Liquid d1
d1 is Liquid AI’s first decision model, built to answer structured questions about a situation with probabilities rather than generating text. It takes unstructured data such as text, images, or both, along with one or more questions, processes them in a single forward pass, and returns probabilities without generating output tokens. d1 supports three question types: noul for yes-or-no decisions, choice for selecting among labels with a probability for each option, and score for placing an input on an ordered scale using probability-weighted levels. Multiple questions can be evaluated against the same state in one request, making it useful for real-time decision workflows. Liquid AI demonstrates d1 across applications such as filtering support tickets, browsing code repositories, organizing documents into smart folders, operating websites, compacting agent context, inspecting industrial parts, and playing games from screenshots.
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Parallel Decisions API
Parallel Decisions API is a hosted, web-grounded classification API for developers building software, data-enrichment pipelines, and AI-agent workflows. A request supplies context and one or more bounded questions; the API returns typed answers with probabilities and citations to supporting web sources. Multiple questions can share context in a single request, supporting uses such as company or vendor qualification, lead routing, compliance screening, content filtering, and agent guardrails. By retrieving current web evidence, it can assess facts that may have changed since a model's training data. Developers call the REST endpoint with an API key or access the web_decisions tool through Parallel's MCP server. A hosted playground and documentation support testing and implementation.
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Perplexity Decider
Perplexity Decider is a 27B decision model fine-tuned from Qwen3.8-27B for structured classification and multimodal decision tasks. Rather than generating unrestricted text, it takes an input state and a typed decision schema, then returns the selected result together with calibrated probabilities. The model supports choice decisions for selecting among predefined categories and noul decisions for estimating the probability of a yes-or-no condition. It can also accept images alongside text, allowing applications to make decisions from screenshots or other visual inputs. Example workflows include routing a support request to billing, technical support, or sales, and estimating whether a message expresses urgency. Perplexity reports an overall accuracy of 85.71% across 11 benchmarks, including FinancialPhraseBank, RAGTruth, JudgeBench, TabFact, ContractNLI, Circa, Belebele, and TruthfulQA binary.
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Clef
Clef is a 27B multimodal decision model that turns a state and a schema of typed questions into structured decisions. It can read state information as text, JSON, images, or video and return a probability for every allowed option of every question. The model supports a 65,536-token context window and vision inputs, allowing developers to evaluate both structured application data and visual content in the same decision workflow. Clef supports three typed question formats: noul for estimating the probability that a proposition is true, choice for selecting among predefined options and returning per-option probabilities, and score for evaluating an input against ordered criteria and producing a probability-weighted score. Applications can submit between 1 and 64 questions in a single request, with answers returned under the same question IDs.
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