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.
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AI spend is measured in tokens, model calls, and dollars. However, none of these fields describe the actual work being done.
At Ramp, we built a semantic layer that attributes agent spend to objectives and outcomes. This allowed us to go from monitoring AI spend to understanding AI ROI.
Here's how we built it 🧵
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We tested NVIDIA NeMo Switchyard’s stage router for coding agents in Ramp SWE-Bench.
Routed agents showed comparable performance to single-model controls while substantially reducing costs and runtime.
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Price cuts to @OpenAI's GPT-5.6 Luna and Terra elevate an already strong outcome per dollar into a dominant one.
See how they handle production engineering →
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We’re open-sourcing PorTAL, our framework for shared task representations and cross model LoRA adaptation.
It now spans from hybrid attention models to multimodal systems including Gemma 4 E2B, Mistral 7B & @thinkymachines' Inkling.
Code: ramp-public/portallib
Models: @huggingface /RampPublic
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We had access to Opus 5, and found a distinctly Fable-shaped addition to the Pareto frontier.
We’re launching Ramp Router, our LLM gateway.
It processes trillions of tokens a day across Ramp’s external products and internal AI workflows, giving us one place to manage reliability, latency, and cost.
Reserve access today.
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Introducing PorTAL: Portable Task Adapters for LLMs. A novel recipe to cheaply port fine-tuning between models. It matches per task LoRA accuracy at half the cost, lowering the switching overhead of adapting tasks across LLMs.
At Ramp, every new model release used to mean retraining our fine-tunes from scratch. PorTAL learns the task once, then efficiently refits it onto any new base model, even across model families.
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Today we’re releasing Ramp SWE-Bench: a private, production-grounded coding benchmark created from real engineering problems we've faced at Ramp.
