Measured against Anthropic’s Claude Opus 4.8 and OpenAI’s GPT-5.5 — both much larger, frontier-scale systems — Faraday runs on a comparatively tiny model called Qwen 3.6 that has just 27 billion parameters.
参数量对比是本文最抓眼球、也最容易误读的一句。被比较的不是两个模型，而是两套完整系统：Faraday 是 27B 权重加上专门的任务训练、工具与流程，对面是通用前沿模型跑同一批题。真正的结论应该是“为这个任务专门设计的系统赢了通用系统”，不是“小模型赢了大模型”。
The quote is a big reversal of stance from a position ~uniformly held by anyone who worked at **Team Big Model**, including his previous head of OpenAI Labs
大多数人认为大型模型实验室会继续专注于基础模型研发，但作者认为这是一个立场的重大转变，因为连OpenAI前高管都开始转向代理产品。这挑战了AI行业长期以来的'模型优先'共识，表明即使是Big Model团队也开始认可代理产品的价值。
Tools give agents the ability to take actions. Agents go beyond simple model-only tool binding by facilitating: Multiple tool calls in sequence (triggered by a single prompt) Parallel tool calls when appropriate Dynamic tool selection based on previous results Tool retry logic and error handling State persistence across tool calls
When you bind tools directly to a Model, the model makes a single, stateless decision. It suggests the best tool for the immediate prompt and then stops.
The Agent, however, uses its loop (often ReAct: Reason, Act, Observe) to execute complex strategies
An LLM Agent runs tools in a loop to achieve a goal. An agent runs until a stop condition is met - i.e., when the model emits a final output or an iteration limit is reached.
The difference lies in autonomy and execution flow: A Model with Tools (via direct binding/function calling) is a single, stateless step where the LLM merely suggests the best tool and its arguments, requiring the developer to manually execute the tool and initiate any subsequent calls. In contrast, an Agent with Tools leverages an Agent Executor to manage a dynamic, multi-step loop (e.g., ReAct), where the LLM acts as the planner, deciding which tool to call next, and the Executor automatically runs the tool, feeds the observation back to the model, and repeats the cycle until the complex, multi-step goal is autonomously achieved.