December's AI model rankings just dropped some interesting shifts.

There's this new version—let's call it the "agentic speed demon"—that's laser-focused on three things: calling tools efficiently, handling messy multi-step workflows, and doing it all fast. Really fast.

Here's where it lands on the leaderboards:

τ²-Bench Telecom? Topped the charts. This benchmark throws ridiculously complex agent tasks at models, the kind that make most systems choke. Not this one.

Berkeley Function Calling Benchmark? Also sitting at #1. Translation: when you ask it to use external tools or APIs, it actually gets the job done accurately instead of hallucinating nonsense.

What makes this notable isn't just the rankings—plenty of models claim top spots on cherry-picked tests. It's the combination: speed + tool accuracy + workflow complexity. That trifecta matters if you're building anything beyond chatbots.

The model architecture clearly prioritizes practical execution over general knowledge breadth. Trade-offs, always trade-offs. But for agentic applications? This positioning hits different.