Introducing Exa Agent Ultra - A New Frontier for Deep Research
Today we shipped Exa Agent Ultra, setting a new state of the art for comprehensive deep research. As the highest effort level for Exa Agent, it coordinates many agents across thousands of sources, delivering higher-quality results than frontier models.
Agent Ultra outperforms Opus 5.5, GPT-6 Astra, and Perplexity Agent on their respective maximum effort settings, across a variety of benchmarks:
Frontier deep research
Exa Agent Ultra outperforms all frontier models on their native harnesses and sets the state of the art on WANDR, scoring highest on quality and lowest on cost-per-task out of all competitors benchmarked.
- WANDR. +12.6% over Opus 5.5, +103% over Perplexity and +213% over GPT-6 Astra, at half the cost per task of Opus 5.5.
- DeepSearchQA. +4.7% over Perplexity, +10.1% over GPT-6 Astra and +21.0% over Opus 5.5, at 46% lower cost per task than GPT-6 Astra.
- WideSearch. +5.2% over Perplexity, +7.7% over GPT-6 Astra and +14.1% over Opus 5.5, at the lowest cost per task of the four.
- Find-All Company. +1579% over Opus 5.5, +2069% over GPT-6 Astra and +2401% over Perplexity, at the lowest cost per entity found.
We evaluated Agent Ultra with WANDR (Wide And Deep Research), a benchmark that tests research agents for their ability to find a large list of qualifying entities (wide) with evidence-supported accuracy across many fields (deep). Tasks are modeled on entity enrichment use cases in domains such as due diligence and legal research. Our grader shares the same vendored evaluation logic as the upstream repository; the core differences lie in the contents tool (Exa), the transport logic, and a better and cheaper judge model (gpt-6-luna). Where Perplexity, Anthropic or OpenAI had published a result obtained on this grader harness, we report the published figure; where none existed, we ran the benchmark ourselves. Faded points are each system's low setting (low effort for Opus 5.5 and GPT-6 Astra, Perplexity Agent on low-effort Astra, and Agent Auto for Exa).
Lists without limits
Exa Agent divides a task into subtasks, assigning subagents to research multiple domains at once. It puts frontier intelligence where a task needs it and faster models where they are enough. As the highest effort mode for Exa Agent, Ultra is built for the hardest research, where completeness and accuracy matter most: deep research, list-building, and entity enrichment - any task that needs to run to exhaustion.
Examples:
Model providers
- Assemble training data from the web, such as "every paper and code repo implementing a given technique" or "every public benchmark with a leaderboard and its top submission".
- Verify hard-to-check criteria about an entity like "has a reproducible eval harness", or "released weights, not just an API".
Financial services
- Create market maps for diligence like "every company that does battery recycling in Europe, adding founders, funding, and customers for each".
- Research a customer for KYC compliance across many sources such as filings, press, court records, and regulator sites, synthesized into one record with citations.
- Monitor for signals, like "every portfolio company that announced a layoff, CFO change, or new facility this quarter".
Go-to-market teams
- Build account lists like "every US company that makes browser-automation tooling" or "every Series A-C fintech with a Head of Risk hired in the last 12 months".
- Enrich a list with new fields requiring judgment, such as "does this company sell to hospitals?", "who owns the data platform?", with a cited URL as evidence.
Ultra makes it easy to expand lists by passing the rows you already have, excluding them from future results.
Try Ultra today
Agent Ultra is available today. Read the docs here or try it out in our API Playground.
