Arena 基于 Agent Arena 数据称，Google DeepMind 的模型在成本上扩展性更好，Gemini 4 Carbon 是典型代表：典型任务上 GPT-6 Astra 成本为其 3.4 倍、Claude Opus 5.5 为 3.9 倍，GPT-6.1 Sol 约为其一半（0.54 倍）。
Models by @GoogleDeepMind scale better on cost than their rivals.
Gemini 4 Carbon is the prime example. As tasks get more demanding, every rival gets more expensive relative to Carbon.
For a typical task, GPT-6 Astra by @OpenAI costs 3.4× as much, Claude Opus 5.5 by @AnthropicAI costs 3.9× as much, and GPT-6.1 Sol is the cheaper option at about half of Carbon’s cost (0.54×).
Across the 5% most expensive sessions, Carbon’s position improves against all three. Opus edges up to 4.2×, Astra’s cost roughly doubles relative to Carbon, and Sol’s discount shrinks.
This in turn can translate into meaningful savings across real-world tasks such as building an Android security app, one of many long-horizon tasks that our Agent Arena users delegate to their agents.
As tasks get more complex, @GoogleDeepMind and @XiaomiMiMo are the biggest winners in the Agent Arena. On cheaper tasks, @OpenAI and @AnthropicAI hold 10 of the 11 frontier spots. On the most expensive 5% of sessions, they hold just 7. The freed spots go to Google’s Gemini 4 Argon and Xiaomi’s MiMo-V2.6 Flash and Pro. These shifts matter as sessions costing $100 or more are not uncommon across real-world use cases coming from Agent Arena. Even within the last 2 weeks, more than 1,000 sessions crossed that boundary, and choosing a suboptimal model might have had a meaningful impact on that final bill.在 X 查看被引用的帖子
来源：Arena.ai · x.com