Mixedbread, a veteran maker of embedding and reranking models, has launched Toast 1, its first specialised search agent model. Toast 1 takes over the entire retrieval loop of agentic workflows — it decomposes queries into subqueries, calls search tools, inspects sources, and hands back only curated context. The company says its search quality matches or outperforms Claude Opus 5 and GPT-5.6 Sol while being up to 10× cheaper and 12× faster (official announcement).

The logic: let expensive models do expensive work

Toast 1 is positioned as a dedicated retrieval subagent. It can run deep search standalone or plug into coding agents like Codex — the main model handles reasoning and decisions while evidence gathering is fully outsourced. In the official demo, an employment-rate comparison query was split into 16 tool calls across 3 rounds, resolved in 5.33 seconds. The context window is 131K tokens, accessible via a standard Chat Completions API, with an OpenCode integration and an open harness.

Two headline results (all vendor-reported)

OfficeQA Pro V2 (released by Databricks, 90 questions): GPT-5.6 Sol running in Codex with Toast 1 as a sub-agent reached 70% answer correctness at roughly 1.15 USD per task — per Mixedbread, the highest score among the systems Databricks evaluated. For comparison, Claude Fable 5 on Databricks Genie reached 60% at about 4 USD per task, while GPT-5.6 Sol in Codex without Toast 1 managed only 33%.

Harvey LAB's law-firm knowledge benchmark (a randomly selected subset of 33 tasks): all three retrieval configurations scored an identical 55, but token consumption dropped from 80.6M (vanilla agent) to 47.0M after swapping in Mixedbread Search (−42%), and further to 23.0M with Toast 1 as the search subagent (−51%), with turns per task falling from 21.7 to 11.2 — same score, 3.5× fewer tokens, and a cost reduction the company puts at over 60%.

A standard run costs roughly 0.016–0.023 USD per query with an eight-second median latency; the highest-quality fusion configuration costs about 0.05–0.07 USD at eleven seconds. Among systems reaching similar performance, Mixedbread claims Toast 1 is 7–11× cheaper, while the frontier-model retrieval agents it compared against took 20 seconds to four minutes on the same evaluation.

Pricing and caveats

Launch pricing: 0.30 USD per million input tokens, 0.036 USD per million cached input tokens (cache writes free), and 0.72 USD per million output tokens, with 5 USD in credits for new users. Worth noting on the boundaries: this is a proprietary API model, not open weights; BenchLM explicitly lists it as proprietary and leaves it unranked because the self-reported results lack an open protocol benchmark table — the 70% figure belongs to the GPT-5.6 Sol + Toast 1 combination system, not Toast 1 standalone. Mixedbread's own footnote acknowledges sibling efforts like SID-1 and Chroma's Context-1: this category is taking shape fast.

My take

The most memorable thing about Toast 1 is not any single benchmark but the way it puts agent economics on the table. When per-token prices of reasoning models stop falling while agentic tasks routinely burn millions of retrieval tokens, outsourcing the retrieval loop to a cheap specialised model is a more realistic cost path than waiting for frontier prices to drop. That said, every key number here is vendor-reported — worth watching until independent replication arrives. It also confirms the industry's division of labour: general reasoning belongs to frontier models, and the grunt work goes to specialised subagents. For teams building RAG and research agents, the 5-USD credit is worth a try.