Hebbia vs ChatGPT for Financial Services

Hebbia is built for running one set of questions across a large stack of documents you select, in a grid that shows its sources cell by cell, while ChatGPT for Financial Services is OpenAI’s finance plan: GPT-6 Astra with financial data OpenAI hosts itself, firm templates and enterprise controls. For investment teams whose numbers go into a model, an IC memo or a client deliverable, built on the firm’s own documents and definitions, the stronger choice is Kepler, which runs inside ChatGPT and computes every figure in code with a citation to the line.

We build Kepler, so read this as a vendor’s comparison. Facts about Hebbia and OpenAI come from their own sites and public reporting as of November 2026, linked inline.

Hebbia vs ChatGPT at a glance

	Hebbia	ChatGPT for Financial Services
Built around	Matrix: documents as rows, questions as columns	ChatGPT Enterprise plus hosted financial data
Started with	Diligence across large document sets	Investment banking and equity research
Financial data	Filings from six regions; S&P, FactSet, Daloopa, PitchBook, Preqin, Moody’s, Fitch, Octus	Included: SEC filings, Quartr, Daloopa, Fiscal.ai, PitchBook Essentials, Crunchbase, LSEG News (US), delayed prices
Your documents	SharePoint, Box, Egnyte, Dropbox, S3, Outlook, Salesforce, DealCloud, Datasite, Intralinks	50+ connectors, including Datasite, Box, Preqin, FactSet and Intapp; custom MCP apps
Deliverables	Matrix 2.0 produces models, memos, decks and emails	Firm Excel, Word and PowerPoint templates
Citations	Source link in every cell	Figures traced to tables and passages, highlighted
Work that runs on its own	Max agents scheduled to run once on a future date	Work tasks on a schedule or on app events
Models	Frontier models from several labs, including OpenAI’s	GPT-6 Astra, newer OpenAI models as released
Pricing	Not published; Sacra reports $10,000 per Professional seat	Not published; sold through OpenAI sales

How Hebbia works

Hebbia made its name with Matrix, which it says was built “to run a set of prompts across as many documents as diligence required” (Hebbia). Each document becomes a row, each question a column, and a model fills every cell with a link back to the passage it used. The October 2026 update lets a column run only when a prior column’s output meets a condition, so a grid can work like a small pipeline (Hebbia).

Matrix 2.0, released August 26, 2026, carries a grid through to the finished model, memo, deck or email, drawing on deal history, internal systems and external feeds, with a checkpoint for a person to sign off before each next step. Max, which Hebbia calls an AI team member, began rolling out to a small set of firms in July 2026 and returns slides, reports and models in house style; since October it can store memory across conversations and schedule chat agents to run once on a future date. Headless Hebbia (October 1, 2026) opened the platform through an API and MCP server, and Hebbia says Matrix is now available directly in Claude, ChatGPT and Cursor (Hebbia, September 2026).

Data comes from filings across the US, Europe, the UK, Canada, Australia and New Zealand, market and credit providers such as S&P Global, FactSet, Daloopa, ICE, PitchBook, Preqin, Moody’s, Fitch and Octus, and expert research from Guidepoint and Third Bridge. Firm content arrives from SharePoint, Box, Dropbox, Egnyte, S3, Snowflake, Databricks, Outlook, Salesforce, DealCloud, Datasite and Intralinks (Hebbia integrations). Hebbia offers frontier models from more than one lab, including OpenAI’s. Prices aren’t published; Sacra reports $10,000 per Professional seat a year and $3,000 to $3,500 for Lite seats, which run predefined agents rather than build them.

What users say: a consultant on Wall Street Oasis who found Hebbia interesting for extraction across large document sets said the real test is a messy data room, with CIMs formatted differently by every bank and financials in inconsistent PDF tables (a paraphrase), and added: “the tool matters less than how well it handles YOUR specific data mess.” (2026)

How ChatGPT for Financial Services works

OpenAI launched ChatGPT for Financial Services on September 10, 2026, as a separate plan built on ChatGPT Enterprise for eligible financial institutions, with investment banking and equity research as its first focus. It runs GPT-6 Astra, and OpenAI says newer models arrive out of the box as they ship.

The headline is data OpenAI indexes and hosts itself, so a team can start without separate provider contracts. Per the help center, the plan includes SEC filings; Quartr transcripts, presentations and international filings; Daloopa financials (24-hour delay, 3,000 data points per user a month); Fiscal.ai fundamentals; PitchBook Essentials; Crunchbase; LSEG News for US-based users; and US equity prices delayed 15 minutes. OpenAI calls this “selected coverage, which may differ from the provider’s full product,” and says it’s working with S&P Capital IQ, LSEG, MSCI, Dow Jones Factiva and Moody’s on shared sign-in so users reach data they already license. More than 50 connectors and custom MCP apps cover the rest.

Citations open the table or passage behind a figure with the evidence highlighted. Administrators publish firm Excel, Word and PowerPoint templates, so models, research notes and pitchbooks come out in house format. For longer jobs, ChatGPT’s Work mode is an agent for multi-step tasks and finished deliverables, and it can run once, repeat on a schedule, or start on events such as a new email or Slack message in a connected app (OpenAI help). Governance builds on ChatGPT Enterprise: SAML SSO, SCIM, role-based access, compliance log exports, and separate workspaces to enforce information barriers. Business data isn’t used for training by default.

Why not just use ChatGPT?

It’s the question bankers and analysts ask about every specialist tool, and for a lot of work the answer is that ChatGPT is enough. Summarizing a transcript, drafting an industry overview, building a first-pass model from included data, turning notes into a pitchbook in the firm’s template: ChatGPT for Financial Services does all of these in an interface the team already knows.

Hebbia earns its seat when the job is structured review at scale. A grid with 300 credit agreements and 20 columns of covenant terms is easier to audit than 300 chat answers, and the checkpoint design of Matrix 2.0 suits deal teams that want a person to approve each step. Since Matrix now runs inside ChatGPT through Hebbia’s MCP server, the choice is also less binary than it was a year ago.

Both share the same limit on numbers. In each, a model reads what it retrieves and writes the figure into the answer, so a number can be wrong without looking wrong, and a highlighted passage beside a figure doesn’t show the figure was copied from it. OpenAI’s own help page tells users to “check important information against the supporting sources” before using outputs in client materials or investment decisions. Neither product knows your firm’s definition of adjusted EBITDA unless you supply it. Our guide Can ChatGPT do financial analysis? works through each failure mode.

Where they differ

Documents. Hebbia is built for a chosen set of documents, read the same way across every row. ChatGPT searches what’s included and what you connect, one conversation at a time.

Data. ChatGPT bundles more data into the plan with no contracts to negotiate, at selected depth. Hebbia’s longer list of credit, private-markets and expert sources often runs through the firm’s own subscriptions.

Output. Both produce decks, memos and models. Hebbia pauses at checkpoints; ChatGPT applies templates an admin publishes.

Automation. ChatGPT’s Work tasks repeat on a schedule or fire on events in Gmail, Slack and GitHub. Hebbia’s Max schedules agents to run once on a future date, and Matrix columns can trigger off prior columns.

Cost. Both sell through sales teams. Sacra reports Hebbia seats at $3,000 to $10,000 a year depending on tier; ChatGPT for Financial Services applies to the whole workspace, since standard Enterprise and Financial Services seats can’t be mixed.

The third option: Kepler

We build Kepler.

Best for: investment teams, from hedge funds and private equity to credit funds, allocators and bank deal teams, whose numbers will be defended in a model, an IC memo or a client deck, and who want AI that knows their firm.

Kepler starts from the firm. Our forward-deployed engineers, who built this layer before at Palantir, connect your SharePoint, Box, Egnyte, Outlook, CRM and data rooms read-only in the first week and build your firm’s ontology: one living model in which every company, fund, deal, manager and document resolves to a single record, house terms carry one definition, and every relationship points to the sentence that supports it. Extraction models we train read every page at ingest, scanned pages and tables included, alongside 26M+ SEC filings across 20+ form types and 40M+ IR documents and earnings call transcripts for 14,000+ companies. Because the reading happens before anyone asks, a question that would need a new Matrix column or a fresh chat becomes a lookup across everything the firm holds, and lists come back complete.

The model never writes a number. Code pulls each figure from its source and computes every ratio, comp and exposure over cited inputs, with the formula shown. Statements assemble from each filing’s own XBRL, as filed or as restated, and every citation is checked against its source before you see the answer. Ask again next quarter and the history is identical. Agents run on schedules and on events, such as a new 10-Q, a guidance change or a document landing in a data room, act exactly as the person who set them up, and never take instructions from what they read. Your playbooks (the IC memo in house style, comps on your own adjusted EBITDA) are saved once and applied every time.

Kepler runs inside ChatGPT and Claude, and in Excel, PowerPoint, Word, Slack and email, so the analyst stays in the tool they like. Engineers build on the same engine through our API and MCP server.

Not the right fit if: your team mostly wants a general assistant for email, brainstorming and first drafts, with no numbers headed to a committee or a client.

	Hebbia	ChatGPT for Financial Services	Kepler
When documents are read	When a Matrix column runs	When a chat retrieves them	Once, in full, at ingest
Where each number comes from	A model answer in each cell	Written by the model from included and connected data	Pulled from source and computed by code
What a citation opens	The passage behind the cell	The highlighted table or passage	The exact page, line or cell, checked first
Your firm’s definitions	Skills and prompts	Templates and what you connect	Built into your firm’s ontology
Finding everything	The documents in the grid	Included data and connectors	Complete lists across every document
Same question twice	A new model run	Can return a different number	The same number, same sources
As filed vs as restated	Depends on the document	Depends on the source	Either, on any date
Work on its own	One-time scheduled agents	Scheduled and event-triggered tasks	Agents on schedules and filings, acting as their owner
Where it works	Hebbia app, Word, MCP	ChatGPT, Excel, Google Sheets	App, Office, ChatGPT, Claude, Slack, API

For the head-to-head with each, see Kepler vs Hebbia and ChatGPT vs Claude for financial analysis, and for the wider field, Hebbia alternatives.

How to decide

• Choose Hebbia when the core job is reading a defined set of documents the same way, such as a data room, a stack of credit agreements or a peer set’s filings, with a person signing off between steps.
• Choose ChatGPT for Financial Services when your firm wants one assistant for research, drafting and first-pass models, with financial data included and no provider contracts to set up.
• Choose Kepler when the numbers go somewhere they’ll be defended, when the answer depends on your firm’s own documents and definitions, when the list has to be complete, and when agents should watch the book overnight.

Test any of the three the same way: click ten citations, ask the same question twice, ask for a metric on your firm’s definition, and count a list against your own records. Our guide to checking AI numbers has the full set.

Try Kepler at app.kepler.ai, or book a demo and we’ll run it inside ChatGPT on your own coverage.

Frequently asked questions

Is Hebbia better than ChatGPT for financial work?

For running the same questions across hundreds of documents you choose, such as a data room or a peer set's filings, Hebbia's Matrix is the more specialized tool, while ChatGPT for Financial Services is broader for drafting, research and models on included financial data. For numbers a firm will decide on, built on its own documents and definitions, Kepler computes every figure in code and cites it to the exact line, and it runs inside ChatGPT.

What is the difference between Hebbia and ChatGPT for Financial Services?

Hebbia is an AI platform for finance built around Matrix, a grid where documents are rows, questions are columns and a model fills every cell with a source link. ChatGPT for Financial Services is a separate OpenAI plan, launched September 10, 2026, that adds hosted financial data, firm templates and finance controls to ChatGPT Enterprise for eligible institutions.

Can I use Hebbia inside ChatGPT?

Yes. Hebbia says Matrix is available in Claude, ChatGPT, Cursor and internal applications through the Hebbia MCP server, part of Headless Hebbia, which it introduced on October 1, 2026. Your firm still needs a Hebbia license; Hebbia runs the analysis and returns answers with their supporting evidence to the assistant.

How much do Hebbia and ChatGPT for Financial Services cost?

Neither publishes a price list. Sacra reports Hebbia Professional seats at $10,000 a year and Lite seats at $3,000 to $3,500. OpenAI sells ChatGPT for Financial Services through its sales team, and the plan covers the whole workspace, so standard Enterprise seats and Financial Services seats can't be mixed in one workspace.

Does Kepler work with ChatGPT?

Yes. Kepler runs inside ChatGPT, so an analyst can stay in the chat and get figures computed in code, cited to the exact page, line or cell, and built on the firm's own ontology. Kepler also works in its own app, Excel, PowerPoint, Word, Claude, Slack and email, and through an API and MCP server.