Jensen showcased PinchBench (by @kilocode) on stage at NVIDIA GTC as the new standard for evaluating @openclaw agent capability.
Trinity-Large-Thinking just hit #2 on @pinchbench globally (91.9%) behind only Claude Opus 4.6 (93.3%), which costs ~20x more per token. That's an expensive percentage point...
To celebrate the ongoing Trinity partnership, @OpenRouter is running a promotion to make Trinity-Large-Thinking free to use for OpenClaw until Sunday, April 5th.
Even when the promo ends, the Trinity economics are pretty incredible.
Trinity is $0.25/m input tokens (OpenClaw uses A LOT of input tokens), but with a 60%-90% cache hit rate at $0.06/m cache tokens the average input cost nets out to $0.087/m.
You no longer have to compromise on logic to keep your agent infrastructure affordable.
We're excited to see what you build.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
Arcee claims 91.9% on PinchBench against Claude Opus 4.6 at 93.3% while charging a fraction per tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition →, which shifts the cost math for input-heavy AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → loops. Free through OpenRouter for OpenClaw until April 5.
