模型密集上新之后：按任务选工具，把成本、控制与信任一起算进去

Choosing an AI stack now means matching models and execution environments to specific tasks rather than relying on one overall winner.

Unchanged token prices do not guarantee unchanged task costs when a model generates more output and makes more tool calls.

A faster model can support more iterations within the same time budget, but extra attempts are useful only when someone can evaluate and improve them.

Benchmark rankings can shift when the tests or their weights change, so a score must be read together with its version and task distribution.

A model can be highly cost-effective without matching the strongest available system across every demanding task.

Whether a provider directly accessed a user's work is a different question from whether product data contributed to model training.

An isolated computer and a separate action checker can reduce some risks, but they do not by themselves prove that every authorized workflow is safe.

A large social platform can introduce agents through familiar activities, while the same personal context can make users more reluctant to grant access.

For iterative visual work, preserving identity and composition during an edit can matter as much as generating an impressive first image.

An independent agent platform can preserve the option to combine providers, although that flexibility still depends on access terms and reliable integrations.