I Tried the 7 Best AI Search Engines in 2025: Here’s What Works

Search has quietly undergone the biggest shift since Google launched. For years, the game was about typing keywords, skimming a page of blue links, and hoping one of them had what you needed. Now, AI search engines are collapsing that process into a single, generated answer—sometimes with citations, sometimes without—pulling from across the web and serving it up in seconds.

It’s a fundamental change in how people find and trust information. Instead of choosing which site to click on, users are often taking the AI’s first answer as the answer. That means the strengths and blind spots of each AI search engine matter a lot more than they did with traditional search.

To see how they really perform, I spent a week using seven of the most talked-about AI search tools—Perplexity, Google AI Mode, Arc Search, You.com, ChatGPT Search, Claude, and Reddit AI Search—for everything from research-heavy queries to breaking news, travel planning, and niche troubleshooting. I ran the same prompts across all of them, compared results, noted frustrations, and found a few surprises. If you want to outrank competitors in AI search, you first need to understand how each platform handles real queries, and that’s exactly what this week-long test revealed.

Why the old search experience no longer cuts it

The internet was originally built for connection and knowledge-sharing. Its early design was about linking people to information directly—no gatekeepers, no layers of commercial intent. If you needed an answer, you could often find it in a single, well-crafted page maintained by someone who cared about the subject.

Over the past two decades, that clarity has eroded. Search engines, once the clean utility that made the web navigable, have been reshaped by the economics of visibility. The rise of search engine optimization (SEO) turned ranking into a competitive sport. Pages became longer not because the topic demanded it, but because algorithms rewarded a certain word count. Articles started leading with “fluff paragraphs” to capture attention metrics. Keyword density, internal link structures, and “People Also Ask” farming replaced direct, user-first answers.

The effect is visible in almost any Google results page today. The first screen is dominated by ads, followed by affiliate-heavy listicles and content engineered to rank rather than to help. Even “informational” pieces are often thin rewrites of the same talking points, produced at scale to capture traffic and route it toward monetized outcomes. As one critic put it: SEO has made the web “less like a library, more like a strip mall.”

For users, the result is a slower, more frustrating search process. Precision has been replaced with noise. Finding a credible answer now often means opening multiple tabs, skimming past repeated sections, and filtering out bias introduced by affiliate incentives. This runs directly against what people increasingly want: a precise, context-heavy response that synthesizes the most relevant perspectives without forcing them to do the legwork.

AI-driven search engines are a reaction to this problem. By drawing on large-scale language models, they aim to bypass the detritus of SEO-driven content and bring GEO context into the equation, extracting and organizing the most useful parts of the web into a single, coherent answer—often with citations so you can verify the source. It’s not a perfect solution, but for many queries, it already delivers what traditional search has drifted away from: relevance, clarity, and speed.

How I tested each AI search engine

I ran each AI search engine through the same set of prompts to compare performance in three real-world scenarios:

• Research: complex topics that require synthesis and credible sources.

• Fact checks: direct questions where accuracy is critical.

• Deep dives: open-ended queries that test reasoning and breadth.

Each engine was scored on:

1. Speed: Time to produce a usable answer.

2. Accuracy: Factual correctness of the response.

3. Citation quality: Presence and reliability of source links.

4. Recency: Ability to surface recent news and developments.

5. Usability: Ease of interaction and clarity of output.

This produced a side-by-side view of where each engine is strong, where it lags, and which use cases it serves best.

How do the most popular AI search engines I tested compare?

Perplexity: Best for research and citation-heavy answers

Perplexity is the search engine I kept coming back to whenever I wanted something credible, structured, and easy to trace back to source material. It works like a hybrid of Google and ChatGPT: you ask a question, it searches the live web, and then it returns a conversational answer with inline citations. Each numbered source is clickable, so you can jump straight to the origin of a claim.

For example, when I asked “What are the current FAA drone regulations in 2025?”, Perplexity scanned recent government updates, summarized the key changes in bullet points, and linked directly to the FAA’s official page and two industry news sites. 

I could verify every single statement without leaving the search page. Compare that to a traditional Google result where I would have had to click through multiple sites—many of them SEO-heavy blogs repeating the same dated rules.

The conversational depth is another strong point. If you follow up with, “How do these rules compare to Europe’s drone laws?”, it understands the context and brings in European Aviation Safety Agency (EASA) sources for a side-by-side comparison.

The trade-off is speed when it comes to breaking news. If you ask about something that happened in the past hour—say, “Who won the 2025 Formula 1 Belgian Grand Prix?”—Perplexity may either take longer to find a definitive answer or give you a cautious, partial one. That said, for anything research-heavy, it’s the engine I’d trust most.

Google AI mode: Best for up-to-date and multimedia-rich answers

Google’s AI mode feels like the natural evolution of what the company has been inching toward for years—a blend of its massive search index with a generative reasoning layer. The real power is in how quickly it surfaces fresh content and how richly it integrates different formats.

When I asked “Show me the latest wildfire updates in California”, the AI mode didn’t just give me text. It pulled in an interactive map, embedded recent YouTube clips from local news stations, and cited live updates from Cal Fire’s incident page. This is where it beats most competitors: if something is happening now, Google can index it and show it in context within minutes.

That said, the old SEO skeleton is still there. Even in AI mode, some summaries clearly pull from articles optimized for rankings rather than relevance, meaning you might see rephrased fluff alongside genuinely useful snippets. For example, when searching “best hybrid cars 2025”, the AI mode generated a neat summary but also included product descriptions lifted from affiliate-heavy listicles.

Where it shines is in multimedia context. If you need both text and visuals—like checking the design changes in the newest iPhone—Google’s AI mode can serve images, videos, and product specs in one blended answer.

Arc Search: Best for mobile-first, distraction-free browsing

Arc Search is the AI search engine that feels the most different in practice—mainly because it’s a browser first, search engine second. Its “Browse for Me” feature is deceptively simple: you enter a query, it visits multiple sites in the background, and then it hands you a clean, ad-free summary in a single scrollable page.

I tested it on my phone while researching “Best places to eat in Kyoto that aren’t tourist traps.” Instead of clicking through blog after blog with intrusive ads and pop-ups, Arc gave me a neat list with short descriptions, average prices, and why each spot stood out—compiled from several travel articles and Reddit threads. The result felt like something a human assistant might have prepared after an afternoon of reading.

The other big plus is speed. On mobile, “Browse for Me” consistently returned summaries in under ten seconds, complete with relevant images and source attributions at the bottom. The desktop version works too, but it doesn’t yet feel as fluid or natural; Arc’s strength is clearly in the handheld experience.

Its limitation is that you sometimes lose depth. Because it condenses aggressively, you might miss the nuance you’d get from reading the full source—though the original links are there if you want them.

You.com: Best for personalization and switching between AI models

You.com is the only search engine in my testing lineup that felt like a control panel for AI rather than a fixed product. At its core, it offers three distinct “AI Modes”:

• Smart: a balanced, general-purpose mode.

• Research: geared toward fact-heavy queries with citations.

• Genius: optimized for creative problem solving and ideation.

Beyond that, You.com lets you switch between large language models on the fly—GPT-4, Claude 3.x, Gemini—without juggling separate logins or subscriptions. In practice, this meant that when I asked “Draft an outreach email for a software demo”, I could first run it through GPT-4 for polished language, then instantly re-run the same query with Claude for a more nuanced, conversational tone.

Its search capabilities are visually rich. For “Explain the differences between lithium-ion and solid-state batteries”, the Research mode gave me a detailed table comparison, complete with technical specs, lifecycle estimates, and citations from engineering journals. 

Switching to Genius mode yielded a plain-English analogy comparing the two to “a high-performance sports car vs. a safe, reliable family SUV”—handy if you need both expert depth and audience-friendly framing.

The drawback is that the interface can feel crowded. Between the model picker, mode selector, app integrations, and inline results, minimalists may find it overwhelming. But if you’re a tinkerer who wants granular control over how your AI searches behave, You.com is unmatched.

ChatGPT Search: Best for conversational exploration

ChatGPT Search works exactly the way you’d expect from OpenAI—conversation first, everything else second. You start with a question, and instead of giving you a static block of text, it engages in a back-and-forth, remembering your context across turns. This makes it ideal for queries that evolve as you dig deeper.

I tested it with “Outline a 1-week beginner’s Python course”. 

ChatGPT Search gave me a structured daily plan with links to beginner-friendly resources, then adapted instantly when I said, “Make it project-based and add a day on API integration.” That fluid adaptability is its biggest strength.

It also integrates citations and synthesis directly into answers—though not as rigorously as Perplexity. For “Summarize the latest IMF global economic outlook”, it referenced IMF press releases and major news outlets, but the citations were often embedded as plain text rather than numbered, clickable footnotes.

User behavior backs up my own testing. A TechRadar report noted that 77% of U.S. users rely on ChatGPT as a faster, more conversational alternative to traditional search. In my experience, that’s accurate—when I needed to brainstorm ad copy variations or troubleshoot a regex pattern, it was simply faster to “talk it out” with ChatGPT than to piece together answers from multiple search results.

The main weakness is the occasional hallucination. When I asked for population figures for a specific rural county, it confidently gave me numbers that didn’t match official census data. For critical facts, you’ll still need to double-check.

Claude: Best for coherent, in-depth explanations

Claude’s approach to AI search is calm, deliberate, and methodical—sometimes to a fault. Anthropic offers three search-related modes:

• Web search – pulls recent sources into responses.

• Extended thinking – allows the model more “thinking time” for complex queries.

• Research – structures output into formal, citation-backed answers.

When I tested “Compare the climate adaptation policies of Japan and the Netherlands”, Claude’s Research mode produced a deeply coherent answer that read like an analyst’s briefing—clear subheadings, bullet points for each policy measure, and direct links to government reports. Switching on Extended Thinking added a section on historical flood management that wasn’t in my original prompt but enriched the context.

In coding scenarios, it proved equally strong. A prompt for “Write a Python function that batch-processes CSV files and outputs JSON” yielded clean, well-commented code—and when I asked for a memory-optimized version, it rewrote the function to stream files instead of loading them all into memory. Programmers on Reddit have echoed this, saying they find Claude 3.5’s explanations more natural and context-aware than GPT-4’s.

The downside is verbosity. If you’re after a quick fact—say, “What’s the GDP of Sweden?”—Claude will wrap the answer in multiple paragraphs of context. That’s not a flaw for researchers, but it’s overkill for quick lookups.

Reddit AI Search: Best for community-driven, experience-based answers

Reddit AI Search (branded “Reddit Answers” in some rollouts) taps into Reddit’s vast network of niche communities and condenses relevant discussions into bullet-point summaries. Instead of linking you to a single subreddit thread, it surfaces a synthesized answer based on multiple posts, quoting key points and highlighting the most upvoted or repeated advice.

For example, when I searched “Best beginner cameras for wildlife photography”, Reddit AI Search pulled perspectives from r/photography, r/birding, and r/wildlifephotography. The result included a short list of recommended models, common pitfalls for beginners (like underestimating lens weight), and a note that many users preferred buying used gear. This saved me from clicking into—and then getting lost in—long discussion chains.

The technology isn’t flawless. Because it draws from human conversation, the quality of the answer depends heavily on the depth and moderation of the underlying communities. Ask a question that’s only been addressed in low-traffic or poorly moderated subreddits, and you may get thin or biased responses.

Strategically, Reddit is doubling down on this. According to TechCrunch, it’s unifying its AI search features into the main app, aiming to become a primary search destination rather than just a place people land after a Google query. That move makes sense: for many niche or experience-based questions, Reddit is already where people end up. If you’ve ever typed a question into Google and clicked on a Reddit link halfway down the results page, you’ve already experienced why this shift matters—Reddit is often where the most practical, first-hand advice lives.

AI integration simply removes the extra steps. Instead of opening five different threads, skimming for relevant comments, and piecing together the consensus yourself, Reddit AI Search does that work in seconds. It distills the collective wisdom of a community into a digestible format, while still linking back to the original discussions if you want to dig deeper or verify context.

That makes it especially valuable for decisions that benefit from lived experience rather than abstract facts—things like “How do I train for my first marathon without getting injured?” or “Which budget espresso machine is actually worth it?” You’re not just getting information; you’re getting it from people who’ve been there.

What surprised me after using them side by side

After a week of running the same questions through all seven AI search engines, I came away with a few surprises—and a couple of disappointments.

The biggest surprise was how often Perplexity outperformed Google AI Mode for research-heavy queries. I expected Google’s unmatched index to dominate, but in practice, Perplexity’s clean citations and focused summaries made it easier to verify facts and move on. On the flip side, I thought Arc Search would be more of a niche tool, but on mobile it became my go-to for quick, clutter-free lookups.

There were disappointments, too. ChatGPT Search is excellent for brainstorming and multi-turn exploration, but when I needed bulletproof facts—like current government policy details—it sometimes slipped in outdated or inaccurate information. Claude impressed me with depth, but its tendency to over-explain slowed me down when I just wanted the answer fast.

As for traditional Google, it still wins in certain cases—especially when you need real-time indexing or niche multimedia content like high-resolution images, embedded videos, or maps. If I wanted to see photos from an event that happened an hour ago, Google was the fastest route. But for almost anything else, especially where precision and synthesis matter, it clearly lagged. Its AI summaries help, but they still pull from the same SEO-heavy content that made me look at alternatives in the first place.

The main takeaway? No single engine is perfect. Perplexity gives me the best research output, Google AI Mode covers breaking news and visuals, Arc Search wins on mobile, Claude handles in-depth analysis, ChatGPT is best for brainstorming, You.com is ideal for model-switching, and Reddit AI Search captures real-world experience. Mixing them—almost like building your own “search stack”—proved far more powerful than committing to just one.

How to make the most of AI search engines as a marketer

After testing these search engines side by side, one thing became clear: they’re not just changing how people find answers—they’re also shaping how brands are perceived in real time. When a buyer asks ChatGPT or Perplexity for “the best [your category] tools,” the model decides which brands to include, how to describe them, and in what order. Sometimes you get a fair, accurate mention. Sometimes you don’t appear at all.

For instance in this search across GPT, Perplexity and Claude, for “best drag and drop design platform for small business,” Canva is left out and one could argue that they deserved to be cited at least.

Most teams have no visibility into this. CMOs and SEO leads have told us the same thing: they’re investing in AI content and PR, but they don’t actually know what’s being said about their brand in these answers—or whether they’re being mentioned at all. That’s a big shift from traditional search, where you could at least see your rankings and optimize against them.

Probe Analytics is built to close that gap. Disclaimer: This is more than just a GEO tool. It lets you search the way your buyers do, across ChatGPT, Claude, Gemini, and Perplexity, and shows you exactly:

• Whether your brand is being mentioned.

• How you’re being positioned.

• Which competitors are taking your spot.

From there, you can dig into the why:

• A Prompt Table shows your share of visibility for each tracked query.

• Mention graphs break results down by model, topic, and intent.

• Sentiment analysis tells you if the tone is positive, neutral, or negative.

• Alerts flag when you’re added, removed, or misrepresented in an answer.

Probe also shows why some brands keep winning prompts and others don’t, with daily tracking of your most important buyer-intent queries across models. You can see how your position changes over time, which citations are driving mentions, and when a competitor displaces you.

And unlike most tools, Probe doesn’t stop at visibility. It connects those mentions to actual site traffic—by model, by prompt, by landing page—so you can see if your AI search presence is actually generating visitors and conversions. 

That way, you can spot high-performing assets, identify pages with visibility but no clicks, and refine your strategy where it counts.

In short: it’s the same lesson I found testing these AI search engines. No single platform tells the whole story—you need the right combination of tools and data to really understand where you stand. Probe just does that for the AI search layer.