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ChatGPT vs Claude vs Gemini vs Perplexity, I paid for all four and ran the same literature review through every one to find the best AI for research. This is not a spec sheet comparison. I checked five things that actually matter in academia and research, then I gave each one an identical task: connect the Consensus MCP, run my literature review skill, and produce something I could trust. Only one of them did it without a fight.
The five essentials I tested. A persistent workspace, so your papers live somewhere and you are not re-uploading PDFs every session. Tool connectivity, so the model can reach out to something like Consensus and fact check itself against real literature instead of its own memory. Skills, so your standard operating procedures and prompting workflows are captured once and your outputs stay consistent. A large context window, because one five page paper is roughly 10,000 tokens, so two average papers is about 20,000 and a real reading pile gets big fast. And code execution, because a dedicated space for a first pass at data analysis saves hours.
Here is roughly how they landed. Gemini takes the persistent workspace category because Gemini Notebooks (what used to be NotebookLM) is still the strongest way to manage a body of literature. Gemini also claims the biggest context window at a million tokens, though people running real tests report it capping out well short of that, and the same doubt applies to the numbers everyone quotes. ChatGPT sits somewhere between 256k and 400k unless you go to the API. Perplexity is the smallest at around 200k, which is a real problem if you upload papers. They all have skills, and skills are largely portable between them, so you can export one and import it elsewhere. They all cost twenty dollars a month, with Gemini a single cent cheaper. On paper, it is a tie.
Then I ran the actual test and the tie collapsed. Claude connected Consensus, ran the literature review skill, asked me sensible questions about scope and paper count, and produced a 26 page referenced review with a full audit log of every source it pulled. ChatGPT eventually produced a good document but kept telling me to connect my Consensus account while apparently already using it, so I do not fully trust the provenance. Perplexity told me I needed more credits after I had already paid twenty dollars, on desktop and on mobile. Gemini Spark refused the MCP with an account linking error and would not let me use connectors in a normal chat at all.
That is the whole point of this video. Busy academics do not have time to chase technical problems around four apps. The tools have to connect, the connection has to be trustworthy, and the output has to be checkable. One of them cleared that bar today.
To be clear about what the output is worth: a generated literature review is a map of a field, not a submittable review paper. It is genuinely useful for a first orientation, and every reference still needs checking.
⏱️ TIMESTAMPS
00:00 ChatGPT vs Claude vs Gemini vs Perplexity: which is best for research?
01:54 Test 1: persistent workspaces and projects
03:28 Test 2: tool connectivity and adding an MCP
05:14 Test 3: skills, and why they are portable between AI tools
08:10 Test 4: code execution and data analysis
09:17 Cost: what $20 a month actually buys you
10:37 The real test: one literature review, four AI tools
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