The short answer: for an institutional research team, no. ChatGPT, Claude and Perplexity run the models the platforms use and now carry FactSet, PitchBook and Daloopa connectors, so the model is no longer the gap. What a chat login cannot give a desk is entitled broker research and an expert-transcript library, a trace from each figure to its passage, a log of every prompt and export, and the firm's warehouse queried in place. Nor can it run a workflow for hours. Those five are what AllMind AI is built around. A solo analyst with no entitled content and nothing internal to connect should buy an assistant plus a monthly data plan.

Who this is for: heads of research and compliance officers weighing an enterprise assistant, analysts already using one, and solo analysts pricing a first stack.

Published August 24, 2026. Last reviewed August 24, 2026. Written by the AllMind AI research team. Reviewed by Anwaar Malik, founder of AllMind AI.

Disclosure: AllMind AI builds one of the platforms compared here. We name the cases where a general assistant fits better, and we do not rank on payment.

Key takeaways

• The model is no longer the gap. Claude Opus 4.7 led the Vals Finance Agent leaderboard at 64.37% on June 4, 2026; the institutional platforms run models from the same vendors under their own retrieval and audit layers.
• Connectors closed part of the data gap in a year. Claude for Financial Services (July 15, 2025), ChatGPT's Public Equity Investing plugin (June 2, 2026) and Copilot in Excel (June 25, 2026) each name FactSet, PitchBook or Daloopa.
• Factuality is the number a desk cannot delegate. In JPMorgan's Deep FinResearch Bench (April 2026), analysts outscored the best agent 2.84 to 2.31, and the most factual agent's claims held up 86.0% of the time.
• Licensing is the barrier large managers report first. 69% of 35 large asset managers named broker and data licensing restrictions the top obstacle (Substantive Research and Aiera, July 16, 2026).
• Entitlements and audit decide before any feature list. Work that touches no licensed content and leaves nothing for a reviewer to reconstruct a year later belongs on an assistant and a data plan; AllMind AI is not self-serve and starts with a scoping conversation.

Can a chatbot do institutional investment research? The 2026 field in one table

A chatbot now does the reading, the first draft and a connector-fed data pull; it cannot hold entitlements, lineage, an audit trail or a long multi-source run. The last column does not move with the next model release.

What can each of these tools do for investment research in 2026?

The platforms sell a governed corpus with entitlements and lineage; the assistants sell a frontier model with connectors to some of the same vendors and, since May 2026, finance agent templates.

AllMind AI

AllMind AI is an AI research system for institutional investors, launched publicly on July 13, 2025: a financial ontology that maps companies, their suppliers and customers, estimates and filings, plus the firm's own research, with agents that traverse those relationships (what AllMind AI is).

Where it wins: the deep, multi-source job. Agents work across Expert Insights transcripts, broker research, S&P and FactSet fundamentals, LSEG and MSCI data, live earnings within minutes of the print, and alternative data. The transcripts require no expert-network contract of the firm's own, while live broker notes read under its research entitlement. Beside that sit the firm's own systems: Snowflake, Databricks and S3 read in place through an IAM role scoped to named tables, resolved by the ontology so a warehouse row and a filing land on the same company. A run lasts minutes, hours or days, each figure opens its passage and arithmetic, and a verification pass re-checks the numbers before the deliverable ships.

The question we used as the test: how fast did NVIDIA's Data Center business grow in the April 2026 quarter, what share of revenue is it, and where did gross margin land? NVIDIA's 10-Q for the quarter ended April 26, 2026, filed May 20, 2026, reports revenue of $81.6 billion, up 85% from a year ago, Data Center revenue of $75.2 billion, up 92%, and gross profit of $61.2 billion. So Data Center is 92.2% of revenue and gross margin is 74.9%, both derived, and the filing notes no Data Center Hopper shipments to China against $4.6 billion a year earlier.

A connector-fed assistant returns the reported figures with a citation. The two ratios are arithmetic the reader must redo unless the tool shows it, and setting that $75.2 billion against the firm's entitled broker estimates for the July quarter needs entitlements the login does not hold.

Where it falls short: AllMind AI has no self-serve tier. Onboarding starts with a working session on which internal systems and entitlements to connect, and pricing is quote-based with no monthly plan, so a team that needs something running today buys an assistant subscription instead. Head count is not what the quote turns on: a two-person institutional desk is a strong fit, because one system replaces subscriptions it could never justify separately.

AlphaSense

AlphaSense sells licensed search: filings, news, broker research and expert transcripts in one index. It also runs its own compare pages against Claude, ChatGPT and Perplexity, which says where it expects the fight.

Where it wins: a licensed corpus searchable in one place. The expert library is company-stated above 280,000 transcripts after the Tegus deal closed on July 8, 2024, and Work Products, launched July 14, 2026, draft PowerPoint and Excel from that corpus. The company raised $350 million at $7.5 billion, announced June 3, 2026.

Where it falls short: it searches and summarizes, and SharePoint, Box and Drive content sits in its Enterprise Intelligence tier as an index, without an entity map. Pricing is quote-only; a third-party estimate puts the median contract at $17,500 (Vendr, February 2026). The full comparison is in AllMind AI vs AlphaSense.

Hebbia

Hebbia runs Matrix, a grid that puts structured questions to thousands of documents at once, strongest in private equity, credit and banking; it added Snowflake support on July 8, 2026, then its Max agent on July 30.

Where it wins: data-room scale. A diligence team loads several thousand documents and gets a column per question with the supporting excerpt, and the Snowflake integration puts warehouse tables in the same grid. Hebbia is also a named data partner inside ChatGPT's Public Equity Investing plugin (June 2, 2026).

Where it falls short: Hebbia brings almost no market data of its own, so the universe is whatever you load. Its last priced round is still the July 2024 Series B of $130 million; seat pricing is unpublished, with a third-party estimate near $10,000 a year for Professional (Metronome, January 2026).

ChatGPT (Enterprise and plugins)

ChatGPT is OpenAI's general assistant, sold to financial services as ChatGPT Enterprise, ChatGPT for Excel, Deep Research with financial connectors and the API; on June 2, 2026 OpenAI put Codex inside the ChatGPT app with six business plugins, including Public Equity Investing.

Where it wins: the connector list is real and dated. Deep Research launched February 2, 2025, and OpenAI's financial-services page names six data connectors for it, listed in the table. The Public Equity Investing plugin's partners are Moody's, Daloopa, Datasite, PitchBook, Hebbia and FactSet. LSEG and S&P round out the eight, per the launch coverage. In JPMorgan's Deep FinResearch Bench, OpenAI's agent posted the highest factuality of the four tested, 86.0%.

Where it falls short: a connector returns what that vendor licenses to OpenAI, not the notes your own sell-side relationships entitle you to. The answer cites the page it read; it does not trace each figure to a passage, re-check it, or record who exported it. Whether an enterprise plan satisfies a fund's supervision policy is worked through in can hedge funds use ChatGPT.

Claude for Financial Services

Claude for Financial Services is Anthropic's finance edition of Claude, launched July 15, 2025 with connectors to Box, Daloopa, Databricks, FactSet, Morningstar, Palantir, PitchBook, S&P Global and Snowflake.

Where it wins: release cadence. The October 27, 2025 update added Aiera, Third Bridge through Aiera, LSEG, Moody's and MT Newswires, a Claude for Excel beta and six Agent Skills from comps to initiating coverage. May 5, 2026 brought ten agent templates (pitch builder and earnings reviewer among them), eight more connectors including Guidepoint and IBISWorld, and Excel, PowerPoint and Word add-ins in general availability. On the Vals Finance Agent leaderboard, Claude Opus 4.7 led at 64.37% on June 4, 2026.

Where it falls short: no published pricing, and the finance connectors return results into a conversation. The broker notes the firm itself licenses are not in the connector list, the join between a Snowflake result and a filing exists only inside that chat's context, and nothing re-checks a figure before it leaves the tool.

Perplexity Finance and Computer for Professional Finance

Perplexity is an answer engine that added finance pages in late 2024 and, in May 2026, launched Computer for Professional Finance for enterprise customers, with Morningstar, PitchBook, Daloopa and Carbon Arc connectors, Quartr and Fiscal data built in, and about three dozen prebuilt workflows.

Where it wins: cited answers at consumer prices. Third-party reports (July 2026) put Pro at $20 a month, Max at $200 a month, Enterprise Pro at $40 and Enterprise Max at $325, per seat per month. Daloopa's Perplexity MCP connector arrived on April 30, 2026.

Where it falls short: factuality of 75.6% in JPMorgan's Deep FinResearch Bench, second of four agents; no entitled broker or expert content; an Excel add-in still on the way at the May launch. Enterprise Max at $325 per seat per month also costs more than the third-party estimate for a Hebbia Lite seat ($3,000 to $3,500 a year, Metronome, January 2026).

Microsoft Copilot in Excel

Copilot in Excel is Microsoft's spreadsheet copilot, and its June 25, 2026 release for finance added pre-built skills and federated connectors, generally available to Microsoft 365 Copilot customers on Excel for Web, Windows and Mac.

Where it wins: it sits where the model is already used. The connectors are Moody's, CB Insights, Daloopa, Morningstar and PitchBook, with FactSet in preview until July. LSEG and S&P Global complete the eight. Skills partners include Rogo, samaya.ai and Vena, and Microsoft's April 2026 acquisition of Fintool, a filings assistant whose site now redirects to Microsoft 365, signals where the filings work is heading.

Where it falls short: it works cell by cell inside a workbook. There is no corpus of filings, transcripts or broker notes to search, no cross-company workflow that runs on its own, and no lineage beyond the connector's citation.

Google Finance and Gemini

Google Finance relaunched on June 25, 2026 with an Android app, portfolios built from uploaded files, an AI research tool, scheduled briefings and Key Moments, in more than 100 countries since April 2026, with no model or price named.

Where it wins: Gemini's deep-research agent scored highest overall of the four agents in JPMorgan's Deep FinResearch Bench, 2.31 against the analysts' 2.84, and the relaunch is free. FactSet announced a Google Cloud alliance on June 30, 2026 that embeds Gemini in the FactSet Workstation, with agents planned for portfolio operations, deal advisory and corporate finance.

Where it falls short: it is a consumer product with 69.6% factuality in the same bench, no entitlements and no audit trail. The alliance puts Gemini inside FactSet's workstation and leaves Google Finance where it was: a fast, free reading layer for public data.

AI research platform vs chatbot for investment research: the five structural gaps

The difference is architectural: five things a desk is audited on live in the platform and outside the chat window, and each can be tested with the NVIDIA question above.

1. Entitled content a login cannot see

Broker research is licensed to a firm and to named users inside it, and AllMind AI enforces those entitlements per user, so an agent inherits the asking user's rights without widening them. Expert Insights sits on the other side of that line: those transcripts come with the AllMind AI subscription rather than with a contract the desk signs itself. A general assistant's connector sees what the vendor licensed to that connector: Third Bridge and Guidepoint through Claude, PitchBook through Perplexity, none of it the notes your sell-side relationships entitle you to. 69% of the 35 asset managers surveyed by Substantive Research and Aiera (July 16, 2026) named licensing restrictions the top barrier.

2. A trace from each figure to its passage, then a verification pass

Every figure in an AllMind AI deliverable opens the passage behind it with the arithmetic shown, and a verification pass re-checks figures before a report ships. A chat answer cites the page or connector result it read and stops there. Apply JPMorgan's factuality scores to a real deliverable: an earnings preview with 50 checkable claims would carry about seven wrong ones at OpenAI's 86.0%, twelve at Perplexity's 75.6% and 23 at Grok's 53.2%, with no way to tell which.

3. A log of every prompt and every export

AllMind AI logs each question and each export, and entitlements are enforced per user, so the log shows who asked what and what left the platform. FINRA's 2026 Annual Regulatory Oversight Report (December 9, 2025) names stored prompt and output logs an effective practice for generative AI, and the SEC's exam priorities for FY2026 (November 17, 2025) ask for policies to supervise AI use. An enterprise assistant can log prompts and responses; it cannot log an entitlement check it never ran.

4. Internal warehouses queried in place and joined to the external corpus

Half of an institutional question is the firm's own data. AllMind AI reads Snowflake, Databricks and S3 in place through an IAM role scoped to the tables it is granted, and the ontology resolves a warehouse row, a filing and a broker note to the same company, so an agent traverses relationships. Claude for Financial Services has had Snowflake and Databricks connectors since July 15, 2025; a connector returns a query result into one conversation, and the join with everything else lives only in that chat's context.

5. Workflows that run for hours

An AllMind AI agent works a question for minutes, hours or across days: a supplier map, three quarters of estimate revisions, two broker notes and an expert call, returned as one deliverable with lineage. Deep Research in ChatGPT, Gemini or Perplexity returns a report in minutes and stops. Depth is where accuracy falls: Fin-RATE (version 4, June 10, 2026) measured accuracy across 17 models dropping 18.60% and 14.35% as tasks widened from one document to longitudinal and then cross-entity analysis. That is the case for AI agents in investment research.

How does AllMind AI compare with Perplexity Finance and Claude for Financial Services?

The split is the same in both cases: the assistant wins on price and speed to a first answer, and AllMind AI wins when entitled content, internal data and lineage are part of the job.

AllMind AI vs Perplexity Finance for investment research

Choose Perplexity if the job is cited answers over public data through Computer for Professional Finance's connectors, and nothing licensed or internal enters the loop. Choose AllMind AI if the work crosses expert calls, broker research under your entitlements, licensed market data, live earnings and your own warehouse, and someone has to defend each figure afterward.

Ten seats of Perplexity Enterprise Max at the reported $325 per seat per month is $39,000 a year, inside the third-party estimate range for an AlphaSense contract ($9,250 to $51,000 across 38 deals, Vendr, February 2026). What separates them is architecture: Perplexity holds no entitled content, no entitlement inheritance and no export log, and AllMind AI's ontology, lineage and audit layer exist for those three. AllMind AI's own limit is setup, agreeing with the vendor what to connect before the first login.

AllMind AI vs Claude for Financial Services

Choose Claude for Financial Services if your team lives in Excel, PowerPoint and Word, wants the ten agent templates shipped May 5, 2026, and can accept unpublished pricing and results that live inside a conversation. Choose AllMind AI if you need those sources plus your own broker entitlements and internal data resolved to the same entities, an agent that runs for hours, and a verification pass before anything ships.

Three checks decide it in a pilot. Ask whether a figure in the Claude output opens a passage and a calculation. Ask where the join between a Snowflake result and a filing lives once the conversation closes. Ask what the log shows a compliance reviewer for one analyst's week; the security page lists what AllMind AI records. The checks are about what surrounds the model, and AllMind AI runs frontier models from several vendors under zero-retention terms.

ChatGPT vs Claude for financial analysis: which is the best LLM for financial analysis?

Claude leads on agentic filing questions, at 64.37% for Opus 4.7 on the Vals Finance Agent leaderboard of June 4, 2026, and OpenAI leads on factuality inside long reports, at 86.0% in JPMorgan's Deep FinResearch Bench of April 2026. The gap between them is smaller than the gap between either and a professional analyst, who scored 2.84 to the best agent's 2.31 in the same bench. Pick by your workflow's failure mode; the ranking moves each quarter.

The trend is steep, from FinanceBench's 81% of a 150-question sample answered incorrectly or refused by GPT-4-Turbo with retrieval in November 2023 to 64.37% correct on a harder agentic test in June 2026, and none of the systems on these leaderboards clears 65% on expert work. The deciding layer is therefore retrieval over entitled content, lineage and a verification pass around the model, and the platforms swap models as leaderboards move: Rogo put Claude Opus 5 inside its product on July 24, 2026. The best AI copilots for equity analysts scores tools on that layer.

When is a general assistant the right answer, and what should you ask before standardizing on one?

A general assistant is the right answer when the work is one person, one document or one draft, and no entitled content or internal data is involved. Five cases come up in most evaluations.

• A solo analyst whose whole week is public filings and transcripts: Claude or ChatGPT for reading and drafting, plus a self-serve data plan such as Koyfin Plus at a published $39 a month.
• An individual investor or a student pitch team: an assistant reading public filings is the whole job, and there is no firm entitlement map to inherit in the first place.
• A one-off reading of a single 10-K or transcript: drop the document into Deep Research or Claude, ask your questions and move on.
• A corporate FP&A team whose work is the workbook: Copilot in Excel with the June 25, 2026 connectors sits where the model already is.
• Drafting, rewriting and quick code at any firm size, provided the firm's AI policy covers the tool and the content stays public.

The limit on the other side is AllMind AI's own: it is not self-serve, and depth comes from connecting internal systems, agreed one system at a time under a scoped cloud role. Copy the checklist below into the evaluation memo.

Ten questions to ask before standardizing a research team on a general assistant

1. Which licensed content (broker research, expert transcripts, S&P, FactSet, LSEG, MSCI) can this
   login query under our contracts, and which connectors substitute for each?
   Pass: a written list per content class, confirmed with each licensor.
2. When the assistant states a number, can a reviewer open the passage and see the calculation?
   Pass: click-through on ten sample figures.
3. What re-checks a figure before it enters a client deliverable?
   Pass: a named step with an owner.
4. Does the log record prompt, response, sources retrieved and every export, per user?
   Pass: a one-week log export for one analyst (FINRA 2026: store prompt and output logs).
5. Can an agent see anything the asking user is not entitled to?
   Pass: a reduced-entitlement test account returns nothing it should not.
6. How does our own data get in: upload per chat, a connector, or the warehouse queried in place?
   Pass: named path, named role, named tables.
7. Can one task run unattended for an hour across twenty sources and return a deliverable?
   Pass: a timed run on a live name in our coverage.
8. What are the retention and training terms on our prompts and documents, in the contract?
   Pass: clause reference.
9. What is the annual cost at our seat count, including the data plan each seat needs?
   Pass: one per-seat figure (Perplexity Enterprise Max reported at $325 per seat per month;
   Claude for Financial Services unpublished; institutional platforms quote-based).
10. What happens to the work when the vendor changes the model or an analyst leaves?
   Pass: workflows and outputs saved as artifacts the firm owns, never in personal chat threads.

Frequently Asked Questions

Can ChatGPT, Claude or Perplexity replace an institutional research platform?

Not for a team whose research is audited. The assistants run frontier models with FactSet, PitchBook and Daloopa connectors. A chat login still holds no entitlement to the firm's broker research, traces no figure to its passage, keeps no export log and cannot query the firm's warehouse in place. The crossover case is work with no entitled content and nothing internal to connect, where an assistant plus a monthly data plan wins on cost and setup time.

What is the best LLM for financial analysis?

On the Vals Finance Agent leaderboard of June 4, 2026, Claude Opus 4.7 led at 64.37%, ahead of Claude Sonnet 4.6 at 63.33%, Muse Spark at 60.59% and DeepSeek V4 at 60.39%, on 537 expert questions over SEC filings. In JPMorgan's Deep FinResearch Bench (April 2026), OpenAI's agent had the highest factuality at 86.0% while Gemini scored highest on report quality. The ranking moves every quarter, so institutional platforms hold the retrieval, entitlement and audit layer constant and swap the model underneath.

AllMind AI vs Perplexity Finance for investment research: which one should a fund use?

Perplexity fits an individual or a small team that wants cited answers over public data, at plans reported from $20 a month for Pro to $325 per seat per month for Enterprise Max (July 2026). AllMind AI fits a fund whose work crosses entitled broker research, Expert Insights transcripts included in the subscription, licensed market data and its own warehouse, with each figure traced to a passage and each export logged. Its setup starts by mapping which systems to connect, and pricing is quote-based, so anyone who needs a login this week should keep the monthly tool.

Can a research team use AllMind AI alongside ChatGPT or Claude?

Yes, and the split is easy to police. Drafting, rewriting and quick reads of public documents go to the assistant under the firm's AI policy, while anything touching entitled content, internal data or a client deliverable stays on the governed platform with its entitlements, lineage and export log. The test for any task is whether a compliance reviewer could reconstruct where each number came from a month later.

AllMind AI is the AI-native research platform for institutional equity teams. If you want proof on your own work, send us the workflow you want tested.