Mixedbread AI
Multimodal search & retrieval API for AI agents in 100+ languages
For deep-research agents that must reason across text, images, audio, and video, Mixedbread is the retrieval API to beat—top benchmark accuracy, sub-200ms latency, and a genuinely unified multimodal pipeline. Its listwise reranking and Toast 1 model now match frontier LLMs at a fraction of the cost. If you only need simple keyword search, lighter tools like Algolia are cheaper, but for cross-modal agentic workloads, Mixedbread justifies the premium.
Verified 4d ago · liveness 86/100 · cite: rightaichoice.com/tools/mixedbread-ai
- Deep-research agents that need to search across text, PDFs, images, audio, and video in 100+ languages
- Multimodal RAG applications requiring sub-200ms latency and high benchmark accuracy
- Coding assistants that rely on precise code and doc retrieval, with MCP and skills.sh integration
- Enterprise knowledge retrieval with SOC 2 Type II and ISO 27001 compliance, plus BYOB/BYOC
- Simple full-text search on small datasets where Algolia or Meilisearch are lighter and cheaper
- Teams that want to bring their own embedding models or run offline/air-gapped deployments
- Cost-sensitive startups that need predictable spending without usage transparency
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Skip Mixedbread if you need simple full-text search on small datasets (Algolia/Meilisearch are cheaper), or if you require custom embedding training, offline/air-gapped deployment, or full pipeline control—Mixedbread abstracts those away.
Search usage is billed separately from the subscription; for example, semantic search costs $4 per 1K queries plus $3.50 per 1K with reranking, so high-volume usage can add up quickly.
Mixedbread's freemium pricing fits startups exploring multimodal search, with a free tier and $20/mo Scale plan. Compared to enterprise search platforms like Algolia (which can run thousands per month), Mixedbread's usage-based model is cheaper for moderate volumes, but for pure full-text search on small datasets, Algolia's free tier may suffice. For agentic search, Toast 1 undercuts Claude Opus 5 and GPT-5.6 Sol by up to 10x.
In short
Mixedbread AI — Multimodal search & retrieval API for AI agents in 100+ languages. Best for Deep-research agents that need to search across text, PDFs, images, audio, and video in 100+ languages, Multimodal RAG applications requiring sub-200ms latency and high benchmark accuracy, Coding assistants that rely on precise code and doc retrieval, with MCP and skills.sh integration. Free to start; paid plans from $20/mo.
What's new in Mixedbread AI
Checked 4 days agoAcross the latest 5 updates: 4 feature updates and 1 launch.
Connect to Your OpenTelemetry Provider
Export every agent action as OpenTelemetry spans to Langfuse, Braintrust, or any OTLP collector.
Stream Agentic Search Traces
Agentic Search now supports streaming server-sent trace events for live debugging.
Introducing Toast 1
Toast 1, a specialized search model, matches Claude Opus 5 and GPT-5.6 Sol at 10x lower cost and 12x faster.
Faster search with a Wholembed v3 query engine
New query engine reduces encoding latency by 39% and end-to-end search latency by 21%.
Faster, higher-quality reranking with mxbai-rerank-v3.1-listwise
New default reranker matches GPT-5.6 Sol quality with 25-54% lower latency.
What people actually say about Mixedbread AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
8 mentions across 2 sources (Hacker News, Bluesky) · researched Jul 6, 2026.
- +True multimodal search across text, images, audio, and video in one API.
- +Sub-200ms latency with state-of-the-art accuracy on retrieval benchmarks.
- +Small, efficient models like mxbai-edge-colbert-v0 outperform larger alternatives.
- +Novel dimensionality pruning compresses embeddings 50:1 without quality loss.
- +Open-sourced reranker and embed models available on Hugging Face.
- −Very limited real-world production feedback — mostly research demos.
- −Advanced features require machine learning expertise to fully exploit.
- −Pricing lacks transparent cost examples at scale, risking bill shock.
- −No native no-code or Zapier integrations for less technical teams.
- −Vendor lock-in on proprietary retrieval models and APIs.
- • Indexing and storage costs scale with data volume — no fixed caps on free or Scale tiers.
- • Pay-as-you-go rates may surprise heavy users without spending limits configured.
Viability Score
How well maintained and how widely used is Mixedbread AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Multimodal ingestion: text, PDFs, images, audio, video
- Semantic search with fine-grained retrieval
- Lexical search with exact regex matching (stores.grep)
- Listwise reranking with mxbai-rerank-v3.1-listwise
- Wholembed v3 unified late-interaction model for audio/video
- Agentic search with multi-step reasoning
- Toast 1 specialized search model via Chat Completions API
- Sub-200ms response times
- Streaming agentic search traces (server-sent events)
- Agentic search observability dashboard
- Chunk listing and metadata filters with numeric sorting
- File upload to stores (PDF, code, images, video, audio)
- CLI for bulk file operations and CI/CD integration
- MCP server integration for Claude Code, Cursor, and others
- Agent skills (skills.sh) for coding agents
About Mixedbread AI
Mixedbread is a hosted retrieval API that lets AI agents search across text, PDFs, tables, images, video, and audio in 100+ languages—without building your own embedding or reranking pipeline. It's designed for teams building deep research tools, document QA systems, and coding assistants that need fast, accurate answers from large, mixed corpora. The service abstracts away the typical three-stage pipeline: upload any file type, search with natural language, and get precisely ranked results in under 200ms. Under the hood, Mixedbread uses proprietary models to push retrieval quality. The Wholembed v3 query engine delivers 39% lower query encoding latency and 21% faster end-to-end search, while the mxbai-rerank-v3.1-listwise reranker matches GPT-5.6 Sol ranking quality at 61x lower latency. For agentic workloads, Toast 1 is a specialized search model that matches or outperforms Claude Opus 5 and GPT-5.6 Sol while being up to 10x cheaper and 12x faster—ideal for cost-sensitive, latency-sensitive agent loops. You can integrate via Python and TypeScript SDKs, a CLI for bulk operations, an MCP server for Claude Code and Cursor, and drop-in agent skills via skills.sh. The dashboard includes observability for tracing agent runs and tuning retrieval. Security is enterprise-grade: SOC 2 Type II and ISO 27001 certified, with optional Bring Your Own Bucket (BYOB) for indexing AWS S3 data ephemerally. Pricing is transparent and usage-based: a free Starter tier with $5 in one-time credits, a $20/month Scale tier with included credits, and custom Enterprise plans with volume discounts, dedicated infrastructure, and BYOC. Compared to full-text engines like Algolia or Meilisearch, Mixedbread is purpose-built for multimodal, agentic retrieval, integrating deeply with LLM workflows via MCP, LangChain, and LlamaIndex.
Behind the Verdict
Mixedbread stands out in the crowded retrieval space by treating multimodal and agentic retrieval as a first-class problem, not an afterthought. The service's core value is its proprietary models—Wholembed v3 for unified late-interaction retrieval across text, audio, and video, and Toast 1 for agentic search—which together deliver benchmark-topping accuracy on tasks like BrowseComp-Plus and OfficeQA-Pro while keeping latency under 200ms. The listwise reranker (mxbai-rerank-v3.1-listwise) adds a layer of quality that pointwise rerankers can't match, especially for instruction-heavy queries. The API is genuinely developer-friendly: you upload files, create a store, and search with natural language. The SDKs for Python and TypeScript are well-documented, the CLI is handy for bulk operations, and the MCP server integrates with Claude Code and Cursor out of the box. The agent skills via skills.sh reduce the setup friction for coding agents, and the observability dashboard lets you see exactly what the agent did. That said, this is not a tool for teams that want to control every stage of the pipeline. Mixedbread abstracts away embedding training, index tuning, and infrastructure—so if you need custom embeddings or offline deployment, it's not the right fit. Pricing is usage-based, which can be unpredictable for high-volume workloads, though the free tier and transparent unit costs help you estimate. The Enterprise tier's BYOB/BYOC options address data sovereignty concerns, but they're not available on lower tiers. Overall, if you're building deep research tools, document QA systems, or coding assistants that need to reason across modalities, Mixedbread is worth a serious look. It's more expensive than lightweight search engines for simple keyword tasks, but for agentic, multimodal retrieval, it's a strong choice.
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Real-world workflow fit
Concrete scenarios for the personas Mixedbread AI actually fits — and what changes day-one when you adopt it.
Upload a batch of PDFs and DOCX files to a new store using the CLI or Python SDK, then query with natural language to extract answers.
Outcome: Get precise, ranked results in under 200ms, with automatic parsing of tables and layouts, enabling fast, accurate Q&A.
Install the Mixedbread Search skill via skills.sh into Claude Code or Cursor, then use the MCP server to search codebases during coding sessions.
Outcome: The assistant retrieves relevant code and docs without manual setup, improving code quality and reducing context-switching.
Enable Bring Your Own Bucket (BYOB) to index AWS S3 data ephemerally, then use listwise reranking to improve answer accuracy.
Outcome: Data stays in their cloud, retrieval quality matches frontier models at lower latency, and SOC 2/ISO 27001 compliance is maintained.
Use Cases
- Build a multimodal search engine that retrieves text, images, audio, and video simultaneously.
- Create a conversational AI agent with persistent memory using Mixedbread's agentic search.
- Implement semantic search over PDFs and documents in 100+ languages.
- Add web search capabilities to your application via the store-compatible API.
- Use listwise reranking to improve retrieval accuracy in your RAG pipeline.
- Integrate search into coding agents (Claude Code, Cursor) with the Mixedbread Search skill.
Models Under the Hood
as of 2026-08-30
Limitations
- Pricing tiers impose rate limits: the free tier allows 100 requests per minute, while the Scale tier permits 1,200 queries per minute and 360 ingestions per minute; Enterprise offers custom limits.
- Usage-based pricing includes semantic search at $4 per 1K queries, with an additional $3.50 for reranking.
- The service is API-first, requiring developer integration, with no dedicated desktop or mobile applications mentioned.
as of 2026-08-29
Verification history
We have re-verified Mixedbread AI 17 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 17 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Mixedbread AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
$0/mo
Ideal for
Developers and small teams exploring Mixedbread with limited data and traffic, needing a no-cost starting point.
What this tier adds
Free entry point with $5 one-time credits, 100 requests/min, 10 stores, and community Slack support.
Scale
$20/mo
Ideal for
Growing teams with moderate to high search volume that need higher rate limits and priority support.
What this tier adds
$20/month with $20 in included credits, unlimited users, 10,000 stores, 1,200 queries/min, automatic backups, and same-day SLA support.
Enterprise
Custom
Ideal for
Large organizations with custom volume, compliance needs, and a requirement for dedicated infrastructure.
What this tier adds
Custom pricing with volume discounts, unlimited stores, custom rate limits, BYOB/BYOC, and dedicated support.
Where the pricing makes sense
The company stage and team size where Mixedbread AI's pricing actually pencils out — and where peers do it cheaper.
Mixedbread's freemium pricing fits startups exploring multimodal search, with a free tier and $20/mo Scale plan. Compared to enterprise search platforms like Algolia (which can run thousands per month), Mixedbread's usage-based model is cheaper for moderate volumes, but for pure full-text search on small datasets, Algolia's free tier may suffice. For agentic search, Toast 1 undercuts Claude Opus 5 and GPT-5.6 Sol by up to 10x.
Setup time & first value
How long it actually takes to get something useful out of Mixedbread AI — broken out by persona, not the marketing-page minute.
For developers, you can create a store and start searching within minutes using the Quickstart. Integrate via SDK (Python/TypeScript) in under an hour. CLI bulk uploads are ready after installation. MCP server and skills.sh recipes take a few minutes to configure. Enterprise features like BYOB require contacting support, so plan for a few days.
Switching to or from Mixedbread AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From in-house vector DB (e.g., Pinecone): Upload your files to a Mixedbread store and use the search API to replace manual embedding + retrieval steps.
- →From Algolia/Meilisearch: If you need multimodal or agentic search, migrate your data by uploading documents and switching to Mixedbread's semantic search API.
- →From a custom RAG pipeline: Use the CLI to sync documents and the MCP server to integrate into existing AI workflows, eliminating the need to manage embeddings.
- ↗To Algolia/Meilisearch: If you only need full-text search, export your indexed chunks and re-import into Algolia's index.
- ↗To a self-hosted solution like Elasticsearch: Download your data from Mixedbread and build your own pipeline if you need full control.
- ↗To vendor-neutral via standard APIs: Use the OpenAPI spec to build a custom migration path.
Integrations
Resources & Guides
- Documentationmixedbread.com
Overview
The Search API that makes all your unstructured data understandable and usable for AI. Build intelligent search experiences with Stores.
- API Referencemixedbread.com
Introduction
The mxbai CLI provides a powerful command-line interface for managing Mixedbread stores and files directly from your terminal.
- Documentationmixedbread.com
Introduction
The Mixedbread MCP Server connects AI assistants to the Mixedbread Search API.
- Resourcemixedbread.com
Blog
The baked bytes of AI research & development.
Tutorials & Learning
Official links
Tools that pair well with Mixedbread AI
Common stack mates teams adopt alongside Mixedbread AI, with the specific reason each pairing earns its keep.
Alternatives to Mixedbread AI
View allFrequently Asked Questions
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