Farewell GPT-4.0: Which is the best ChatGPT Model for Econometrics & Data Science Programming?

The New Model Era: What’s Out, What’s In

As of April 30, 2025, GPT-4.0 will officially retire from the ChatGPT interface, marking a shift in how econometricians, data scientists, and applied researchers interact with AI.  

Here’s a quick timeline of major recent changes:  

GPT-4o (Omni) is OpenAI's multimodal model capable of real-time reasoning across text, vision, and audio. It will be the standard option for both free and Plus ChatGPT users. Meanwhile, GPT-4.5, a more advanced model available in research preview, is being explored by power users, especially in the Pro tier and API environments.  

For Timberlake's clients, many of whom use Stata with Python, this is a renewed opportunity to supercharge coding workflows, research, and large-scale data analysis.   

Contents:

ChatGPT for Econometrics Programming 

Our Top ChatGPT Model Pick for Econometrics Programming

The Future of AI and Econometrics

ChatGPT for Econometrics Programming

As Sébastien Laurent of Aix-Marseille Université explored in a previous article for Timberlake, “Harnessing the Power of ChatGPT-4.0 in Econometrics and Programming”, GPT-4.0 speeds up scripting, helps explain regression outputs, aids in debugging, and enhances the learning curve of statistical software. That said, the latest models like GPT-4o go even further to introduce scalability, customization, and multimodal intelligence, opening the door for an increasingly personalized AI coding partner.

Here's a Quick Guide to the Latest Models (What’s Best for What)

GPT-4o (Omni)

•    Default model, fast, relatively inexpensive to run, capable of analysing images and audio too.
•    Perfect for code walkthroughs, error diagnostics, and even interpreting chart screenshots in Stata.
•    In theory, it is the most dependable model for executing chain-based automated tasks with consistency.

GPT-4.5 (Currently available as “Research Preview”)

•    Available to Pro/API users, it offers better reasoning, fewer hallucinations, and enhanced memory.
•    Ideal for multi-session projects, large dataset summaries, or complex Python + Stata integration tasks.

o4-mini / o4-mini-high (Internal test models)

•    Mini-high is reportedly strong at coding and visual reasoning, which may make it promising for hybrid workflows involving Stata automation with Python.
•    However, its effectiveness for econometrics-specific programming tasks (e.g., causal inference, panel data modelling) hasn’t been thoroughly established.

o3

•    Uses advanced reasoning. Reasonably good for logic-heavy programming
•    Potentially holds relevance to difference-in-differences design validation for example.

As noted in our earlier analysis, ChatGPT’s core value for econometrics programmers remains the same—it accelerates workflows and enhances coding accuracy. What’s new, however, is a growing second layer of benefits: the ability to reduce data acquisition costs at scale (e.g., by generating synthetic datasets or parsing open data), and to customize your AI assistant more deeply by training it through project-based memory, tied to your ongoing research files and environment.

If you’re selecting a single model to support your econometrics programming—particularly workflows involving Stata and Python—our top recommendation is:

#1 - GPT-4o

Why?

• It’s the default model, making it the most accessible and optimized option available.
• It supports core econometric tasks in Stata, such as xtreg, ivregress, and cate, and integrates effectively with Python for automation, data manipulation, and model execution.
• It can interpret screenshots, tables, and charts, which is especially helpful for debugging or explaining Stata outputs visually.
• It is faster and more cost-efficient than GPT-4.0 and benefits from continuous OpenAI improvements.
• In theory, it is the most dependable model for executing chain-based automated tasks with consistency, making it suitable for users working with structured research workflows.
• It allows you to optimize your research environment over time by leveraging project-based memory—helping ChatGPT retain context and adapt to your style, scripts, and data-handling conventions across sessions.

Honourable mentions:

GPT-4.5 – Designed for advanced, research-intensive projects, likely to serve as the successor to GPT-4o and currently in model training under the “Research Preview” program.

• Offers superior reasoning, enhanced memory, and reduced hallucination rates.
• Ideal for multi-session research projects, large dataset integration, and building personalized assistant behaviour through continued interaction with project files.
• Well-suited to replication studies, academic writing support, and advanced econometric methods like Bayesian modelling.

o4-mini-high - for coding tasks, especially in Python:

• This internal test model has shown strong performance in coding and visual reasoning.
• It’s particularly useful for Python programming tasks such as debugging, script generation, and refactoring, even if its effectiveness in econometric-specific modelling (e.g., panel data or causal inference) remains less validated.

Recent forecasting work such as AI 2027 (April 2025) suggests that Artificial General Intelligence, i.e. superhuman AI systems, could soon transform research, data collection, and modelling to a degree greater than even the Industrial Revolution​.

While today's GPT-4o and GPT-4.5 offer us only glimpses of this capability, for econometricians and data scientists, this could mean:

• AI agents capable of conducting entire replication studies, scouring public datasets, and even simulating policy models autonomously.
• Massive drops in the cost of high-quality data acquisition, as AI automates scraping, cleaning, and structuring large economic databases.
• The rise of personal AI research assistants: not just answering questions but executing complex empirical strategies like Difference-in-Differences or Instrumental Variable estimation on demand.
• Greater model development speeds: Econometric model building could transition from weeks of human coding to minutes with AI-led assistance.

Writing on behalf of Timberlake’s consulting and academic training team, Gavin regularly provides updates on the latest developments in data science, econometrics, and statistical software.