Finance models parse reports, news and documents and help analysts find and explain key figures. On-premises deployment matters when client and transaction data must stay in-house. Check what data the model was trained on, how it handles financial terminology in your languages, and what the license allows.
A foundation model for market candlestick data: trained on data from more than 45 exchanges, it forecasts prices and volumes. The largest version, large, is not open.
Models that spell out their reasoning before answering financial questions with numbers and tables. Not investment advice: decisions are made by a specialist.
A reasoning model for financial tasks based on Qwen2.5-7B: calculations, report analysis, regulatory questions. Trained on Chinese and English data.
A large Chinese model family for the financial industry: advice, document reading and long texts up to 8k-16k. Not investment advice: decisions are made by a specialist.
One of the first open model families for financial text: reading statements, news and questions about numbers. Not investment advice: decisions are made by a specialist.
An open set of lightweight add-ons for ordinary language models that work with financial texts and news. Not investment advice: decisions are made by a specialist.
A large model for financial documents with a context window of about 131k tokens: it holds long reports whole. Not investment advice: decisions are made by a specialist.
Finance-tuned versions of Llama 2: reading industry texts, reports and questions about terminology. Not investment advice: decisions are made by a specialist.
A financial assistant made of several fine-tuned experts: advice, calculations, document reading and knowledge-base search. Not investment advice: decisions are made by a specialist.
A classic model that determines the tone of financial news: positive, negative or neutral. English only, runs fast on a CPU.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
