Clef-flash
Clef-flash is a fast 9B 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 returns a probability for every allowed option of every question. The model supports a 24,576-token context window and vision input, allowing developers to evaluate structured application data and visual content within the same workflow. Clef Flash supports three typed question formats: noul for estimating the probability that a proposition is true, choice for selecting among predefined options with per-option probabilities, and score for evaluating an input against ordered criteria and returning a probability-weighted score. Applications can submit from 1 to 64 questions in a single request, with answers returned under the same question IDs. Example uses include deciding whether a support request is urgent, routing it to the correct team, and scoring the severity of customer impact.
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GPT-6 Luna
5.0 out of 5 stars
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GPT-6 Luna is OpenAI’s cost-efficient GPT-6 model designed for everyday professional work, coding, computer use, and agentic applications at scale. It brings many of the advances introduced with GPT-6 Astra to a faster and substantially lower-cost model while improving on GPT-5.6 Luna in capability and factual reliability. The model supports adjustable reasoning effort, allowing applications to spend more compute on harder tasks and less on simpler requests. GPT-6 Luna can handle software engineering, multi-step business workflows, computer interaction, and other tool-using tasks that benefit from low operating cost. Improved GPT-6 prompt caching helps long-running agents reuse more context, respond faster, and reduce the cost of repeated input. GPT-6 Luna is available in ChatGPT Work, Codex, the ChatGPT desktop app for Free and Go users, and the OpenAI API as gpt-6-luna.
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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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Gemini 4 Argon
5.0 out of 5 stars
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Gemini 4 Argon is a frontier artificial intelligence model developed by Google DeepMind for complex reasoning, software engineering, cybersecurity, and professional knowledge work. The model supports multimodal inputs, including text, images, audio, video, and PDF documents, with a unified context window of approximately one million tokens. Its advanced reasoning capabilities enable it to analyze difficult problems, maintain context across lengthy workflows, and execute multistep tasks. Gemini 4 Argon supports autonomous AI agents, code generation, application development, scientific research, and analysis of legal and financial information. Developer capabilities include function calling, code execution, structured outputs, search grounding, and preview support for computer use. The model is available through the Gemini API, Google AI Studio, Google Antigravity, and supported Gemini applications and enterprise services.
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