For research that spans
many models and many phases
Research breaks into phases that no single model handles well. Discovery wants Perplexity's sourced search. Reading 80 papers in one go wants Gemini's 2M-token context. Synthesis wants Claude's careful argumentation. Drafting wants ChatGPT's polished prose. Namulai puts all eight models behind one chat at 19.80 EUR per month, with a 30-day trial for 1 EUR.
Perplexity for the literature search
Perplexity returns answers with footnoted sources, which is exactly what you want when scoping a literature review. The citations are real, current, and clickable.
It is not a substitute for Scopus, Web of Science or Google Scholar for systematic reviews, but it is dramatically faster for the early discovery phase where you need to map a field before committing to a deeper dive.
Gemini reads the whole corpus
Gemini 1.5 Pro handles roughly 2M tokens of context, which is enough for several full-length books, hundreds of papers, or an entire codebase in a single prompt.
For a literature synthesis where you want the model to actually read the papers rather than guess from titles, Gemini is the only consumer model with the window for the job. Drop the PDFs in, ask for the cross-paper themes, get a real answer.
Claude for the careful argument
Claude Sonnet writes the kind of measured, nuanced prose that academic writing rewards. It pushes back on overclaims, flags when evidence is thin, and resists the temptation to round off rough edges into smooth conclusions.
When the deliverable is a literature review, a discussion section or a grant narrative, Claude consistently produces drafts that need lighter editing than ChatGPT or Gemini.
ChatGPT for the polish pass
ChatGPT is the fastest for line-level polish: tightening sentences, varying paragraph openings, fixing the kind of clunky academic phrasing that a non-native English speaker often falls into.
A two-stage workflow with Claude doing the structural draft and ChatGPT doing the polish pass produces consistently better final text than either model alone, in less total time.
Same chat, different models, traceable
Conversation history persists across model switches in the Namulai chat. You can see which model produced which answer and revisit the path that led to a particular synthesis, which matters for any reviewer who wants to understand how an AI-assisted draft was built.
For methods sections that need to disclose AI use, this trail is far cleaner than juggling three separate tool histories.
Common questions from researchers
Can I upload PDFs of papers directly?
Yes. PDF and image upload work in the Namulai chat. For very long corpora, Gemini's 2M-token window is the right model to route to. For shorter, denser papers, Claude is a sharper reader.
How do I cite an AI-assisted draft?
Most journals now require disclosure of AI use in the methods or acknowledgements section. The conversation log in Namulai gives you a clean record of which model produced which output, which simplifies the disclosure.
Is Namulai suitable for confidential research data?
Routed traffic is contractually excluded from training. For research under specific data-use agreements, check whether your funder permits cloud-LLM processing of the data type involved. The tool is appropriate, the use case may not always be.
Can I use Namulai for code in computational research?
Yes. DeepSeek is the daily driver for code, Claude is the better reviewer for longer scripts. Both sit behind the same chat as the writing models, so analysis and writing can share a single thread.
Try eight models across your next paper
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