Claude vs Gemini for Obsidian Notes

Obsidian is a Markdown-based note-taking app that stores notes as plain files on your device. Neither Claude nor Gemini connects to Obsidian directly in a live session — you bring notes to the AI, not the AI to your notes. The question is how: which model handles large pastes of Markdown well, which handles the links and structure Obsidian notes often rely on, and how much of the vault you can realistically give either model at once. A tool like fenbs keeps shared project facts and source references outside the vault, available to any connected assistant without requiring you to export notes each time.

For a guide specific to Claude and Obsidian, search the AI models topic. For the general Claude and Gemini comparison, see Claude vs. Gemini. This page focuses on the choice between models for Obsidian-based workflows.

How each model reads Obsidian notes

Both Claude and Gemini read Markdown text well. A note pasted into either assistant will be read as structured text: headings, bullet lists, bold and italic text, and code blocks all make sense to both models. What neither model does is follow wikilinks ([[Note Title]]) — they see the link text but cannot navigate to the linked note unless you paste that note separately.

For single-note tasks (summarizing a note, extracting action items, writing a continuation), paste the note and ask. Both models handle that well. For tasks that depend on linked notes — "summarize this note and the three notes it links to" — you need to paste all four manually or use a community plugin that assembles them for you.

Claude's current context window of 200,000 tokens fits roughly 150,000 words of plain text — a substantial vault section, but not a large research vault in its entirety. Gemini 1.5 Pro's one-million-token window can hold more. For most tasks you will not hit either limit; the practical ceiling is the time it takes to select and paste the relevant notes.

Community plugins and vault access

The Obsidian community has produced plugins that bridge the gap between the vault and AI assistants. Some plugins connect to the Claude API or the OpenAI API and allow the model to query notes directly in the app. These change the interaction model significantly: instead of pasting, the plugin assembles the context and sends it to the model.

For Claude, check the Obsidian community plugin directory for current Claude integrations. For Gemini, the options are fewer; most existing AI plugins target OpenAI or Anthropic APIs rather than Google's. Check the Obsidian plugin directory and Gemini's developer documentation for the current state.

When using any plugin that sends vault content to an AI API, check what data is transmitted and to which endpoint. Obsidian stores notes locally by default; a plugin that sends them to a third-party API changes that. Read the plugin's documentation before enabling it on a vault that contains sensitive material.

Retrieval and access limits to test

Before relying on either model for vault-based work, test the limits that matter for your workflow. These questions narrow down quickly what each tool can actually do for you.

• How much of the vault can you paste in one call before hitting the context limit?
• Does the model handle your note structure — nested headings, callouts, embedded blocks — or does it misread the formatting?
• If you use wikilinks as the primary navigation structure, can you assemble the linked notes manually or through a plugin before pasting?
• How does each model handle a note that references an external source by URL? Can it fetch the source, or does it reason only from the text in the paste?

Neither Claude nor Gemini fetches URLs automatically in the standard chat interface. If a note contains a reference to an external source, the model reasons from the note's own text about that source, not from the source itself. If the source text matters, paste it separately.

Source references and link management

Obsidian users often build link graphs across hundreds of notes. When you want an AI to reason across that graph, you need to give it the relevant slice. The practical challenge is knowing which notes are relevant before asking the question — which requires some of the synthesis you wanted the AI to do.

A workable approach: keep a "current project" note in Obsidian that links to the notes relevant to the active task. Before a session, paste that note and the linked notes it references. That gives the model a scoped view without overwhelming it with unrelated vault content.

For references to external sources — papers, articles, vendor documentation — track where they live and their current status outside the vault as well. A note that says "see [paper X]" is useful for your own navigation; a shared record that says "paper X, confirmed March 2026, link: [url]" is more useful to an assistant that cannot navigate your vault.

How to do this with fenbs

1. Create a free account and add a project for the research or writing work your Obsidian vault supports.
2. Add key decisions and confirmed facts as context notes in fenbs. These are the shared facts that should reach any session, not just sessions where you remember to paste the right note.
3. Track sources in Links and listings: each important reference, its URL, and when it was last verified.
4. Add tasks for the active work. Record where things stand so the next session continues from the current position rather than re-reading the full vault.
5. Connect a supported assistant from Integrations. Paste the relevant vault notes alongside the fenbs project context for the best starting point.

Choosing between Claude and Gemini for Obsidian work

• Use Claude if your vault notes are dense and multi-part, and you want the model to follow complex structures and constraints reliably.
• Use Gemini if your vault is very large and you want to paste more of it at once, or if your workflow connects to Google Docs for the final output.
• Use either for summarizing, extracting action items, and writing continuations of a single note.
• Test on a representative note from your actual vault before choosing: Markdown structure varies and some formats cause parsing issues in one model but not another.