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Daniel Orson builds practical AI tools and writes about model selection, benchmark context, cost, and agent workflows at Dervity.
Dervity tracks AI models, pricing, benchmark results, and practical workflow tradeoffs. The goal is simple: help builders choose a model that fits the task and the budget.
The research combines provider documentation, Artificial Analysis data, Arena results, practitioner reports, community discussion, and selected internal tests. Sourced numbers and first-hand observations are kept distinct so readers can see where each recommendation comes from.