Agentic AI models: five built to run a task from start to finish

Five models are built explicitly for autonomous multi-step work: planning a task, calling tools in sequence, noticing when a step failed and recovering without a human in the loop. Muse Spark 1.2 and 1.1 sit here alongside Grok Build 0.1, with output from $1.10 to $4.25 per million tokens.

Agentic runs have their own cost profile. A single request can expand into dozens of model calls, and errors compound - a wrong turn at step three wastes everything after it. Reliability is worth more than headline price, and prompt caching and adjustable thinking are unusually valuable here. Budget by task completed rather than by token, and cap iterations.

• Cap iterations and wall-clock time before anything reaches production.
• Log every step; debugging an agent without a trace is guesswork.
• Use adjustable thinking to stop over-deliberation on simple steps.
• Budget per completed task, since token counts vary wildly per run.