Compound is the AI analyst for buy-side investors. It picks the best model for each step across OpenAI, Anthropic and Google, builds real Excel, PowerPoint and Word files with live formulas, and traces every number back to its source. Its analysis scores highest on PE Bench, our own private equity benchmark: 80.5%, against 78.1% for Claude Code with Opus 5.5.
ChatGPT and Claude are general assistants that also do finance work. Compound is built for the work private equity firms take from the data room to the financial model, IC memo and deck.
ChatGPT and Claude vs Compound at a glance
| Compound | ChatGPT and Claude | |
|---|---|---|
| Built for | Buy-side investors: private equity, credit, real estate, hedge funds and LP allocators | General use, with finance offerings |
| PE Bench score (labs’ own coding agents) | 80.5% overall | 78.1% for Claude Code with Opus 5.5, 70.1% for Codex with GPT-6 Astra |
| Models | The best model for each step, across OpenAI, Anthropic, and Google | Each runs on its own lab’s models |
| Excel, PowerPoint and Word | Builds real Excel, PowerPoint and Word files, many at once, with formulas that match Microsoft Office | Add-ins work only on files already open, one at a time; files built outside Office can calculate or render differently |
| Works with your firm’s AI stack | Adds finance work to the Claude Code or Codex your firm already uses, through the Compound CLI | Their own apps and agents |
| Full auditability of financial models | Click the citation on any number to trace it from the deck, through the model, to the authoritative source page or cell | Basic citations in answers, which don’t consistently link numbers in a model or deck back to authoritative sources |
| Market data | Built in with no extra licenses, or connect your FactSet subscription | Connectors to your own data subscriptions; otherwise general web search |
What private equity firms get with Compound
| Built into Compound | What it means for the work |
|---|---|
| Full auditability of financial models | Click a number’s citation on a slide to open the model cell behind it, then the filing or data room page it came from. A partner audits the deck without asking the analyst. |
| One agent for the whole team | The deal team shares one agent and one workspace with the deal’s files, tasks and comments, so anyone can pick up the work. A VP comments on a cell and the agent makes the change. See Collaboration. |
| Data room at scale | Drop in a data room of up to 15,000 files for due diligence, and Compound runs hundreds of tasks across it in parallel, ready when you’re back. |
| Your firm’s templates | Compound fills your firm’s templates for each new deal, keeping their layouts, charts and callouts, and applies the firm’s conventions in every agent. |
ChatGPT vs Claude vs Compound on private equity tasks
PE Bench is our own benchmark of the work a deal team hands a partner, from the quality of earnings to the LBO, IC memo, and deck, on full deal data rooms.
| Agent | Overall PE Bench score |
|---|---|
| Compound | 80.5% |
| Claude Code, Opus 5.5 | 78.1% |
| Claude Code, Fable 5.1 | 73.5% |
| Codex, GPT-6 Astra | 70.1% |
Source: PE Bench v1, October 2026.
Compound’s lead is largest on the messy private data rooms.
In one data room, the seller adds back $4.8M of sale costs. Opus 5.5 and GPT-6 Astra accepted all of it. Compound rejected $2.9M, spent before the board authorized a strategic review: about $60M of price at the deal’s 21.0x entry multiple. The PE Bench write-up shows each agent’s output side by side.
Beyond ChatGPT and Claude for finance
Worked examples on illustrative deals:
- On a data center sale’s adjusted EBITDA, Compound tested each add-back in the seller’s $224.0M against the data room and arrived at $215.1M.
- On an industrial park’s rent roll, Compound tied every row to its lease and found $109.3k a year of rent that no executed lease supports.
- On a buyout fund’s DDQ, Compound showed that the fund’s net IRR is 11.8% without its subscription line, against 14.5% as reported.
Using Compound from Claude Code
With the Compound CLI, Claude Code, Codex, or any agent hands financial modeling and presentation work to Compound and gets professional-grade files back, with every figure cited. The work also appears in Compound for the rest of the team.
Security: Compound never trains on your data
Compound never trains on customer data, on any plan.
Compound is SOC 2 Type II, encrypts data at rest and in transit, and supports SAML and OIDC single sign-on, SCIM, audit logs, and deployment in your own AWS or GCP account. See Security.
FAQ
ChatGPT or Claude: which is better for finance? It changes with each model release. On PE Bench, our private equity benchmark, as of October 2026, Anthropic’s and OpenAI’s own coding agents scored 78.1% (Claude Code with Opus 5.5) and 70.1% (Codex with GPT-6 Astra). Compound picks the best model for each step across OpenAI, Anthropic and Google, switches as new ones ship, and scored 80.5%.
What is the best AI for finance? For private equity, Compound: its analysis scores highest on PE Bench, our private equity benchmark, at 80.5%, on work that runs from a data room to a model, IC memo and deck in the firm’s templates.
What is the best LLM for financial analysis? No single LLM is best at every step of financial analysis. Compound sends each step to the model best suited to it, so your team never has to choose.
