According to AnthropicAI on Twitter, simulated negotiations between Claude Opus and Claude Haiku agents showed Opus consistently securing substantially better deals, while human survey participants failed to perceive the gap, as reported by Anthropic’s post and study snippet. According to Anthropic, the result underscores how higher‑capability LLMs can translate model quality into tangible economic outcomes in automated bargaining and procurement workflows. As reported by Anthropic, this perception gap creates operational risks for enterprises that evaluate agent performance by intuition rather than outcome metrics, suggesting demand for rigorous A/B testing, revealable logs, and controllable negotiation policies in agentic systems. According to Anthropic, organizations deploying multi‑agent systems for sourcing, ad bidding, or dynamic pricing can realize measurable ROI by upgrading from lighter models to stronger models like Opus where negotiation or strategic reasoning is core.