@GoSailGlobal: Practical data on multi-agent AI collaboration: Use Opus 4.8 for planning, Deepseek/Gemma for execution — 10x cost reduction, 2x speed improvement. The secret is not using the most expensive model, but having cheap models do the heavy lifting and expensive models only make decisions. This is the same as company management: the CEO shouldn't write code, and interns shouldn't set strategy. A…
Summary
A practical sharing on multi-agent AI collaboration, proposing a hierarchical strategy using Opus 4.8 for planning and Deepseek/Gemma for execution, achieving a 10x cost reduction and 2x speed improvement, with open-source implementation.
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Cached at: 06/08/26, 05:18 AM
Multi-agent AI collaboration practical data is here: Using Opus 4.8 for planning, Deepseek/Gemma for execution — 10x cost reduction, 2x speed increase.
The secret isn’t using the most expensive model; it’s having cheaper models do the heavy lifting while the expensive model handles decision-making.
This is like running a company: the CEO shouldn’t write code, and interns shouldn’t set strategy. The AI agent team has finally learned to divide labor.
In your current agent workflow, are you using expensive models for everything, or have you started to layer them?
Bindu Reddy (@bindureddy): 🚨 Multi-Agent - Lite Agent Swarms - Optimize Cost On Large Agentic Loops
After a lot of experimentation we have open-source AI agent swarms live!!
- Opus 4.8 and GPT 5.5 do the planning
- Deepseek flash and Gemma do the work
- Perfect for multiple parallel tasks
- 10x cheaper
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