Perplexity Computer vs Claude Code vs Cowork vs Manus: Tested Side by Side (2026)

I gave Perplexity Computer, Claude Code, Cowork, and Manus the same two tasks, tracked every dollar, and had AI rank the outputs. Here's which agentic AI tool to choose for your work.

The agentic AI world is moving fast. First it was Manus and Claude Code, then Claude Cowork. Now Perplexity just dropped Perplexity Computer. They all want to be the tool you can't live without. But how do you know which one to pick for your work? And how do they compare when you put them to work?

Instead of telling you which one is 'the best' based on feature lists, I did what I always do: I tested them side by side.

I gave all four of them the exact same two prompts, tracked the costs, compared the outputs, and then built a tool to determine a winner. An LLM Council, four different AI models that judged all four outputs blindly and ranked them.

This started from a thread in our community chat. I wanted to do a deep dive on Perplexity Computer, but most of you voted for the comparison article first.

And it makes sense. I assume some of you reading this also wonder which agentic tools to invest in when you keep hearing the news and seeing how cool they all are. The temptation is big, but so is the bill for getting all of them.

So this is what we’re doing today.

---

Here’s what we’ll cover:

• What are agentic AI tools (and why should you care)?

• Perplexity Computer vs Claude Code vs Cowork vs Manus AI: what each tool does and costs

• How I set up the experiment and evaluated the outputs

• Task 1: Real estate property dossier + the LLM Council verdict

• Task 2: AI news briefing app + the LLM Council verdict

• What the results tell us: Perplexity Computer vs Claude Code vs Cowork vs Manus AI

• Which agentic AI tool should you invest in?

---

What are agentic AI tools (and why should you care)?

Before we get into the comparison, a quick note for those of you still figuring out what “agentic AI” means. I’ve written about this before, but it’s worth a reminder.

• Regular AI chatbots wait for you to type something, then respond. One message in, one message out.

• Agentic AI tools are a different beast. You give them a goal, and they figure out how to get there on their own. They break the task into steps, use different tools and models, browse the web, write code, create files, and keep going until the job is done.

Think of it this way: a chatbot is like texting a smart colleague. An agentic tool is like hiring a freelancer who goes off, does the work, and comes back with a deliverable.

That’s why these tools matter. They’re not just answering questions. They’re doing work.

And I've covered several of them before: Claude Cowork, Claude Code, OpenClaw, Manus.

---

Perplexity Computer vs Claude Code vs Cowork vs Manus AI: what each tool does and costs

Before getting into the actual test I ran on all four, let’s look at what each agentic AI tool is, what it does, and what you’ll pay for it.

One more thing worth mentioning: all four tools are converging on a similar model. They all support skills, agents, and custom workflows in some form. The differences today are more about execution quality, pricing, and what they’re best at.

That’s exactly why a hands-on comparison like this matters more than comparing feature lists.

---

How I set up the experiment

I wanted to go beyond feature lists and marketing claims.

So I designed a simple test: give all four agentic AI tools the exact same prompt, compare the outputs, and use a structured method to evaluate them.

But one task wasn’t enough to draw real conclusions. So I ran two. Each one tested a completely different type of work.

The two tasks

Task 1: Real estate property dossier. A research task. Produce a professional PDF report on a specific property with verified data from multiple sources.

Task 2: AI news briefing app. A build task. Create and deploy a working app that generates personalized AI news tailored to your industry, role, and current priorities.

I then tested them all on the same use case so the outputs could be compared properly.

How I evaluated: the LLM Council

To evaluate the outputs, I didn't want to rely only on my own opinion. I wanted something more objective. So I built an LLM Council tool, inspired by Andrej Karpathy’s concept but adapted for my use case.

Here’s how it works:

1. Upload the outputs. You upload the outputs from different tools plus a prompt explaining what to evaluate and compare.

2. Independent peer review. The tool sends everything to four AI models (GPT 5.1, Gemini 3 Pro, Claude Sonnet 4.5, Grok 4), anonymized so none of them know which tool produced which output. Each model evaluates and ranks independently.

3. Chairman synthesis. A chairman model reviews all four peer evaluations, aggregates the rankings and reasoning, and delivers a final verdict with justification.

I’ll share the Council’s verdicts after you’ve seen what each tool produced.

Now that you know what each tool does and how I set up the experiment and the evaluation, let’s get to the fun part. The actual testing.

Task 1: Real estate property dossier

The problem: You’re a real estate agent and want to create detailed descriptions for the properties you list. Or you’re looking to move to a new apartment, city, or country and need thorough details about the area. Either way, you need research that would normally take hours or cost money to outsource.

The idea: Give each tool a property address and ask it to create a comprehensive dossier with comparable sales, zoning data, school ratings, walkability scores, and risk flags. All sourced and formatted as a professional report.

The exact prompt I gave to Perplexity Computer, Claude Cowork, Claude Code, and Manus AI:

Research the property at 409 Eastern Pkwy, New York and create a comprehensive property dossier as a single report. 
Include:
1. Comparable sales (comps): Find 5-8 similar properties sold within the last 12 months within a 1-mile radius. Include sale price, square footage, beds/baths, price per square foot, and date sold.
2. Current zoning: What is the property zoned for? What uses are permitted? Are there any pending zoning changes in the area?
3. Neighborhood trends: Average home prices over the past 3 years, price trend direction (up/down/flat), average days on market, and inventory levels.
4. School ratings: List the assigned public schools (elementary, middle, high) with their ratings from GreatSchools or Niche. Note any highly-rated private schools within 3 miles.
5. Walkability & livability: Walk Score, Transit Score, and Bike Score. List what's within walking distance — grocery stores, restaurants, parks, public transit stops.
6. Key risks or flags: Flood zone status, crime trends, any major developments planned nearby (construction, commercial projects, infrastructure). Format everything as a clean, organized report with sections and tables where appropriate. Include sources for every data point.

A pretty complex task. A lot of research, data from multiple sources, and structuring it all into something professional.

Perplexity Computer

Here’s what the process looked like:

The output: See the full PDF

Cost: 901.27 credits (~$18)

Claude Cowork

Here’s what the process looked like:

It first created an HTML report, then I asked it to convert to PDF.

Output: See the full PDF

Cost: Can’t track exact spending. Claude doesn’t show token counts, only a usage percentage, which isn’t precise enough for a real comparison.

Claude Code

Here’s what the process looked like:

It initially created a markdown file, so I asked it to create a PDF so we could compare properly.

Output: See the full PDF

Cost: Same issue as Cowork. No token-level tracking available.

Manus AI

Here’s what the process looked like:

It started with a text file, then I asked it to convert to PDF.

Output: See the full PDF

Cost: 112 credits (~$0.56)

Your turn before you read the verdict

Before you read the Council’s ranking, have a look at all four outputs yourself: Perplexity Computer, Claude Cowork, Claude Code, Manus AI. Open them side by side and go through each one. Then vote below.

The LLM Council verdict for Task 1

And just like I thought myself (and would’ve ranked them if you asked me), the council deliberated similarly:

Rank 1: Perplexity Computer. Won on granularity and accuracy. The only tool to get the zoning code (R6A) correct and provide deep links to primary sources. It acted like an analyst, whereas the others acted like language generators.

Rank 2: Claude Code. A solid executive brief. Good for a quick neighborhood overview, but the zoning error and lack of specific comps kept it in second place.

Rank 3: Claude Cowork. Lost credibility by guessing the zoning with the word “Likely.” In a professional context, guessing on legal data is a problem.

Rank 4: Manus AI. Hallucinated irrelevant information about a Marine Terminal that had nothing to do with the property.

---

Task 2: AI news briefing app

The problem: You want to stay on top of AI news that’s relevant to your industry, not generic ones. But scrolling through feeds and newsletters takes time, and most of it doesn’t apply to your work.

The idea: Ask each tool to build a personalized AI news briefing app and deploy it as a live, shareable link. Three input fields, fresh results every time, sorted by impact level.

The exact prompt I gave to Perplexity Computer, Claude Code, Claude Cowork, and Manus AI:

Build a personalized AI news briefing app and deploy it as a live, shareable link.

The interface has three input fields:
"Your industry" (e.g., real estate, healthcare, e-commerce)
"What you do" (e.g., I run a 10-person marketing agency specializing in B2B SaaS)
"What you care about most right now" (e.g., cutting content production costs, finding new lead sources, automating client reporting)

When the user submits, the app generates a fresh AI news briefing with up to 10 items. Only include developments that have a specific, explainable connection to the user's industry and situation. Skip generic AI hype.

For each item, include:

- Headline and source link
- 2-3 sentence summary of what happened
- "Why you should care": a specific explanation of how this connects to their industry, role, and current priorities. Not generic. If you can't explain a concrete connection, don't include the item.
- Impact level: High (requires attention or action within 30 days), Medium (worth monitoring over the next quarter), Low (good to know, no action needed yet)
- One concrete next step the reader could take based on this news

Sort by impact level, highest first. Make the design clean and scannable. The app should generate fresh results each time someone submits, not show static content.

I then tested all four on the same use case (same industry, same role, same priorities) so I could compare the outputs properly with the LLM Council. Here’s the use case:

Perplexity Computer

Here’s what the process looked like:

And here’s what the partial results looked like:

Simple app, looks pretty good. Did everything as asked.

Cost: 396 credits (~$7.92)

Claude Cowork

Here’s what the process looked like:

Cowork directly asked me if I wanted to brand it using my AI blew my mind brand skill, and I said yes. So this one has more of my brand look compared to the others (really love what it looks like!).

Then I put it to the test for the same use case, and here’s a partial result:

Cost: Can’t track exact spending. Part of my subscription.

Claude Code

Here’s what the process looked like:

It created the app and deployed it directly to Vercel on its own. Loved the UI of this one too.

Then I put it to the test for the same use case, and here’s a partial result:

Cost: Can’t track exact spending. Part of my subscription.

Manus AI

Here’s what the process looked like:

Then I put it to the test for the same use case, and here’s a partial result:

It doesn’t look the best. You can barely see the news titles (though you could iterate on the UI).

Cost: 93 credits (~$0.47)

My take before the Council verdict

A few things stood out before even running the LLM Council:

• Design: Claude tools win. Both Cowork and Claude Code produced better-looking apps than Perplexity Computer and Manus.

• Functionality: All four had the same features I asked for in the prompt. No one missed a requirement.

• Ease of use: All four were straightforward to operate. With Claude Code and Cowork, I had to add my own API keys. With Perplexity Computer and Manus, the apps were launched and deployed on their platforms directly, no API keys needed.

The LLM Council verdict for Task 2

I dropped all four outputs into the LLM Council, anonymized in the same order they appear in this article.

Rank 1: Perplexity Computer. The unequivocal winner. It acted like a high-level consultant, using the user’s context to build a case for specific, real tools and developments. The news was anchored in the present with verified sources.

Rank 2: Claude Cowork. A distant second. Competent structure, but lacked the deep intelligence and verification of Perplexity.

Rank 3: Claude Code. Its attempt at personalization was more sophisticated than Manus, but it had a catastrophic hallucination with 2026 dates that made the content unreliable. Better “architecture” but broken data.

Rank 4: Manus AI. Failed the prompt. Generic news with broken links and zero specific actionable value.

---

What the results tell us: Perplexity Computer vs Claude Code vs Cowork vs Manus AI

Across both tasks, Perplexity Computer won.

And it makes sense. It’s built for search and research anchored in the present. It pulls real-time data, verifies sources, and links back to primary references.

Claude Code and Cowork both run on your Claude API key, which doesn’t have the same real-time web access. So for tasks that depend on current, verified data, Perplexity had an advantage from the start.

That said, both Cowork and Claude Code built the best-looking, most production-ready apps in Task 2.

Here's how it all shook out:

What surprised me

Cowork ranked higher than Claude Code in Task 2. I didn’t expect that.

Claude Code actually built the better app. But the Council flagged a “catastrophic hallucination” in Claude Code’s news content, which tanked its ranking.

A good reminder that looking good and being accurate are two different things.

---

Which agentic AI tool should you invest in?

After running both tests and seeing the LLM Council verdicts, here’s where I landed.

If you can only pick one platform: Claude

Go with Anthropic (Claude Pro at $20/month or Max, like I have). You get both Cowork and Claude Code with your subscription. Two agentic tools for the price of one.

And the ecosystem around them keeps growing. You can build custom skills that teach Claude specific workflows and knowledge (here’s how I built mine for writing in my voice), install plugins that extend what it can do, set up scheduled tasks that run on autopilot, and use Claude Code to build full websites and apps.

Cowork is the easiest entry point for non-technical users. Claude Code is powerful when you need to build things. For most everyday tasks, this combination covers you.

And if you’re working with data where accuracy matters and you’re worried about hallucinations, you can connect it to external APIs like Perplexity’s search to ground the outputs in real-time data.

If accuracy matters more than cost: add Perplexity

It won both tasks in this experiment and the accuracy gap was significant.

The catch is cost. Perplexity Computer is by far the most expensive tool I tested. On the $200/month Max plan, you get 10,000 credits. That means you could run roughly 11 heavy research tasks or about 15 medium ones before you hit the limit for the month.

If you want to try it before committing, Pro subscribers ($20/month) got a one-time 4,000 credit bonus. And if you want practical tips on managing the credit burn, Karo (Product with Attitude) wrote a deep dive on reducing costs that’s worth reading.

What about Manus?

It didn't win on anything in my tests. The outputs had hallucinations, broken links, and generic content.

To be fair, I don't have a Manus Pro subscription. I have a bunch of credits in my account because when I wrote my Manus article, many of you signed up through my referral link, which gave you bonus credits and gave me bonus credits too (thank you for that!).

Without a Pro plan, I only had access to Manus 1.6 Lite, while Pro subscribers get the more powerful Manus 1.6 and Manus 1.6 Max models. So my results might not reflect what you'd get on a paid plan.

But based on what I tested, I'd still pick Claude over Manus for the same price.

My personal setup

I use both Claude (Cowork + Claude Code) and Perplexity Computer. They complement each other well.

Claude for building, creating, automating. Perplexity for deep research that needs verified data. It’s actually amazing for that. I just wish it didn’t burn through credits so fast.

---

Your turn

More and more AI companies are entering the agentic space, and this is just the beginning. I keep saying this, but it’s true: the future of work is already the present.

We should all learn how to operate these agents and experiment with them, because they can do a lot more than most people realize. You can automate repetitive work, build custom tools for yourself or your team, and get things done that used to require hiring someone or spending hours on manually.

And everything is still young. Things are just getting started. Given how fast these tools are shipping, by the end of the year the picture will look completely different from what it is today.

So go experiment. Try them on your own tasks. See what works for your workflow.

And do let me know: what did you think of the results? Have you tried any of these tools yourself? What’s been your experience?

If you found this useful, share it with someone who’s also trying to figure out which agentic AI tool to invest in. It helps more than you’d think.

This post is free. If you found it useful and want access to more of what I’m building - prompts, automations, step-by-step guides - paid subscribers get all of it.