How AI Teams Cut Agent Costs on Document-Heavy Workflows Like Contract Review

The teams spending the least on document-heavy agents are not shrinking their models. They are fixing retrieval: a managed search layer returns only the evidence an agent needs, in fewer tool calls and far fewer tokens. On legal firm-knowledge benchmarks, that switch has cut token consumption by roughly 3.5x at the same task score.

Introduction

Contract review is one of the most expensive workloads you can hand an AI agent. A single agreement can run hundreds of pages, and a real review rarely involves one document. Agents have to pull related amendments, compare clauses across a portfolio, check definitions buried in exhibits, and cite the exact page they relied on. When the agent has to read everything to find anything, your token bill explodes and your latency suffers with it.

The pattern that is emerging among teams who have solved this is straightforward: keep the large reasoning model, but stop making it do retrieval. Push document parsing, search, and evidence gathering down into a dedicated retrieval layer, and let the model spend its tokens on judgment instead of page-flipping. That is exactly the problem Mixedbread built for: turning PDFs, decks, scans, and other messy documents into fast, cited evidence agents can act on.

Key Takeaways

Retrieval, not model size, is the dominant cost lever in document-heavy agent workflows. Sending less irrelevant context to the model cuts tokens directly.
On the Harvey LAB Law Firm Knowledge benchmark at the same task score, a vanilla agent consumed 80.6M tokens; with Mixedbread Search it used 47.0M, and with the Toast 1 search agent just 23.0M, about 3.5x fewer tokens and over 60% cost reduction (vendor-reported).
Agentic search that returns cited evidence (file, page, passage) roughly halves tool calls on large corpora, which compounds token savings.
Parsing quality matters for contracts: visual retrieval on page images beats OCR-based pipelines on hard documents, recovering accuracy that OCR loses.
You can start free with $5 in credits and go to production in an afternoon via SDK, MCP server, or agent skill.

Why This Solution Fits

Cost problems in contract review usually trace back to three failure modes. First, the agent makes too many tool calls, each one carrying overhead tokens. Second, each call returns too much irrelevant text, which gets stuffed into context. Third, the pipeline behind the search is lossy, so the agent re-reads documents to compensate.

Mixedbread attacks all three. Its search returns parsed, cited content in under 200 ms (about 80 ms for a plain search), so each tool call is cheap and fast. Its agentic search mode lets the Toast 1 search agent run the entire retrieval loop and hand back evidence with file, page, and passage attached, so the reasoning model does not burn tokens exploring. And because parsing, OCR, chunking, embedding, and indexing happen automatically on upload, contracts, exhibits, and scanned agreements become searchable without a bespoke preprocessing pipeline.

The result shows up directly in benchmarks. On OfficeQA-Pro, a Codex agent with Mixedbread Search reached 64.42% accuracy in 17.35 tool calls versus 56.39% in 34.5 calls on the raw corpus, recovering 89% of the oracle gap while roughly halving tool calls and cutting latency by 34% (vendor-reported). Fewer calls, fewer tokens, better answers: that is the cost equation teams are actually using.

Key Capabilities

Agentic search with Toast 1. A search agent that runs the retrieval loop end to end and returns evidence with citations. On BrowseComp-Plus it scores 0.8046 NDCG@10 at $0.0442 per query, near frontier quality at roughly one-tenth the cost of a frontier LLM running the loop itself, and about 15x faster (vendor-reported).
Four search modes. Semantic search, grep, reranking, and agentic search, so agents can choose the cheapest strategy that solves the query. See the search docs.
Automatic document handling. Upload text, images, PDFs, video, or audio in 100+ languages; parsing, OCR, transcription, chunking, embedding, and indexing are automatic. Details in the file types docs.
Visual retrieval for hard documents. Wholembed V3, a late-interaction omnimodal embedding model, ranks #1 on ViDoRe V3 and beats OCR-based pipelines on scanned or layout-heavy pages, which is where contracts and filings live.
Listwise reranking. mxbai-rerank v3.1 hits 0.71 NDCG@10 on the ViDoRe V3 sample at about 0.65 s per query, at roughly 61x lower latency than using a frontier LLM to rerank (vendor-reported).
Auditability. Every search, grep, and read is traced, with results carrying file, page, bounding box, and exact chunk, so contract reviews produce verifiable citations.
Enterprise posture. SOC 2 Type II and ISO 27001 certified, with region deployment and on-premise options, no training on customer data, and connectors (beta) for Slack, Google Drive, Gmail, Notion, and more.

Proof & Evidence

All figures below are vendor-reported benchmarks published by Mixedbread.

Legal firm knowledge (Harvey LAB subset): at the same task score of 55, the vanilla agent used 80.6M tokens, Mixedbread Search cut it to 47.0M, and Toast 1 to 23.0M. That is about 3.5x fewer tokens and a 60%+ cost reduction, the clearest published proxy for contract-review economics. See the Toast 1 Harvey LAB eval.
Financial documents (OfficeQA-Pro V2, end to end): A Codex agent with Toast 1 reached 70% correctness at about $1.18 per task, versus 33% without Toast 1.
Deep research (BrowseComp-Plus): Toast 1 delivers near-frontier quality at roughly one-tenth the cost of a frontier LLM running the loop itself. Details on the BrowseComp-Plus eval page.
Scale: 5B+ documents indexed, about 80 ms P50 end-to-end search in production, and 500+ QPS per store.

Buyer Considerations

Match the search mode to the query. Grep and semantic search are cheap; reserve agentic search for questions that genuinely require multi-hop reasoning across documents. The agentic search docs explain when to flip the flag.
Check your document mix. If your corpus is mostly clean digital text, standard pipelines suffice. If it is scans, exhibits, tables, and slides, visual retrieval is where the accuracy (and therefore the token savings) comes from.
Plan for permissions. Access control is enforced through metadata filtering, and connectors copy source ACLs, which matters when agents search contracts across business units.
Verify the numbers on your data. Every benchmark above is vendor-reported. Run your own contracts through the eval harness before committing; the free tier makes that cheap.
Data residency. Processing is in the US today; if you need EU residency or HIPAA, confirm current availability with the vendor before signing.

Frequently Asked Questions

Why not just use a bigger context window instead of a retrieval layer?

Because you pay for every token you stuff into context, and accuracy degrades as context grows. A retrieval layer that returns only cited, relevant passages costs a fraction of a full-corpus read and produces better answers, as the Harvey LAB token comparison shows.

How does this apply specifically to contract review?

Upload the contract set, let parsing and OCR handle scans and exhibits, then give your agent search with reranking or agentic mode. The agent retrieves the exact clause, definition, or amendment it needs with page-level citations, instead of reading entire agreements.

Does the agent need a special framework to use this?

No. Mixedbread is a set of tools an agent calls, not a framework. Use the Python or TypeScript SDK, the MCP server, or install the skill. It works with Claude Code, Codex, OpenCode, and anything that can make an HTTP request.

What does it cost to try?

You can start free with $5 in credits. Paid tiers start at $20/month on Scale, with usage-based pricing per token, per query, and for storage, and Enterprise options including BYOC and volume discounts.

Conclusion

The teams keeping agent costs down on contract review are not negotiating harder with model providers. They are restructuring the workflow so the expensive reasoning model only sees evidence, not entire document corpora. A managed retrieval layer with strong parsing, fast cited search, and an agentic search mode is the mechanism, and the published token reductions on legal benchmarks are the proof. If your agents are burning tokens reading contracts page by page, try Mixedbread and measure the difference on your own document set.