Abstract
This paper compares frontier models for agentic production workflows, where the question is not only which model reasons well, but which model can sustain tool use, long-horizon execution, and engineering throughput under realistic cost constraints.
The evaluation is designed as a selection guide for practitioners. It maps model behaviour to concrete deployment scenarios, separating broad reasoning capability from operational suitability in agent pipelines.
Authors
TippyBot Research, AAI Labs
What the study evaluates
The benchmark examines depth of reasoning, reliability over extended tasks, accuracy in tool-mediated work, and practical engineering throughput. Price is treated as a hard constraint rather than an afterthought, because model choice in production is constrained by both quality and operating cost.
Main findings
The central result is that the best model for an agent pipeline is not always the model with the strongest general reputation. Different models show distinct strengths depending on whether the workflow requires careful reasoning, repeated tool calls, fast iteration, or economical scaling.
The paper therefore reframes model evaluation as a deployment decision: the strongest choice is the model whose failure modes, cost profile, and latency characteristics match the specific agentic workload.
Why it matters
Agentic systems expose weaknesses that simple chat benchmarks often miss. A model must remain consistent across many steps, call tools at the right time, and recover from intermediate uncertainty. This work helps teams choose models for those conditions rather than relying on generic leaderboard scores.