Best AI search tools for stock analysis in 2026: Perplexity, Grok & Claude compared
How multi-agent AI search beats single-model research for equity analysis
You probably used deep research with Gemini, ChatGPT, or another tool.
But which one is the best for stock analysis?
And can we build something ourselves that is even better?
To answer these questions, we first need to understand how LLMs “search”.
How AI search works for stock research
All these tools search in a specific way.
Here are the main steps outlined:
You ask a question/prompt: Find all information on company X. Only use official company information. No third-party websites.
The LLM will not add your prompt to its search API. It will decompose the prompt into several search queries
Now it will use its search API, mainly Bing and Google (just like we would)
It then scrapes the TOP 10 to 20 URLs for each query and cleans the text
Now it needs to divide and sort the contents for relevance
The LLM will synthesize everything and answer with citations
Now, in the flowchart, you can see I added something called Council Mode.
We’ll get back to that later on.
So whether you’re using Claude, Gemini, or ChatGPT, it always works the same way.
A Google search on steroids.
Which search engine do AI models use? (Google vs Bing vs Brave)
Here’s what the main LLM’s are using:
Nobody except Google is using Google Search. Almost everyone is using Bing. Only Claude is using Brave.
The best AI search tool for equity analysts
When it comes to stock analysis, we want 3 things:
Maximum amount of data
Correct citations of the data
No hallucinations
There are some research papers online in which researchers tested and compared different models.
Let’s first go to an older paper.
In June 2025, Chinese researchers conducted a comprehensive analysis.
The final result was the following:
Where different metrics were tested.
Both Perplexity and Gemini stand out, where Perplexity shows the highest accuracy in citations, and Gemini shows the highest effective number of citations.
But this analysis does not include the newest models.
The most recent paper dates from February 2026.
Here, Perplexity, combined with Opus 4.6, is the winner.
And more specifically, when it comes to specific Finance tasks:
Perplexity with Opus for Finance seems to be the best performer.
Now, let’s take a final, deeper look at the criteria we outlined: Accuracy, Depth of research, and citation quality.
Perplexity takes the lead once more.
Be mindful that it does not score 100%!
Now, although that was an independently reviewed paper, there is only one problem.
The paper was written by Perplexity.
And there is one important aspect we have not yet highlighted. Council Mode.
Multi-Agent “Council Mode”: why it wins
Imagine you analyze a company. And after analyzing, you discuss your ideas with other investors who have also analyzed the company. Often, it will shift your perspective about the company a little bit.
Your research has become stronger.
The final analysis is better.
What works for us humans works for LLMs.
But let’s first check whether this truly matters. And the best way is to look at what happened with Grok 4.2
Grok 4.2’s Multi-agent approach
This model was released with a special beta feature:
You add your prompt
Grok 4.2 will assess each prompt by 4 different agents
I tested it by copy-pasting one of my latest stock pitches and seeing what it has to say about it. Here’s what happens:
After 44 seconds (rather fast), it gave me this:
And Benjamin was the one who explicitly looked for weaknesses in my pitch:
Their overall conclusion: 4.5/5, great pitch.
Now, what matters more is that, because of this multi-agent mode, hallucination rates for Grok 4.2 have sprinted past Gemini 3 Pro and Claude Opus 4.6
That means multi-agentic analysis works.
Note: You need a Supergrok subscription to get this agentic analysis. ($30/month)
Perplexity Council mode for stock research
Perplexity was one of the first to offer a council mode.
In their max plan, when you prompt perplexity, it runs it through different models and reports where they agree and where the key differences are.
Don’t underestimate the value of this. Again, it increases the accuracy of the final analysis.
Note: Only available in Perplexity Max ($200/month)
But for those who don’t want to cough up $200/month, and are tech-savvy, here’s a “free” solution:
Multi-agent equity research
This GitHub project lists the code you need to build a multi-agent project.
With over 40,000 stars, it is popular. And it goes beyond what Grok and Perplexity do.
But it is more suited for traders:
It parses fundamental, technical, and real-time data before giving buy and sell evidence to the trader.
Building a custom researcher
In our previous articles, you’ve seen how we built Hunter to evaluate a large number of companies on a specific framework. You can find the results of the 95 software companies we analyzed here.
We also built a tracking system that, every week, gives us information on whether our thesis for our portfolio companies still holds.
Our next step is to build the researcher.
The researcher will use financial data, filings, and the latest news. It will complement that with a deep research function and output a full research report on the company.
As you can see in the image, we have a separate “red team” that does the same thing but takes the opposite stance.
But that’s food for a future article.
My recommendation: The best setup for stock analysts
When it comes to research, Perplexity and Gemini deep research score the best. Grok 4.2, thanks to its multi-agent mode, now significantly reduces hallucinations.
But if you want something customized to your needs, you’ll need to build it yourself or adapt an existing project.
Keep learning AI. Keep on building.
Kevin