Imagine you have a giant test with super-hard questions—so hard that even grown-up experts have trouble answering them! Now, think about having a really smart helper that can look up facts, do calculations, and figure out answers for you. That’s what Deep Research is all about.
Over the weekend, I watched OpenAI's team demo their newest product along with a couple of interesting use cases.
The demo was neat but what was even more compelling (or confusing) was the set of metrics and benchmarks they shared to prove Deep Research's superior performance!
These are industry agreed comparative metrics that took me a while to process.
I had to do my own research to understand these metrics and I figured that it would be useful to share. Below I have listed out what each of the 5 benchmarks mean (both simple and hard versions) and how Deep Research measures up on each of them.
1. Humanity’s Last Exam
- Simple Definition: This is a made-up name for a huge, difficult test. Many different “smart helpers” (like GPT-4o, Grok-2, or Claude 3.5 Sonnet) tried to pass it. They all got scores showing how many answers they got right.
- Hard Definition: A fictional, catch-all assessment of extremely challenging questions, often far beyond standard school or college-level. It’s designed to test the upper limits of Large Language Models (LLMs) by seeing how well they can reason, retrieve knowledge, and generate accurate solutions in a domain-agnostic way.
Why is Deep Research special?
- Deep Research got a higher score than the others! Think of it like a robot in a race who can run faster than most others—because it can do more tricks, like using special tools and searching the internet.
2. GAIA (Levels 1, 2, 3)
- Simple Definition: GAIA is just a fancy name for another big test that comes in different difficulty levels (Level 1 is easier, Level 2 is medium, and Level 3 is super tough).
- Hard Definition: GAIA is a tiered evaluation framework with multiple difficulty levels (Level 1 = easier tasks, Level 2 = moderately difficult tasks, Level 3 = highly complex tasks). It measures an AI’s adaptability and depth of understanding across progressively more demanding challenges, providing a granular view of its performance at each level.
Why is Deep Research special?
- Deep Research did better than the old champion at each level. It’s like someone breaking the record in a video game at level 1, then again at level 2, and level 3, too!
3. Pass Rate on Expert-Level Tasks by Estimated Economic Value
- Simple Definition: Some tasks are easier to do and some are very valuable or take a lot of effort. This chart shows how often the “smart helpers” got these tasks right. Tasks with “low” value are kind of simpler, while “very high” ones are like super important, tricky problems.
- Hard Definition: A metric indicating how often an AI can correctly complete tasks that are categorized by their perceived market or business impact (e.g., “low” = routine tasks, “very high” = highly specialized, high-stakes tasks). It serves as a proxy for real-world utility—demonstrating whether an AI can handle assignments that produce substantial return on investment.
Why is Deep Research special?
- Deep Research can handle both easy tasks and harder, more valuable tasks better than older models, because it can gather more information and think things through more carefully.
4. Pass Rate on Expert-Level Tasks by Estimated Hours
- Simple Definition: This shows how many tasks each “smart helper” solved if the task usually takes 1-3 hours, 4-6 hours, 7-9 hours, or even more than 10 hours for a person to do.
- Hard Definition: An evaluation of success rates for tasks that a human expert would typically spend a given range of hours (1–3, 4–6, 7–9, 10+) to complete. This measures how effectively the AI scales to more intricate, time-consuming problems, reflecting its capability to shorten lengthy workflows.
Why is Deep Research special?
- Whether it’s a quick chore or something super time-consuming, Deep Research can use its extra research skills (like browsing or coding) to get the right answers more often.
5. Pass Rate on Expert Tasks & Max Tool Calls
- Simple Definition: Sometimes the “smart helper” needs to do extra searching or run special software—these are called “tool calls.” The chart shows that the more tool calls a helper can make, the better it does on super-tough expert tasks.
- Hard Definition: This metric shows how an AI model’s accuracy on complex tasks improves when it can “call” or use external tools (e.g., code interpreters, APIs, or web search) multiple times. Each “tool call” allows the AI to fetch information, run computations, or verify data—essentially acting like a researcher with unlimited library access.
Why is Deep Research special?
- Deep Research lets the helper make lots of tool calls if needed, so it can dig deeper for the correct answers—imagine a detective checking more clues to solve a mystery.
So why/how is Deep Research a big deal?
Deep Research is like having a super-powered assistant who can do all sorts of extra homework to find the right answer. It doesn’t just think really hard; it also uses tools, searches online, and double-checks its work. Because of that, it gets better scores than most other helpers on the hardest tests.
#ai #research #responsibleai #tools #benchmarks
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