Gartner says open-weight models can do 90% of agentic work at a fraction of frontier LLM cost

Gartner analysts Mark Petty and Raymond Paquet say for-profit model makers will chase revenue from only about ten percent of steps in agentic AI workflows, because nine in ten steps are routine and open-weight models can handle them at parity for a quarter of the price of the cheapest closed model. In best cases, open-weight models can do 90 percent of agentic work at as little as a twentieth of the cost of running a frontier LLM on every step. The analysts also note that current open models can be more verbose and retry more often, so real savings against a frontier model are closer to five or six times.

Cost and control

The analysts wrote last week in a document titled Open-Weight Models Will Run 90% of Enterprise Agentic Workflow Steps that typical operational workflows involve hundreds of routine executions, such as classification, tool calling, passing, validating and reconciling data. They say open-weight models can handle hundreds of these routine steps for every one that needs frontier reasoning. Against the cheapest closed model at the same score, an open model costs a quarter as much, while against a frontier model run on every step it costs as little as a twentieth.

Petty and Paquet argue that cost is a bonus rather than the main reason to move. Years of workflow history are a large part of what an acquirer pays for in a SaaS company, and every routine step sent to a frontier API moves a slice of it outside the buyer's perimeter. Open-weight models preserve control, they say.

Adoption and roles

The analysts offer a strategic planning assumption that by 2030, 90 percent of enterprise operational agent workflow steps will execute on open-weight models. They expect enterprises to mostly adopt domain-specific open-weight models, which do well in scenarios where a small number of highly stable steps run millions of times a month.

Most models are already commodities, while harnesses, the tools that run agents and manage agents, are the source of value in AI products. A harness routes work to the most appropriate model for a job without users noticing any impact, and a good one saves money, imposes strong guardrails and manages agent fleets. Less powerful harnesses leave users with more to do.

Frontier and open-weight roles

The paper suggests frontier models will continue to have a role as the engine of research and development and are effectively capital expense. Open-weight models become an operational expense after frontier models do the heavy lifting.

Sources: The Register