When I, a human, need an answer to anything moderately complex, it's unlikely that I get it on the first (pre-AI) round of google searching. Simple stuff, sure, but more likely I'll need to go 2-5 rounds. Maybe click a few links. Double-check my assumptions.
An LLM that can do that quickly seems like a slam dunk. I wonder what other problems benefit from that 10x-100x increase in context + 2-5 rounds with the LLM.
For example, a couple of days ago I described a problem with my refrigerator's water dispenser to Google Gemini, and it told me exactly how to fix it. I then went looking for a video and fixed the thing in under 15 minutes. The only way that Gemini could have been better is if it linked to a video itself.
Do you mean search into less-well-known topics? Or something else?
I think in a lot of ways Google peaked and is now on the decline into a profitable but much less relevant services company.
Toast 1, our first specialised search agent, is available today. It provides frontier search quality, matching or outperforming Claude Opus 5 and GPT-5.6 Sol while being up to 10× cheaper and 12× faster. It performs best with Mixedbread Search, but it can work with any search backend.
Today, frontier models are now able to perform real knowledge work. They can reason, analyse, and find information in complex document collections. But they are also the most expensive models in the stack. As intelligence is increasingly metered, the need for specialised agents able to match their capabilities at a fraction of the cost is greater than ever.
Toast 1 can run as a standalone specialized retrieval agent, or as one of many subagents your frontier model already knows how to rely on. It fully takes over the search loop: given an initial query, it decomposes it into subqueries, gathers evidence, inspects sources, and curates the relevant context before returning it. This lets your agent spend its context and compute on the task that requires a generalist, frontier-level model: reasoning, acting, and producing the final answers.
Waterfall trace of a Toast 1 agentic search: 16 tool calls across 3 rounds answering an employment-rate comparison query in just over 5 seconds. Expand the trace, then select a step to see the sub-query, grep pattern, or plan the agent produced at that point.
This specialisation of agentic labor results in considerably cheaper search, but also in better end-to-end results on many realistic tasks. We found that Toast 1 establishes a new Pareto frontier across agentic workloads across cost per task and speed per task.
OfficeQA Pro V2, released by Databricks, evaluates answer correctness across 90 questions in realistic, complex enterprise financial situations.
GPT‑5.6 Sol with Toast 1 made available as a sub-agent within Codex reaches 70% answer correctness at approximately $1.15 per task: that is the highest score among the systems evaluated by Databricks in the OfficeQA v2 release, establishing new state-of-the-art performance in both quality and efficiency.
Scatter plot of answer correctness versus cost per rollout on OfficeQA Pro V2, log-scale cost. GPT-5.6 Sol running in Codex with Toast 1 as a sub-agent reaches 70 percent correctness at about $1.20 per task, above the previous Pareto frontier from the Databricks evaluation, where Claude Fable 5 on Databricks Genie reaches 60 percent at about $4.
Answer correctness vs. cost per rollout on OfficeQA Pro V2. Genie and harness numbers as reported by Databricks; Codex + Toast 1 runs are ours. Shaded region sits under the previous Pareto frontier.
By comparison, the previous best performer, Claude Fable 5 on Databricks Genie, reaches 60% correctness at approximately $4 per task, while GPT-5.6 Sol within Codex without Toast 1 only reaches 33% correctness.
This improvement stems from reformulating the economics of evidence gathering. Toast 1's specialization allows it to produce high-quality, token-efficient evidence packages, leaving ample resources for the reasoning process to reach the final answer.
Harvey LAB's Law Firm Knowledge benchmark seeks to evaluate how well an agent can search and use institutional legal knowledge at large, realistic scales.
Legal work, by nature, is context-heavy. You cannot outargue someone with access to better, more relevant precedents and details. But it is also noisy: many situations are similar but vary by simple details, making it tricky to collect high quality evidence packages without numerous false positives.
On a randomly selected subset of 33 tasks,1 we found that GPT-5.6 Sol's answer quality remained constant across search methods.
Bar chart of total tokens used on the Harvey LAB firm-knowledge benchmark. A vanilla agent uses 80.6 million tokens at 21.7 turns per task. Adding Mixedbread Search cuts that by 42 percent to 47 million tokens at 14.6 turns per task. Adding Toast 1 as a subagent cuts it by another 51 percent to 23 million tokens at 11.2 turns per task. All three configurations reach the identical task score of 55, so the end result is the same performance with 3.5 times fewer tokens.
Tokens are totals across the 33-task benchmark; turns are agent loop iterations per task. All three configurations reach the identical task score of 55.
However, increasing search quality drastically increased token efficiency: replacing the vanilla agent's filesystem search with Mixedbread Search cut token usage from 80.6M to 47M at an identical task score. Subsequently adding Toast 1 as its dedicated search subagent reduced it further to 23M, and allowed it to finish in half the turns required by vanilla agent.
The introduction of a Mixedbread Search-powered Toast 1 preserved answer quality, while consuming 3.5× fewer tokens, leading to a cost reduction of over 60%. Toast 1 frees up the context window of frontier models to let them spend their tokens on reaching the right answer.
Benchmarks and numbers can only tell one part of the story. To truly understand how Toast 1 works, there is no better way than watching it search in action. At Mixedbread, we really enjoy Dwarkesh's podcast, and thought being able to search deep into its transcripts would be fun.
You can try it yourself here.
Although it is a capable subagent for complex tasks, Toast 1 is also a capable standalone model, trained specifically for deep search. It represents the next step of our co-design approach behind our embedding models and Silo: the model, agent harness, and retrieval primitives are designed to work together.2
Retrieval quality versus cost and latency per query on BrowseComp Plus, OfficeQA Pro, and LongSeal. Toast 1 matches or approaches the best frontier-model sweeps on each benchmark while costing a fraction per query and answering in about 8 to 10 seconds, far faster than the frontier sweeps.
Cost per query at list prices with prompt caching; latency is p50 per query. Lines show each model's Pareto-efficient reasoning sweep.
On a variety of deep search benchmarks, it reaches frontier model performance, standing in the same league as GPT-5.6 Sol and comfortably outperforming models such as Kimi K3 or GLM-5.2.
It remains lightweight in doing so. A standard Toast 1 run costs approximately 0.016−0.016 - 0.023 per query and has an eight-second median latency. Our highest-quality fusion configuration costs approximately 0.05−0.05 - 0.07 per query and has an eleven-second median latency. In practice, among the systems in our evaluation that reached similar performance, Toast 1 was 7–11× cheaper and considerably faster: Frontier-model retrieval agents took between 20 seconds and four minutes on the same evaluation.
Toast 1 is available immediately through the Mixedbread API at the discounted launch pricing:
Mixedbread search invoked by Toast 1 is priced at a special rate.
Toast 1 was co-designed with Mixedbread Search's primitives and will be at its strongest performance with it. But we put special care in ensuring that it remains backend agnostic: it can run over your existing retrieval indexes, and does not require migrating your existing backend. We conducted thorough testing to ensure that Toast 1 remains competitive with the performance of frontier models in similar conditions at a fraction of the cost and latency, no matter the provided index.
You can use Toast 1 with our Chat Completions API and add it as a retrieval tool to your existing agentic workflows in just a few minutes. Here is a golden harness you can use directly.
Let your coding agents handle the integration with npx skills add mixedbread-ai/skills. Or use Toast 1 directly as a subagent with our OpenCode integration.
from mixedbread import Mixedbread
client = Mixedbread()
results = client.stores.search(
store_identifiers=["legal-documents"],
query="does the MSA allow assignment on a change of control?",
search_options={
"agentic": True, # enable Toast 1
},
)
Get an API key with $5 in included credits to try it out.
We evaluated a randomly selected subset of 33 tasks to make repeated comparative runs tractable. Every configuration used the same tasks and evaluation setup; only the retrieval stack changed. ↩
Toast 1 is part of a growing body of work on specialised search agents, alongside SID-1 and Chroma's Context-1. While each takes a different approach, they share the goal of bringing frontier-level retrieval to production at lower cost and latency. ↩