FinGPT vs STORM
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source large language models tuned for financial tasks like sentiment analysis and stock forecasting.
Open source · MIT
Open-source LLM system that researches a topic online and drafts a Wikipedia-style article with citations.
The short answer
Choose FinGPT if you need…
- Developers who want to experiment with finance-tuned LLMs for sentiment analysis
- Researchers comparing open-source models on financial datasets
- People who want to try the FinGPT-Forecaster demo without installing anything
- Students learning how LoRA fine-tuning is applied to finance data
Choose STORM if you need…
- Developers who want to build a customizable research or report-writing pipeline
- Writers who want a first-draft outline and source list before writing
- Researchers exploring a new topic who want a structured overview with references
- Teams experimenting with different language models and search engines
Side-by-side
| FinGPT | STORM | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 21.5k | 31.8k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Jupyter Notebook | Python |
| Last code update | 12 days ago | 13 months ago |
What each one is
FinGPT
FinGPT is a project that releases open-source large language models (LLMs) trained or fine-tuned on financial text, such as news and tweets. Its models are published on Hugging Face, and the project also includes a FinGPT-Forecaster demo that produces an analysis and a next-week stock movement prediction for a ticker you enter. The README says the goal is a more accessible alternative to closed, costly finance models like BloombergGPT.
STORM
STORM is a research system built on large language models. Given a topic, it searches the internet, simulates conversations to gather information, builds an outline, and writes a full-length article with citations. Co-STORM extends it so a human can take part in the discussion and steer the research. The README says the output is not publication-ready and often needs significant editing, but experienced Wikipedia editors have found it useful for pre-writing.
Best for
FinGPT
- Developers who want to experiment with finance-tuned LLMs for sentiment analysis
- Researchers comparing open-source models on financial datasets
- People who want to try the FinGPT-Forecaster demo without installing anything
- Students learning how LoRA fine-tuning is applied to finance data
STORM
- Developers who want to build a customizable research or report-writing pipeline
- Writers who want a first-draft outline and source list before writing
- Researchers exploring a new topic who want a structured overview with references
- Teams experimenting with different language models and search engines
Strengths
FinGPT
- Models and datasets are publicly available on Hugging Face
- MIT license allows broad reuse
- Backed by published papers and an active README with a clear changelog
STORM
- Modular design: language models and search engines can be swapped, per the README's litellm and retriever support
- Produces outlines and cited text rather than a single unsourced answer
- Open source under the MIT license with an active research and release history
Not ideal for
FinGPT
- Anyone looking for investment advice: the README presents Forecaster as a research demo, not a trading tool
- Users needing non-US stock predictions: the README says Forecaster is trained on US DOW 30 data and other markets need their own fine-tuning
- People without a GPU who want to run local models: the README says local models require a GPU
STORM
- Anyone needing publication-ready text, since the README says articles often need significant edits
- People without an API key for a language model or search provider, since the system depends on them
- Users who need a finished no-setup app, since the README centers on a Python package and code setup
Trade-offs
FinGPT
- The README notes that much of the Wall Street data and APIs remain closed, so the project depends on public data
- Setup for local models requires a GPU and points to a separate SETUP guide for details
- The README is truncated and the benchmark table is cut off, so some performance claims cannot be verified here
STORM
- The README says output is not publication-ready and often needs significant editing
- Requires configured language model and search API keys, which may carry costs
- Installation and customization assume Python and some comfort with code
Getting started
FinGPT
Clone the repository
git clone https://github.com/AI4Finance-Foundation/FinGPT.gitEnter the project folder
cd FinGPTSTORM
Install the library from PyPI
pip install knowledge-stormClone the repository for source install
git clone https://github.com/stanford-oval/storm.gitFrequently asked questions
What is the difference between FinGPT and STORM?
FinGPT: Open-source large language models tuned for financial tasks like sentiment analysis and stock forecasting. STORM: Open-source LLM system that researches a topic online and drafts a Wikipedia-style article with citations. FinGPT suits developers who want to experiment with finance-tuned llms for sentiment analysis, while STORM suits developers who want to build a customizable research or report-writing pipeline.
Are FinGPT and STORM free?
FinGPT: Free and open source. STORM: Free and open source.
Which is easier to set up, FinGPT or STORM?
STORM is easier to get started with (rated intermediate); FinGPT is rated advanced and usually needs more setup.
Is FinGPT or STORM more popular?
STORM has more GitHub stars (31.8k) than FinGPT (21.5k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to FinGPT and STORM?
Other options people compare them with include FinRL, pix2tex, Humanizer, Vibe-Trading, AI Berkshire.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.