OpenAI's GPT-6.1 Sol Is Brilliant. I Still Won't Put It Inside My Agents.

说明与用途

StewMind 1.7B 允许在本地运行代理，消除 API 成本，将延迟降至约 0.01 秒，并保持数据在设备上。

问题

High API costs, tighter rate limits, and token usage on every agent step make using GPT‑6.1 Sol expensive and unreliable for agent workloads.

结果

Agents run locally with zero API cost, ~0.01s routing latency, and no data leaving the device, while still leveraging GPT‑6.1 Sol for deep reasoning when required.

工作流

• 1. Train StewMind 1.7B on 100,000 agentic workflow samples.
• 2. Deploy the model locally using a GGUF format in Ollama or llama.cpp.
• 3. Invoke the model for tool routing, memory ops, and intent routing within the agent loop.
• 4. Optionally call GPT‑6.1 Sol for deep reasoning when needed.

证据

API prices went UP on 10 of 14 model tiers, rate limits got tighter, and every agentic loop I ship burns tokens on every step — routing, tool calls, memory ops, retries.

来源

StewMind 1.7B (local) – Cost per 1M tokens $0.00 — forever; Agent routing call ~0.01s, on your CPU; Data privacy Never leaves the device; Works offline Yes; Ownership Yours (Apache-2.0, weights included).

来源

Tool routing — input: "Send 5000 naira to my brother today"; reply with JSON only. She answered: {"tool":"payment","args":{"amount":5000,"recipient":"brother","currency":"naira","date":"today"}}

来源