What Topics and Models Are Covered in Vals AI's Latest Reports?

Vals AI's latest reports cover two main things: benchmark releases and individual model evaluations. Topics span finance, software, cybersecurity, frontier risk (including recursive self-improvement and mental health), web search, and AI detectors. Models evaluated in the recent cycle include Gemini 4 Argon, Claude Sonnet 5.5, GPT-6 Astra, Claude Opus 5.5, and a long list of others ranked on the site's leaderboard.

Topics covered in recent reports

The site describes its benchmark scope as "economically valuable tasks such as finance, software, and frontier risk like cybersecurity, recursive self improvement and mental health." Recent report entries add two more specific areas:

• AI-driven scientific discovery — a report on Claude Opus 5.5 agents searching for room-temperature magnetic semiconductor candidates.
• Web search evaluation — the "Vals Web Search Index," framed around evaluating search for real work rather than synthetic tasks.
• AI detectors — a piece asking whether the "bitter lesson" has arrived for AI detector tools.

The web search report also states a rationale worth noting: search providers have built their own benchmarks to work around data contamination, unrealistic tasks, and answers leaking onto the web, and Vals AI argues customers need a third-party benchmark to tell which search tool is best for their work.

Models evaluated

The leaderboard snapshot lists the best model from each lab, with performance scores:

Individual evaluation reports exist for Gemini 4 Argon and Claude Sonnet 5.5, both described as "evaluated across our benchmark suite."

What each report includes

The magnetic semiconductor report is the most detailed example of what a report contains. It describes the task given to Claude Opus 5.5 agents (finding a room-temperature magnet with zero net magnetism whose electrons are still sorted by spin — a Luttinger-compensated magnet), the candidate the agents designed (YBaMnFeO5), the follow-up finding that it may be hard to make, and the alternative they identified (KV[Cr(CN)6], a magnet first made in 1999 whose sample stayed magnetic up to 376 K). The post links to a ledger containing the inputs, raw outputs, and analysis code behind its numbers.

That pattern — task description, model output, and a link to underlying data — is the clearest signal of what to expect from other reports on the site.

How to use this to judge coverage

If you want to know whether Vals AI has evaluated a specific model, check the leaderboard first; it shows one representative model per lab rather than every variant. If you want to know whether a specific capability is tested, look at the report titles — the site names the domain (web search, AI detectors, scientific discovery) rather than burying it. And if you need to verify a claim, the magnetic semiconductor report suggests at least some reports publish their inputs and analysis code, so look for a linked ledger before assuming a number is unauditable.