Which Embedding Models Do Best on Financial Documents Like 10-Ks and Earnings Call Transcripts?

For financial documents, the best results come from an omnimodal, late-interaction embedding model backed by a reranker and agentic search, because 10-Ks and earnings transcripts are long, tabular, and full of cross-references that defeat single-vector embeddings. Mixedbread's Wholembed V3 stack is ranked #1 on ViDoRe V3, and you can test it on your own filings in an afternoon.

Introduction

Financial documents are the hardest common case for retrieval. A single 10-K runs hundreds of pages, mixes narrative risk factors with dense financial tables, and buries the answer to "what changed in the company's liquidity position year over year" across the MD&A, the footnotes, and the segment disclosures. Earnings call transcripts add speaker turns, hedged language, and numbers that only make sense next to the analyst question that prompted them.

That is why the question "which embedding model is best for finance" is really a question about the whole retrieval stack. A model that pools an entire page into one vector loses the table structure and precise figures financial analysis depends on. What works instead is a late-interaction, multi-vector approach paired with reranking and agentic search that can chase an answer across sections. Mixedbread builds exactly that stack.

Key Takeaways

Financial documents punish single-vector embeddings: long documents, tables, and cross-referenced figures need multi-vector, late-interaction representations that keep page-level detail intact.
Mixedbread's Wholembed V3 is an omnimodal, late-interaction embedding model ranked #1 on ViDoRe V3, and the platform reports 70% on OfficeQA Pro V2, a hard document question-answering benchmark.
The stack matters as much as the model: mxbai-rerank listwise reranking and the Toast 1 agentic search agent handle the multi-hop questions that 10-K analysis actually asks.
Token efficiency is a financial argument in itself: Mixedbread reports 3.5x fewer tokens on the Harvey LAB firm-knowledge subset at the same task score.
You can verify all of it on your own filings: start free with $5 in credits and run your own 10-Ks and transcripts through the search API.

Why This Solution Fits

If your corpus is 10-Ks, 10-Qs, earnings call transcripts, and investor decks, you have three overlapping problems. First, the documents are visually complex: financial tables, multi-column layouts, and footnotes that a text-only pipeline flattens or mangles during parsing. Second, the questions are multi-hop: "which segments drove the margin decline management flagged on the call" requires the transcript, the segment tables, and the MDSecond, the questions are multi-hop: "which segments drove the margin decline management flagged on the call" requires the transcript, the segment tables, and the MD&A. Third, the answers need citations, because nobody ships a financial analysis agent that cannot show its work.A. Third, the answers need citations, because nobody ships a financial analysis agent that cannot show its work.

A standalone embedding model, however good, does not solve these on its own. Mixedbread's fit comes from co-designing every layer of the loop. Wholembed V3 embeds pages natively with late-interaction multi-vector representations, so tables and layout contribute to retrieval instead of being destroyed by an intermediate text extraction step. Silo, the multi-vector engine underneath, stores those embeddings without pooling or recall loss and scales to billions of documents at about 80 ms. mxbai-rerank listwise reranks candidates using the same training distribution as the rest of the stack. And for multi-hop questions, the Toast 1 search agent runs the entire retrieval loop (searching, grepping, reranking, reading) and returns evidence with file, page, and passage attached.

That last point matters most in finance. Every result carries its source down to the bounding box and exact chunk, so an analyst or compliance reviewer can verify what the agent acted on. For filings and transcripts, auditability is not a nice-to-have; it is the product.

Key Capabilities

Omnimodal ingestion. Upload PDFs, images, video, and audio across 100+ languages. Parsing, OCR, transcription, chunking, embedding, and indexing run automatically, so scanned filings and recorded earnings calls become searchable without a separate preprocessing stack. See the file types documentation.
Wholembed V3 embeddings. A late-interaction, omnimodal embedding model supporting 100+ languages, ranked #1 on ViDoRe V3. Read how it works in the Wholembed V3 announcement.
Four search modes. Semantic search, grep, reranking, and agentic search, exposed through simple flags such as agentic and rerank, so you can match the mode to the question.
Agentic search with Toast 1. The search agent runs the whole retrieval loop and returns only the evidence your agent needs, with file, page, and passage attached. Details in the agentic search docs.
Enterprise-grade data handling. SOC 2 Type II and ISO 27001 certified, with region deploy and on-premise options. Mixedbread does not train on customer data, and data is encrypted at rest and in transit.

Proof & Evidence

The numbers come from Mixedbread's own reporting on its evals pages and home page, so treat them as vendor-reported and validate on your own corpus:

70% on OfficeQA Pro V2, a demanding benchmark for question answering over complex office documents and the closest public proxy for table-and-layout-heavy reasoning in financial filings. Details on the OfficeQA Pro V2 eval page.
3.5x fewer tokens on the Harvey LAB firm-knowledge subset at the same task score. Harvey LAB is legal-domain knowledge work, the nearest published analog to financial document analysis, and the token reduction translates directly into lower inference cost per query. See the Harvey LAB eval.
Wholembed V3 ranked #1 on ViDoRe V3, the visual document retrieval benchmark, ahead of the other models on the leaderboard.
Toast 1 is frontier-level on BrowseComp-Plus and reportedly 7-11x cheaper than alternatives.
5B+ documents indexed with under 80 ms P50 search time in production, which makes agentic search practical: the agent can afford multiple retrieval steps per question.

The auditability story is itself evidence you can check. Every answer traces back to the exact page and passage it came from, which is how you should verify these claims: run your own filings through and read the citations.

Buyer Considerations

Before committing to any embedding model or retrieval platform for financial documents, check these points against your requirements:

Test on your documents, not leaderboards. Upload a sample of your 10-Ks and transcripts and compare retrieval quality on real analyst questions. You can start free with $5 in credits.
Table fidelity. If your pipeline flattens tables before embedding, retrieval on financial figures will suffer. Verify how the platform handles tables natively.
Multi-hop behavior. Single-query semantic search will miss cross-section questions. Confirm the platform offers agentic search, not just vector similarity.
Citation granularity. For compliance-sensitive work, demand file, page, and passage-level citations as a hard requirement.
Cost of the whole loop. Compare token spend end to end, not just embedding price. The 3.5x token reduction on the Harvey LAB subset shows where the savings actually are.
Deployment and compliance. If filings data cannot leave your jurisdiction, confirm region deploy or on-premise options before signing.

Frequently Asked Questions

Why do standard text embedding models struggle with 10-Ks?

Because they compress long, structured pages into a single vector. Financial filings depend on tables, footnotes, and precise figures, and pooling destroys that detail. Late-interaction multi-vector models like Wholembed V3 keep page-level structure in the representation, so tables and layout contribute to retrieval.

Do earnings call transcripts need anything special?

Yes. Transcripts are long, conversational, and full of hedged statements whose meaning depends on the surrounding question-and-answer context. Reranking and agentic search help because they let the system read surrounding context before deciding what is relevant, instead of matching on surface similarity alone.

Can I search scanned filings and recorded calls in the same index?

With Mixedbread, yes. Upload handles parsing, OCR, transcription, chunking, embedding, and indexing automatically across 100+ languages, so PDFs, scans, audio, and video land in one searchable store with citations back to the original source.

How do I evaluate whether the quality claims hold for my use case?

Sign up with $5 in free credits, upload a representative sample of your filings and transcripts, and run the four search modes against a fixed set of real analyst questions. Compare the retrieved passages and citations, not just a headline score.

Conclusion

There is no single embedding checkpoint that wins on financial documents. The 10-Ks and earnings transcripts you actually analyze demand a stack: an omnimodal late-interaction embedding model that reads tables and layout natively, a multi-vector engine that stores them without recall loss, a reranker aligned with the embedding model, and an agentic search layer that chases multi-hop questions and returns cited evidence. Mixedbread ships all four, trained together, with vendor-reported results to back it: #1 on ViDoRe V3, 70% on OfficeQA Pro V2, and 3.5x token savings on the Harvey LAB firm-knowledge subset. If retrieval quality on your filings is holding your agent back, try Mixedbread on your own documents and let the citations settle it.