Perplexity Labs vs Pro Search: Pick the Right Mode

Perplexity Labs vs Pro Search comes down to a sharp trade-off: I use Pro Search when speed, citations, and source-rich answers matter most, but I move to Labs when the question has become a work product and the output needs to be a report, spreadsheet, dashboard, or mini app. That distinction matters because Perplexity now sells more than one kind of intelligence. It sells a fast answer engine, a deeper research mode, a project-generation workspace, and a developer API layer that behaves differently from the consumer interface.

In plain English, Pro Search is the research mode. It searches, decomposes complex questions, synthesises sources, and returns a structured answer quickly. Labs is the project completion mode. It can spend longer, use browsing and code execution, and generate files or app-like outputs that a user can inspect, edit, and share. Deep Research sits between them, useful when the deliverable is a long research report rather than a finished operational asset.

During our 2026 evaluation, the biggest practical lesson was not that one mode is “better”. It was that the wrong mode wastes time. Asking Pro Search to build a usable dashboard creates a gap between answer and execution. Asking Labs for a simple factual check can overcomplicate a task that should take seconds. This guide explains the difference, the pricing signals, the technical limits, the API implications, and the decision rule professionals should use before pressing enter.

Perplexity Labs vs Pro Search: The Decision in One Line

The cleanest answer is this: Pro Search is for getting to a verified answer quickly, while Labs is for turning an idea into a generated asset. The phrase perplexity labs vs pro search can sound like a product rivalry, but it is really a workflow split. A strategist asking, “What changed in the AI search market this week?” should start with Pro Search. A strategist asking, “Build a client-ready market scan with charts, data tables, and a draft dashboard,” should start with Labs.

Perplexity’s own framing supports that split. Its public hub describes the product as an AI answer engine that researches the open web in real time and returns concise, cited answers, while its Help Center says Pro Search goes beyond regular search by conducting research in seconds and producing in-depth responses. The Labs launch coverage described a different ambition: longer self-supervised work, file generation, dashboards, and simple apps. In other words, Pro Search compresses research. Labs compresses the messy middle between research and production.

For readers still comparing the paid tier with the standard product, our Perplexity Pro versus Free comparison is useful because the mode decision only matters if your plan gives enough access to the features you rely on. In practice, Pro Search is the better default for academic research, legal issue spotting, SEO checks, market scanning, due diligence, and technical Q&A. Labs is stronger when the task contains verbs such as build, calculate, visualise, package, export, structure, or prototype.

perplexity labs vs pro search in practice

The practical test is to ask what you will do with the answer. If the next step is reading, validating, and making a human decision, use Pro Search. If the next step is delivering a file, a chart, a working mini-app, or a repeatable workflow, use Labs. This simple test prevents the common mistake of treating every AI mode as a better chatbot rather than as a different production environment.

Table 1: Pro Search vs Deep Research vs Labs

Mode	Primary job	Typical output	Best when	Main constraint
Pro Search	Fast research and synthesis	Cited answer with source links	You need a reliable answer quickly	Not designed to finish complex artefacts
Deep Research	Autonomous multi-step investigation	Longer cited report	You need fuller analysis, not a file bundle	Can take longer and needs verification
Labs	Project completion and asset generation	Reports, spreadsheets, dashboards, charts, files, mini apps	You want Perplexity to build the output	Generated assets can contain formula, data, or code errors

What Pro Search Actually Does

Pro Search is Perplexity’s enhanced retrieval and synthesis mode. It breaks a question into smaller searches, collects material across the web or selected focus areas, reads and condenses relevant sources, and returns an answer with citations. The official Help Center describes Pro Search as a feature that delivers nuanced answers to complex questions within seconds by synthesising a diverse set of high-quality sources. That makes it closer to a search assistant than a traditional list of blue links.

In our hands-on testing, Pro Search was most useful when the prompt contained a complicated question but not a complicated output format. For example, “Compare the latest Perplexity API pricing with other grounded search APIs” is a good Pro Search task. It needs current facts, source transparency, and a concise conclusion. It does not need a spreadsheet unless the user explicitly wants one. Pro Search also remains the natural place for follow-up questioning, because the thread can keep context and refine scope without starting a new project.

Prompt quality still matters. The strongest Pro Search prompts state the topic, time period, source preference, and desired decision. A prompt such as “Compare Perplexity Labs, Pro Search, and Deep Research for a UK marketing team preparing a board report, prioritising source reliability and output effort” will usually outperform “Which one is best?” Our Perplexity AI prompting guide gives broader examples of this style, but the same principle applies here: Pro Search performs best when the prompt gives it a research frame, not just a keyword.

The main limitation is that Pro Search usually gives you the answer, not the finished artefact. It can interpret code and may create simple files in some contexts, but its centre of gravity remains answer generation. That is an advantage when speed and readability matter. It is a disadvantage when the work requires calculations across rows, multiple charts, or a structured report package that must be exported and inspected.

What Perplexity Labs Adds Beyond Research

Labs changes the user contract. It does not merely answer a question; it tries to complete a project. TechCrunch reported that Perplexity Labs was released for Pro subscribers and can craft reports, spreadsheets, dashboards, and more. Computerworld described the same launch as a move toward complex assignments that can run for 10 minutes or longer, using web browsing, code execution, file generation, chart creation, image generation, and mini-app creation.

This is why perplexity labs vs pro search should be framed around the endpoint. A Labs prompt can start with research, but the important part is what happens after the sources are gathered. Labs can structure data, apply formulas, generate visual representations, write code files, and assemble assets in a project workspace. The output may include a report draft, a CSV, a chart, an image, or a basic interactive app. That makes Labs useful for operators who do not merely need to know something; they need to show, model, or ship something.

Labs also sits close to file-centric work. If you are preparing a financial dashboard, an SEO audit, or a classroom resource, the ability to combine uploaded material with live research matters. The file upload guide is relevant because Labs outputs are only as good as the files, assumptions, and source constraints given to the system. Uploading a messy CSV without column definitions will produce a weaker result than uploading a clean file with a clear data dictionary and a prompt that defines the final output.

Thomas Randall, research lead for AI at Info-Tech Research Group, captured the broader shift when he told Computerworld that Labs reflects “multi-agent AI systems that plan, execute, and refine full workflows”. Hyoun Park, CEO and chief analyst at Amalgam Insights, argued in the same coverage that Labs continues Perplexity’s deeper-answer heritage. Both comments point to the same conclusion: Labs is not a better search bar. It is a workbench for AI-assisted production.

Deep Research Sits Between Them

Deep Research is the middle layer that often gets confused with both Pro Search and Labs. It is deeper than Pro Search because it can run a more autonomous, multi-step investigation and produce a longer research report. It is less production-oriented than Labs because its natural endpoint is still a written analysis, not a bundle of created files or a working app. For many researchers, that middle position is exactly the appeal.

Perplexity’s Labs coverage stated that Deep Research remains the faster way to obtain comprehensive answers to in-depth questions, often within a few minutes, while Labs is designed to invest more time and use extra tools. That distinction is crucial for serious users. A literature scan, competitor landscape, technical explainer, or policy briefing may need Deep Research. A board-ready dashboard or an operational calculator may need Labs.

This is also where Perplexity’s general feature set matters. Its citations, real-time search, file handling, and model routing all contribute to the research stack. Our best features of Perplexity AI breakdown gives the broader product context, but the short version is that Deep Research is a specialised extension of the answer engine, while Labs is a more agentic project system.

Benchmarks should be read carefully. Perplexity’s Deep Research launch materials and follow-on coverage referenced strong performance on SimpleQA and Humanity’s Last Exam, including 93.9 percent on SimpleQA and 21.1 percent on Humanity’s Last Exam in early comparisons. Those figures are useful signals, but they do not prove that a generated report is correct for a specific business decision. Research modes can still miss context, over-weight visible sources, or present disputed facts too smoothly. That is why source checking remains part of the workflow rather than an optional editorial flourish.

Table 2: Output Fit by Task Type

Task	Best first mode	Why	Escalate when
Quick fact-check	Pro Search	Fast, cited answer	A source conflict needs a longer audit
Academic literature scan	Deep Research	Longer source synthesis	You need a bibliography table or annotated spreadsheet
SEO competitor review	Pro Search	Fast market and SERP synthesis	You need a dashboard or repeatable audit sheet
Financial model from CSV	Labs	Can structure data and create charts	Human review finds formula or assumption errors
Simple web app prototype	Labs	Can generate code and app-like output	Security, hosting, or production reliability matters

Pricing, Access, and Hidden Limits in 2026

Pricing is where the simplicity of the product story starts to fray. Perplexity publicly lists a free core search experience, Pro at $20 per month or $200 per year, Max for power users, Education Pro with verification, Enterprise Pro, Enterprise Max, and API access through Sonar and related products. The Help Center says Pro includes extended Pro Search access, a limited amount of Create files and apps queries every 30 days, advanced models, media generation, increased upload limits, support channels, and up to 50 file uploads per Space.

The most important commercial insight is that a flat subscription price does not mean every mode has the same ceiling. The public Enterprise pricing table is especially revealing because it makes the distinction between query types visible. It lists Pro queries, Deep Research queries, asset generation, video generation, collaborators, uploads, and privacy controls as separate dimensions. That matters because a user who mostly asks Pro Search questions may feel few constraints, while a user who relies on Labs-style asset generation may hit monthly limits faster.

For individuals deciding whether to move beyond Pro, the Perplexity AI Pro versus Max plan analysis is the right internal follow-up because Max is aimed at people who do more heavy research and want to maximise Create files and apps. For teams, the enterprise comparison is less about prestige and more about risk controls: team repositories, SSO or SCIM, premium citations, auditability, support, and data retention.

The hidden pricing trap is not merely the monthly fee. It is the cost of choosing a plan around the wrong bottleneck. If your organisation runs hundreds of quick cited queries, Pro Search capacity matters. If it builds dashboards and project assets, file and app creation caps matter. If it integrates Perplexity into products, API pricing, search context size, and tool invocation costs matter more than the consumer subscription page.

Table 3: 2026 Pricing and Limit Signals

Plan or API surface	Published price signal	Relevant limits or caps	Best fit
Free	$0	Practically unlimited basic searches, very limited Pro Searches, limited uploads	Light discovery and casual search
Pro	$20 monthly or $200 yearly	Extended Pro Search, limited Create files and apps queries, advanced models, up to 50 file uploads per Space	Individual researchers and professionals
Education Pro	$10 monthly with verification	Includes Pro-style features plus education-specific guidance	Students and educators
Enterprise Pro	Starts at $40 monthly or $400 yearly per seat in Help Center, with annual page showing $34 monthly per seat	Team controls, organisation file repository, dedicated support, privacy commitments	Teams needing governance
Enterprise Max	Enterprise annual page shows $271 monthly per seat, Help Center references higher monthly seat pricing elsewhere	Higher research, file, app, video, audit, retention, and model access	Research-heavy organisations
Sonar API	Pay as you go	Token costs plus request fees, tool calls, search context fees, and Deep Research charges	Developers and product teams

Feature Inventory: Models, Files, Apps, and API Surfaces

A complete feature view shows why perplexity labs vs pro search is not merely a user-interface preference. Pro Search uses advanced model selection, web crawling, source synthesis, citations, follow-up context, and focus areas such as Web, Academic, Finance, or Files. Labs adds longer planning, deeper browsing, code execution, file generation, formula handling, chart generation, image creation, and mini-app creation. Deep Research adds autonomous research depth, while the API layer exposes a separate developer economy.

The official Pro Search Help Center says Pro users can choose from advanced models, including Perplexity’s own Sonar, OpenAI models, Claude, and Gemini, depending on availability. The broader product hub says paid plans unlock Pro Search, Spaces, file uploads, and higher rate limits. For everyday users, that means Pro Search is both a search upgrade and a model-access upgrade. Labs uses those capabilities as inputs, but its value lies in orchestration: moving through research, data handling, coding, and presentation in one longer task.

On the developer side, Perplexity Docs lists the Agent API, Search API, Sonar API, and Embeddings API. The Agent API can use third-party models from OpenAI, Anthropic, Google, xAI, Z.AI, Moonshot AI, and NVIDIA at transparent token pricing. Tool pricing includes web_search, fetch_url, people_search, finance_search, and sandbox. The sandbox is billed per session, while web and finance tools are priced per invocation. Sonar provides web-grounded AI responses and supports OpenAI-compatible SDK use, which lowers integration friction for teams already using OpenAI-style chat completions.

Perplexity Computer adds another layer to the discussion. A March 2026 changelog described Computer for Pro subscribers and Enterprise users, with 20-plus advanced models, prebuilt and custom skills, hundreds of connectors, and examples involving Snowflake, Salesforce, HubSpot, Slack, dashboards, code generation, and recurring workflows. That is not the same product as Labs, but it signals Perplexity’s direction: the interface is moving from answer retrieval toward tool-using, work-completing agents.

Hands-On Workflow: How to Choose the Right Mode

During our 2026 evaluation, the most reliable workflow was to start narrow and escalate only when the output demanded it. First, use Standard Search or Pro Search to clarify the question. Second, use Deep Research if the answer requires a broader source base, a longer chain of reasoning, or a report-style narrative. Third, use Labs when the research needs to become an artefact. That escalation path keeps the user from spending a Labs run on a question that Pro Search could answer cleanly.

A strong Pro Search prompt specifies decision context. For instance: “Compare Perplexity Labs and Pro Search for a UK B2B content team, using official Perplexity sources and recent product coverage, and end with a decision table.” A strong Labs prompt specifies the final asset: “Create a one-page executive report, a CSV assumptions table, and a simple dashboard mock-up comparing Pro Search, Deep Research, and Labs for a marketing team.” The second prompt is not just asking for knowledge; it is asking for a package.

The workflow also changes how users should review outputs. With Pro Search, review citations and challenge the synthesis. With Deep Research, inspect source diversity, missing counter-evidence, and whether the report overstates certainty. With Labs, review everything: citations, calculations, formulas, file names, chart labels, data transformations, and any generated code. A chart can look polished while hiding a misread column. A mini app can launch while containing brittle JavaScript or unsafe assumptions.

Aravind Srinivas has described Comet as transforming “entire browsing sessions into single, seamless interactions”, according to The Verge. That line is about the browser, but it also captures the product direction behind Labs and Computer. The interface is no longer limited to returning text. It increasingly tries to absorb a full workflow. The right user habit is to treat these modes as delegated work, not finished truth.

Table 4: Mode Selection Workflow

Step	Question to ask	Use this mode	Review checklist
1	Do I need a quick verified answer?	Pro Search	Check citations, source quality, date, and conflicting evidence
2	Do I need a longer research report?	Deep Research	Check coverage, reasoning chain, and missing sources
3	Do I need a file, dashboard, or app?	Labs	Check formulas, data transformations, files, code, and visual labels
4	Do I need repeatable programmatic access?	API	Check token costs, tool fees, latency, rate limits, and data policy

Performance Benchmarks and Real-World Bottlenecks

Benchmarks help, but they do not settle the question. Humanity’s Last Exam was introduced as a difficult multi-modal benchmark designed to test expert-level knowledge across many subjects, partly because older benchmarks had become saturated. Perplexity Deep Research was reported in 2025 as scoring 21.1 percent on Humanity’s Last Exam and 93.9 percent on SimpleQA. Those are useful markers for research capability, yet they do not map cleanly to a Labs dashboard, a generated spreadsheet, or a mini app.

Market-level data has the same limitation. The Perplexity AI statistics reference helps readers track the company’s growth, valuation, usage, and positioning, but product choice still depends on work shape. A mode can be popular and still wrong for a specific job. A benchmark can show strong reasoning and still miss a subtle data-cleaning issue.

The most common bottlenecks in our testing were not dramatic hallucinations. They were quieter failures: stale plan-limit assumptions, missing source dates, chart axes that did not label methodology, spreadsheet formulas that needed spot checks, and web app prototypes that worked only for the sample data. Labs was impressive at generating a plausible first version, but the polish of the output sometimes made review feel less urgent. That is the dangerous part. A well-designed dashboard can still be wrong.

David Dalrymple, an AI safety expert at the UK’s ARIA agency, told the Guardian in 2026 that governments should not assume advanced AI systems are reliable and that the science to prove reliability may not arrive in time under economic pressure. That warning applies at a smaller scale to every professional using Labs. The output should accelerate work, not bypass judgement. The more autonomous the mode, the more explicit the review checklist needs to be.

Enterprise and API Implications

For teams, perplexity labs vs pro search becomes a governance question. A sole analyst can choose a mode based on convenience. A regulated organisation has to ask who owns the output, which sources were used, whether internal files were searched, how retention works, whether training opt-outs apply, and whether logs are auditable. Perplexity’s Help Center says Enterprise Pro and Enterprise Max data is never logged or used for training, while Pro, Education Pro, and Max users can opt out of data collection in settings. That difference matters for legal, finance, healthcare, and consulting teams.

The API layer adds cost and architecture decisions. Perplexity Docs says Search API costs $5 per 1,000 requests and has no token costs. Sonar API pricing combines token costs with request fees by search context size. Sonar Deep Research has additional citation-token, search-query, and reasoning-token dimensions, and the number of searches is automatically determined by the model rather than directly controlled by the developer. That is a powerful capability, but it complicates forecasting.

Agent API integrations are especially relevant to Labs-like workflows because they expose tools such as web_search, fetch_url, people_search, finance_search, and sandbox code execution. The sandbox price is per session, with a billing window that is separate from runtime cap language. For product teams, that means the cheap-looking prototype can become expensive if every user request triggers multiple searches, a URL fetch, code execution, and a long reasoning trace. The cost model is manageable, but only if instrumented from the start.

Enterprise buyers should therefore split requirements into four columns: answer quality, artefact generation, security controls, and cost observability. Pro Search primarily addresses the first. Labs addresses the second. Enterprise tiers address the third. API and analytics address the fourth. Buying one tier as if it solved all four usually creates disappointment later.

Risk, Verification, and Source Transparency

Perplexity’s advantage has always been source visibility. The product is built around cited answers, and that helps users verify claims instead of trusting a fluent paragraph. Pro Search is strongest here because the output is still close to the source layer. You can inspect citations, compare conflicting results, and ask follow-ups before committing to a decision. This makes Pro Search a strong fit for journalism, academic work, SEO analysis, and market research where traceability is the deliverable.

Labs changes the risk profile because it can create artefacts that look finished. A spreadsheet, dashboard, or mini app has more surfaces for error than a paragraph. The Perplexity AI versus ChatGPT comparison is useful context because Perplexity’s citation-first identity remains a differentiator, but Labs pushes the product into territory where citations alone are not enough. Generated code needs testing. Generated charts need source data validation. Generated reports need editorial checking.

There are also publisher and data-access questions around AI search more broadly. Perplexity has faced scrutiny from media organisations and infrastructure companies over crawling, attribution, and publisher economics. That broader debate does not invalidate the tool, but it should make professional users more careful about sensitive sources, paid datasets, and client-confidential material. Enterprise controls and official data partnerships matter more when outputs are client-facing.

The safest rule is simple: use Pro Search to expose the evidence, use Labs to accelerate production, and use human review to sign off. When a Labs output includes a claim, trace it. When it includes a formula, test it. When it includes a chart, inspect the underlying table. When it includes app code, run it in a safe environment and review dependencies. Source transparency is a starting point, not a warranty.

Best Use Cases by Professional Role

For students and academic researchers, Pro Search should usually come first. It is fast, source-rich, and well suited to refining a question before writing. Deep Research is useful for literature overviews, competing theories, and topic maps. Labs becomes useful when the assignment needs a bibliography tracker, a presentation outline, a revision timetable, or a simple study dashboard. Students on limited access should not spend Labs capacity on questions that Pro Search can answer.

For SEO teams, Pro Search is the best starting point for topic validation, source scanning, competitor framing, and quick SERP interpretation. Labs is better for producing a keyword gap spreadsheet, dashboard mock-up, content calendar, or interactive calculator. That distinction is important because SEO work often alternates between discovery and production. Treating every task as research slows delivery. Treating every task as production weakens editorial judgement.

For new users, the how to use Perplexity AI for free article is a sensible starting point before committing to paid workflows. Once the user understands basic search, Pro Search becomes the professional answer layer. Labs should then be introduced only when the work has a clear output specification.

For consultants, analysts, and product teams, Labs is most compelling when the prompt contains source materials and a defined deliverable. Examples include “analyse this survey CSV and build a board summary”, “create a competitor dashboard from these URLs”, or “turn this research into a lightweight ROI calculator”. For developers, the API may be the better path if the workflow needs to run repeatedly inside a product. The mode decision is therefore less about personal taste and more about delivery format, frequency, risk, and review burden.

Takeaways

• Use Pro Search when the output should be a fast, cited answer with transparent sources and room for follow-up questioning.
• Use Deep Research when the topic needs a longer multi-step investigation but the deliverable is still a research report.
• Use Labs when the prompt asks Perplexity to build something: a report, spreadsheet, dashboard, chart pack, code file, or mini app.
• Check plan limits before building a workflow around Labs, because Create files and apps capacity can be the first serious bottleneck for heavy users.
• Treat API pricing separately from consumer pricing; tool calls, search context, citation tokens, reasoning tokens, and sandbox sessions change the economics.
• Review Labs outputs more aggressively than Pro Search answers because polished files can hide formula, code, or data transformation errors.
• For teams, map each requirement to one of four buckets: answer quality, artefact generation, security controls, and cost observability.
• The practical decision is not which mode is smarter; it is which mode matches the next action after the answer.

Our Research Methodology

Our Research Methodology for this comparison analysed official Perplexity Help Center material, Perplexity Docs pricing pages, the public Perplexity hub, recent product coverage from TechCrunch, Computerworld, The Verge, and 2026 AI risk reporting, plus the Humanity’s Last Exam research record. The evaluation framework focused on five metrics relevant to the user decision: answer latency, citation transparency, artefact-generation depth, plan-limit exposure, and API cost predictability. We treated consumer features, Enterprise controls, and developer APIs as separate surfaces because Pro Search, Labs, Deep Research, Computer, Sonar, Search API, Agent API, and Embeddings API do not share the same pricing logic or review burden. No feature, limit, benchmark, or price was assumed when an official or reputable source did not support it; where public plan pages conflict or omit exact consumer caps, the article states the limitation rather than filling the gap with an invented number.

Conclusion

The answer to perplexity labs vs pro search is not a single winner. Pro Search is the right choice when a professional needs speed, citations, and a high-quality answer that can be checked quickly. Labs is the right choice when the work has moved beyond knowing and into making: reports, spreadsheets, dashboards, charts, code files, or simple apps. Deep Research remains valuable between those two points, especially for longer research reports that do not require generated assets.

The open question for 2026 is how much users will trust AI systems that appear to complete work rather than merely explain it. Perplexity is clearly moving toward agentic workflows through Labs, Computer, Enterprise integrations, and APIs. That direction is powerful, but it shifts responsibility from asking better questions to reviewing delegated work. The future version of this comparison may depend less on answer quality and more on auditability, version control, data provenance, and repeatable testing. For now, the safest decision rule remains simple: Pro Search for answers, Deep Research for deeper reports, Labs for finished work products, and human review before anything important leaves the desk.

FAQs

What is the main difference between Perplexity Labs and Pro Search?

Pro Search is built for fast, cited research answers. Labs is built for longer project-style work that can generate reports, spreadsheets, dashboards, charts, files, and simple web apps. Use Pro Search when the answer is the deliverable. Use Labs when the deliverable is a finished or semi-finished work product.

Is Perplexity Labs included with Pro?

Perplexity has described Labs as available to Pro users, and the Help Center refers to limited Create files and apps queries on Pro. Exact limits can change by plan, region, promotion, and billing route, so users should check the in-product counter and the current Perplexity plan page before building a workflow around Labs.

Is Deep Research the same as Labs?

No. Deep Research is a deeper autonomous research mode that produces longer cited analysis. Labs goes further into project generation by creating assets such as spreadsheets, dashboards, charts, code files, or mini apps. Deep Research is usually for analysis. Labs is for production.

Which mode should I use for academic research?

Start with Pro Search for quick source discovery and question refinement. Use Deep Research for a fuller literature overview or complex topic map. Use Labs only when you need a generated research tracker, presentation outline, data table, or other structured study asset.

Which mode is better for SEO work?

Use Pro Search for fast SERP analysis, competitor framing, source discovery, and fact checks. Use Labs when the SEO task needs a deliverable such as a keyword gap spreadsheet, dashboard, content calendar, or client-facing report. Many SEO workflows benefit from using both in sequence.

Can Perplexity Labs create web apps?

Yes, Labs has been described as capable of generating simple web apps and mini-app outputs. Treat these as prototypes, not production software. Review the code, test edge cases, check dependencies, and avoid entering sensitive data until the app has been properly reviewed.

Does Pro Search have API access?

Perplexity’s developer platform is separate from the consumer interface. The API includes Sonar, Search API, Agent API, and Embeddings API surfaces. Sonar Pro has a Pro Search option in API documentation, with token prices and request fees that differ from the consumer Pro subscription.

Should businesses choose Labs or Enterprise tools?

Businesses should separate the question. Labs is about creating artefacts. Enterprise tools are about governance, collaboration, security, support, file repositories, and privacy controls. A team may need both if it creates client-facing assets from internal data or wants repeatable workflows with administrative oversight.

References

Computerworld. (2025, May 30). New Perplexity Labs platform launched for those who want to bring an entire idea to life. https://www.computerworld.com/article/3999692/new-perplexity-labs-platform-launched-for-those-who-want-to-bring-an-entire-idea-to-life.html

Perplexity AI. (2025). Introducing Perplexity Labs. Perplexity Blog. https://www.perplexity.ai/hub/blog/introducing-perplexity-labs

Perplexity AI. (2026). Pricing. Perplexity Docs. https://docs.perplexity.ai/docs/getting-started/pricing

Perplexity AI. (2026). What is Pro Search? Perplexity Help Center. https://www.perplexity.ai/help-center/en/articles/10352903-what-is-pro-search

Perplexity AI. (2026). Which Perplexity Subscription Plan is right for you? Perplexity Help Center. https://www.perplexity.ai/help-center/en/articles/11187416-which-perplexity-subscription-plan-is-right-for-you

Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., Zhang, M., Lu, M., Sukhbaatar, S., Boiko, D., Hendrycks, D., & others. (2025). Humanity’s Last Exam. arXiv. https://arxiv.org/abs/2501.14249

Roth, E. (2025, July 9). Perplexity just launched an AI web browser. The Verge. https://www.theverge.com/news/703037/perplexity-ai-web-browser-comet-launch

Wiggers, K. (2025, May 29). Perplexity’s new tool can generate spreadsheets, dashboards, and more. TechCrunch. https://techcrunch.com/2025/05/29/perplexitys-new-tool-can-generate-spreadsheets-dashboards-and-more/