It checks its work much earlier. Argon makes its first check 44% of the way through an attempt. For Opus 5.5, it's 77%.

We had early access to @NVIDIAAI Nemotron 3.5 Super VL and post-trained it for one of our highest-volume workflows: receipt extraction. One epoch of supervised fine-tuning on 10,000 examples made it competitive with leading open and closed-source models on our receipt benchmark.

Two new challengers to the Pareto frontier on accounting work. First Sonnet 5.5, then GPT-6.1 Sol: each one scores more than any model at its price or lower. GPT-6.1 Sol then pushes both GPT-6 Sol and Sonnet 5.5 off the frontier.

For KVzip, we use dense scoring and compare shared selection rules that balance importance across heads. For Attention Matching, we compare HAK and OMP selection, then fit per-head biases and shared latent values against GLM’s sparse attention. We fit one layer at a time, accounting for changes introduced by earlier compression.

We had early access to Opus 5.5 and ran it on Ramp Accounting Bench. It performs close to Fable 5.1, at 61% lower cost and 1.7x faster.

Introducing Ramp Accounting Bench. We partnered with accounting professionals to create 137 tasks and grading rubrics grounded in everyday accounting workflows. Even with three attempts, the best model achieved only 21% accuracy. Reliable agentic accounting remains an open challenge.