Code InterpreterCode Interpreter unlocks powerful data analysis capabilities within LLMs, democratizing complex tasks for a wider audience.

Code Interpreter, a feature of advanced LLMs, empowers users to upload datasets and perform complex Python-based analysis, ideal for data science exploration and generating visualizations.

A powerful feature within advanced LLMs that allows users to upload datasets and perform complex analysis using Python. It's excellent for data science exploration and generating visualizations.

Not yet. This listing is compiled from OpenAI’s public documentation, pricing pages and changelogs — nothing on this page is presented as a hands-on test result.Code Interpreter sits in our testing queue; when we run it, this section will state what we tested, how long for, and what it actually produced. How we review AI tools.

• Exploratory Data Analysis: Upload CSVs, Excel files, or other datasets to quickly understand trends, identify outliers, and perform statistical analysis without writing extensive code.
• Data Visualization: Generate various types of plots and charts, from simple bar graphs to complex heatmaps, to visually represent data and communicate insights effectively.
• Data Cleaning and Transformation: Automate repetitive data cleaning tasks such as handling missing values, correcting formats, and restructuring datasets for easier analysis.
• Report Generation: Create automated reports with findings and visualizations derived directly from uploaded data, streamlining the reporting process.

• Dataset uploading for analysis
• Python code execution
• Automated data cleaning and transformation
• Generation of charts and graphs
• File manipulation and conversion
• Code debugging assistance
• Integration with advanced LLMs

Prices as of July 25, 2026, taken from OpenAI’s public pricing page. Vendors change pricing without notice — check before you buy.

• Limited execution time per session
• Cannot install arbitrary external libraries
• File size upload limits may apply

• Google Colaboratory
• Kaggle Kernels
• Jupyter Notebook

1. Subscribe to a paid ChatGPT plan that includes Code Interpreter (e.g., ChatGPT Plus).
2. Start a new chat and enable Code Interpreter from the model settings.
3. Upload your dataset(s) and prompt the AI with your analysis or visualization requests.

Use a SynaBot assistant to produce the thinking, then move the output into Code Interpreter for execution. Every SynaBot assistant is included with the platform membership.

• Content Creator (ZARA) — drafts the copy, captions and campaign angles you'll run through Code Interpreter.
• Business Planner (VIKRAM) — decides whether Code Interpreter belongs in your stack and what it should replace.
• Project Manager (PACE) — turns the rollout of Code Interpreter into owned, dated tasks.

Browse the full AI assistant roster, grab a starting point from the prompt library, or have us wire it together with our AI consultancy service.