What is Toast 1 used for?

Toast 1 is used for agentic retrieval in AI search, AI research, and AI coding workflows. It decomposes a query, performs multiple retrieval operations, inspects evidence, and returns ranked chunks that can be used by an application or another agent.

How does Toast 1 differ from a standard search API?

A standard search API often runs a single retrieval pass and returns matching results. Toast 1 is designed to operate more like a search agent by iterating through retrieval steps, using parallel search methods, checking sources, and curating evidence before producing the final ranked chunk list.

Can Toast 1 be used inside another AI agent?

Yes, the provided information states that Toast 1 can run as a retrieval subagent inside larger frontier-agent workflows. This makes it relevant for coding assistants, research agents, and RAG applications that need a specialized search component.

Does Toast 1 support existing API workflows?

Toast 1 is described as offering a Chat Completions-compatible API with an agentic flag. It also returns the same ranked-chunk response shape, which may help teams integrate it into existing retrieval or LLM application stacks.

Toast 1 is listed as a paid tool. The provided information does not include exact plan names, pricing numbers, free trial availability, or billing details, so the official Mixedbread website should be checked before purchase.

Last updated: 10/6/2026Last verified: 2026-08-14

Toast 1 Review

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Toast 1 is Mixedbread's agentic search model: it decomposes a query into steps, runs parallel retrievals with "next file search" iteration, inspects sources, and curates evidence before returning. It runs standalone or as a retrieval subagent, delivering frontier search quality at roughly 1/10th the cost and 12x the speed of comparable frontier models.

Paid

What is Toast 1?

Toast 1 is Mixedbread's agentic search model designed to improve retrieval quality for AI search, research, and coding workflows. It breaks queries into steps, runs parallel retrieval operations, inspects sources, and curates evidence before returning results. The model can run as a standalone search agent or as a retrieval subagent inside larger frontier-agent workflows.

Key Features

Decomposes complex queries into smaller retrieval steps before returning results
Runs up to 8 parallel retrieval calls per round, including semantic search, grep, metadata filtering, and context pruning
Uses a bounded agentic loop with up to 4 rounds to gather evidence and inspect sources
Returns a final ranked chunk list designed for retrieval-augmented generation and agent workflows
Supports standalone operation as a specialized search agent
Can be used as a retrieval subagent inside tools and coding agents such as Codex-style or OpenCode-style workflows
Offers a Chat Completions-compatible API with an agentic flag for easier integration into existing stacks

Best For

Teams building AI search systems that need multi-step retrievalDevelopers adding agentic retrieval to RAG pipelinesAI coding workflows that need source-aware file search and ranked code chunksResearch agents that need to inspect sources before generating an answerProduct teams looking for a drop-in retrieval component with a familiar API shape

Pricing

Toast 1 is listed as a paid tool. The provided information does not include specific pricing tiers, usage-based rates, free trial details, or enterprise contract terms, so buyers should check Mixedbread's official website for current pricing.

Pros & Cons

Pros

Agentic retrieval process can break down complex queries instead of relying on a single search pass
Parallel retrieval calls may help cover semantic, keyword, metadata, and contextual signals in one workflow
Bounded loop design gives the retrieval agent a defined maximum number of rounds
Ranked chunk output is well suited for RAG systems and AI coding assistants
Chat Completions-compatible API may reduce integration work for teams with existing LLM infrastructure

Cons

Specific pricing details are not included in the provided information
Performance claims such as lower cost and faster speed versus comparable frontier models need independent verification
The review data does not provide details about security, compliance, data retention, or enterprise controls
Teams may still need to benchmark Toast 1 against their own corpus, retrieval stack, and latency requirements

Alternatives

Perplexity

Perplexity is an AI search and research tool focused on web-based answers with cited sources, making it relevant for users evaluating AI-assisted research workflows.

Exa

Exa provides AI-native search APIs for retrieving web content and can be used in research agents, RAG systems, and custom search applications.

Tavily

Tavily offers search APIs designed for AI agents, including retrieval features aimed at grounding LLM responses with external information.

Sourcegraph Cody

Sourcegraph Cody is an AI coding assistant that uses code search and repository context, making it relevant for teams focused on code retrieval and developer workflows.

Glean

Glean provides enterprise AI search across workplace knowledge sources and is an alternative for organizations prioritizing internal knowledge retrieval.

FAQ

View all Toast 1 alternatives →

Details

Platform

API

Features

Decomposes queries into steps and iterates with up to 8 parallel retrieval calls per round (semantic search, grep, metadata filter, context pruning)
Bounded agentic loop (4 rounds max) that gathers evidence, checks sources, and returns a final ranked chunk list
Runs standalone as a specialized search agent or as a retrieval subagent inside frontier agents (e.g. Codex, OpenCode)
Drop-in for existing stacks: Chat Completions-compatible API with an agentic flag and the same ranked-chunk response shape

Languages

Known limitations

API-only hosted model — no open weights on HuggingFace and model size is undisclosed as of 2026-08-14
Launched 2026-08-13 — benchmarks are vendor-reported and independent third-party evaluation is still pending
Slower than a single search call (extra LLM + retrieval rounds), and the loop is bounded to 4 rounds, limiting very deep retrieval
Strongest with Mixedbread Search as the backend; other retrieval backends are competitive but not at full strength