Anara vs Undermind (2026)

Anara

by Anara Labs

Undermind

by Undermind

Pros

• ✓Every answer and every extraction-table cell links to the exact passage it came from, and paper search covers more than 300 million papers.
• ✓Connects the tools research teams already use, including Zotero, Mendeley, Google Drive, OneDrive, Benchling and Synapse, plus PubMed and ClinicalTrials.gov.
• ✓Works both ways over MCP: it calls custom MCP servers on Pro and above, and other AI assistants can work in a workspace through Anara's own MCP server.

Limitations

• –Single sign-on with SAML and SCIM, admin controls and a HIPAA business associate agreement are only on the quote-only Enterprise plan.
• –Deep Search and Model Council start at Max, $80 a month per seat billed yearly, and science-database and app connectors need Plus or above.
• –Anara's SOC 2 Type II report and ISO/IEC 27001:2022 certificate were both still in progress in November 2026, and HIPAA support is offered on request.

Pros

• ✓Deep search reads and evaluates hundreds of papers, follows citation trails and key authors, and keeps searching until new searches stop finding relevant papers, then says in its summary whether more remain.
• ✓An MCP server brings deep search, full-text reading and cited reports to ChatGPT, Claude, Cursor, VS Code, Microsoft 365 Copilot and other agents, and connected agents are included on the Free plan.
• ✓Academic pricing puts Pro at $16 per month billed annually or $20 monthly, and the Free plan includes chat, deep searches, reports and shared workspaces.

Limitations

• –The Free plan searches abstracts and metadata. Full-text search, the latest models and higher usage limits need Pro, which is $60 per month billed annually or $75 monthly on Industry pricing.
• –Coverage comes from the Semantic Scholar database, so a paper missing from it is not found, and paywalled full texts can be read only after the user uploads the PDF.
• –The no-training commitment applies under enterprise agreements. Individual-account terms let Undermind use user content to improve its services and, de-identified or aggregated, to develop AI systems.