In this follow-up experiment, we tested how generative AI models handle handwritten text when provided as image input rather than PDF. Unlike in the earlier test, all models were able to process the images and return readable outputs. Performance Comparison 🏆 Champion as of October 2: Copilot, Gemini 2.5 Pro,
In this post we present a comparative test of several generative AI models, focusing on their ability to retrieve official sports statistics. The task involved collecting NBA game data through a single prompt, with the expectation that the models would accurately locate, extract, and structure the requested information. While the
We tested the new OpenAI Agent to assess its usefulness in academic research tasks, comparing it directly with Manus.ai and Perplexity’s research mode. Our aim was to evaluate how effectively each tool finds relevant scholarly and policy sources, navigates restricted websites (including captchas and Cloudflare protections), and allows
Accessing large language models (LLMs) through APIs opens up research and development opportunities that go well beyond the limits of traditional chat interfaces. For researchers API access enables language models to be built directly into bespoke workflows, analytical tools, or automated processes—supporting tasks such as large-scale text analysis, data
How well can today’s leading GenAI models classify real-world biodiversity data—without bespoke code or traditional machine learning pipelines? In this study, we benchmarked a range of large language models on the task of predicting penguin species from tabular ecological measurements, including both numerical and categorical variables. Using a
Having previously explored the FutureHouse Platform’s agents in tasks such as identifying tailor-made laws and generating a literature review on legislative backsliding, we now directly compare its Falcon agent and OpenAI’s o3. Our aim was to assess their performance on a focused literature search task: compiling a ranked
Can large language models return accurate results when asked to find real academic texts written by specific philosophers on a specific topic? In this case, the topic was Marxist theory — and the instruction was to list peer-reviewed publications in which Gilles Deleuze or Michel Foucault offer a critique of it.
In two recent blog posts, we demonstrated how scanned PDFs can be processed using Mistral’s tools—first via a prompt-based approach in Mistral Le Chat, and then through the Mistral OCR API. Both methods effectively extracted readable, structured text from complex documents. But how well do other generative AI
In earlier explorations, we identified the GenAI models that appeared most promising for data visualisation tasks—models that demonstrated strong code generation capabilities, basic support for data wrangling, and compatibility with popular Python libraries such as Matplotlib and Seaborn. In this follow-up case study, we examine a different dimension: rather
As data visualisation becomes an increasingly integral part of communicating research and insights, the choice of generative AI models capable of handling both code and chart rendering has never been more important. In this post, we explore which models can be reliably used for visual tasks—such as generating Python-based
We asked generative AI models to draft a short news-style summary based on a recently published academic article. After generating the initial text, we prompted the models again — this time falsely claiming that a specific expression they had used did not appear in the original publication and had never been
If your goal is to generate a downloadable .txt file—be it for survey responses, formatted data, or any structured text—not all models can deliver. In this case study, we tested various models to see whether they can create actual .txt files rather than just displaying text or offering
