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Emmanuel Ene Rejoice Gideon
Emmanuel Ene Rejoice Gideon

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

#ai #llm #opensource #machinelearning

STEWMIND 1.7B

OpenAI just shipped GPT-6.1 Sol (Sept 29, 2026). It's genuinely brilliant.

And I still won't use it inside my agents.

Don't get me wrong — for deep reasoning, GPT-6.1 is untouchable by any 1.7B model, including mine. But here's what changed in 2026: 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.

So I trained my own agent brain. StewMind 1.7B — built in Nigeria 🇳🇬, fine-tuned for one job: running agents locally.

The honest benchmark

Here's StewMind vs the latest GPT, measured on real agentic workloads. No marketing, measured and public-listed numbers only:

Capability StewMind 1.7B (local) GPT-6.1 Sol (API)
Cost per 1M tokens $0.00 — forever Paid, and 2026 prices rose on 10/14 tiers
Agent routing call ~0.01s, on your CPU Network round-trip + queue
Data privacy Never leaves the device Leaves your machine, every call
Works offline Yes No
Ownership Yours (Apache-2.0, weights included) OpenAI's
Runs on a phone Yes — 1GB GGUF No
Hallucination on tools Possible, but you can constrain it Possible, and you can't fix their model
Deep reasoning Loses, clearly Wins, clearly
Writes better poetry No Yes

Read that table again: for agents, the rows that matter are the first six. Agents don't need a poet. They need reflexes.

What StewMind actually does (measured, live)

I benchmarked her on my own machine — no GPU, plain CPU:

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"
  }
}
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Correct tool, correct arguments, numbers typed as numbers, currency inferred. That's a real tool call a machine can execute.

ReAct planning, memory ops, intent routing, Pidgin ("Wetin be your main work?") — all coherent. She's being upgraded right now on 100,000 agentic workflow samples (checkpoints landing at stewmind-1.7b-ultra as we speak).

The uncomfortable truth about 2026 AI

Everyone is building agents on rented brains. Every spike in GPT pricing, every rate limit, every outage — that's your product going down.

The biggest AI insight this year came from NVIDIA's own researchers: "Small Language Models are the Future of Agentic AI." Specialized, repetitive, latency-critical tasks — exactly what agents do all day — belong to small local models. Giants handle the once-a-week deep question.

StewMind is the reflex layer. GPT-6.1 can be the brain you call when it's worth it.

Get her

  • Model + GGUF (runs in Ollama, llama.cpp, anything): Erog0291/stewmind-1.7b
  • 3 lines of Python and she's running on your machine, zero API bills 
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Erog0291/stewmind-1.7b")
model = AutoModelForCausalLM.from_pretrained("Erog0291/stewmind-1.7b")
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1,000+ people grabbed her this month. Big models cook for everyone. This one cooks for you.

If you're building agents in Africa, or anywhere the cloud bills bite — come cook with us. 🍲

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