Replies include numbered citations like [1][2] plus the URLs of the pages the information came from. This lets you verify each claim and reference the original source in your own work.

What context and price does Perplexity have on Genosai?

On Genosai the model runs with a 120,000-token window, and one request costs 1.5 credits. That is enough for a detailed question, dialogue history, and retrieved page snippets at once.

Can Perplexity work with images?

No. On Genosai Perplexity does not process images or files, it is a text model for search and answers. For image work, pick a multimodal model from the catalog instead.

When should you pick a different model instead of Perplexity?

For creative writing, long free-form prose, role-play, and complex code, classic LLMs are usually stronger. Perplexity wins where you need current, verifiable facts with sources rather than imagination.

Do you need a subscription to try Perplexity?

No. Perplexity is available right in the Genosai studio on a credit model: you spend 1.5 credits per request. Just open Genosai.io, pick the model, and ask your question.

Try Perplexity on Genosai