Liquid AI D1-Ultra

Liquid AI released d1-ultra, a multimodal decision model it says outperforms all other decision models on the Artificial Analysis Decision Index by at least 8 points and beats Jev 1.14 across all tasks, based on 112 unique evaluations. The model answers noul (yes/no with a 0-1 probability), Choice (one label from many with per-label probabilities), and Score (a position on a scale) questions, reads text, images, or both, and is available on the Liquid API at $0.042 per million input tokens, billed on input tokens only with images counted at 1.5 tokens per 32×32-pixel patch. Liquid AI said d1-ultra will soon be available through Vercel and OpenRouter.

News https://www.liquid.ai/news Models https://www.liquid.ai/news/models d1-ultra: Introducing the World’s Best Decision Model Today, we release d1-ultra, our new multimodal decision model with state-of-the-art performance . Most decision models work well on easy and common tasks. We built d1-ultra for harder, messier real-world use cases, like spotting a defect on a circuit board against its spec or checking a damage claim against the customer’s photo. d1-ultra is available today on the Liquid API https://console.liquid.ai/ at $0.042 per million input tokens. Try on Liquid Console https://console.liquid.ai/ Try on d1 Playground https://d1.liquid.ai/ What d1-ultra does d1-ultra answers the same three question types as d1: - noul : a yes/no question, answered with a probability from 0 to 1. - Choice : one label from many, with a probability per label. - Score : a position on a scale. Results Compared to d1, we based d1-ultra on a larger open-weight model with a series of Liquid-specific heavy customizations. It provides significant gains when combined with our training pipeline, especially on knowledge-related tasks. We compared it to our previous model d1 and Jev 1.14 across 112 unique evaluations. Accuracy is not enough: decision models also output probabilities that must be properly calibrated. We realized that most decision models are overconfident in their responses. That’s why we specifically trained d1-ultra to produce more reliable probabilities. d1-ultra outperforms Jev 1.14 across all tasks, delivering better-calibrated answers and higher accuracy at the same price. To see how it works in practice, you can find more demos running on d1-ultra in the d1 Playground http://d1.liquid.ai/ . d1-ultra also outperforms all other decision models Artificial Analysis Decision Index https://artificialanalysis.ai/evaluations/artificial-analysis-decision-index by at least 8 points." Availability and Pricing d1-ultra is available on the Liquid AI API http://console.liquid.ai/ . Create your API key and start building today. d1-ultra will soon be available through Vercel https://vercel.com/ai-gateway/models/d1 and OpenRouter https://openrouter.ai/liquid/d1 . The model reads text, images, or both at the same time, like in this example: python import base64, os, requests image = base64.b64encode open "board.jpg", "rb" .read .decode response = requests.post "https://api.liquid.ai/decisions/v1/systemone", headers={"Authorization": f"Bearer {os.environ 'LIQUID API KEY' }"}, json={ "model": "d1-ultra", "images": f"data:image/jpeg;base64,{image}" , "state": "Camera image of a circuit board on the production line.", "questions": {"defect": { "type": "noul", "instructions": "Does this circuit board have a defect?" } }, }, print response.json "answers" "defect" "noul" The full API reference is in the decision models documentation https://docs.liquid.ai/lfm/models/decision-models . d1-ultra is billed on input tokens only, with no output tokens. Images are counted as input tokens at the same rate as text: 1.5 tokens per 32×32-pixel patch, so a 1024×1024 image costs 1,536 tokens. Each question is billed as its own prompt, including its text and all images. d1-ultra is our most capable decision model so far. We’re committed to continuing our work on decision models across a range of sizes, with new features, higher decision quality, and lower latency, and we plan to provide regular updates. Try on Liquid Console https://console.liquid.ai/ Read our docs https://docs.liquid.ai/lfm/models/decision-models Citation For citations, please use the following reference or BibTeX: