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OpenAI released the Decisions API in public beta, which makes classification and decision-making tasks roughly 10 times faster than their Responses API. The tradeoff is higher cost per request and currently no token caching support, which some developers find economically unattractive for certain use cases.
- Decisions API processes text and image inputs to return typed answers (probabilities, choices from sets, rubric scores) about 10x faster than the Responses API, with median response times around 0.3-0.8 seconds on typical connections
- Accuracy is comparable to Luna (the model used in Responses API) but slightly faster and more variable in performance, though the practical difference is negligible
- No token caching is currently available, making the API more expensive per request than alternatives for repeated classification tasks—a limitation developers hope gets addressed soon
Musubi released PolicyLM-1.7B, a lightweight decision model that applies content policies written in plain English to messages in under 50 milliseconds without needing retraining when policies change. The model costs and runs as fast as traditional AI classifiers but offers the flexibility of modern language models to handle complex moderation rules.
- PolicyLM-1.7B can process content policies in plain English and make binary moderation decisions in under 50 milliseconds, matching the speed and cost of existing classifier systems while avoiding the need for retraining when policies shift.
- Decision models output predetermined outcome probabilities rather than text, making them faster and cheaper than large language models while keeping transformer architecture flexibility — a trend that started with TypeSafe AI's Jev in September, followed by OpenAI and Amazon releases.
- The model gives platform teams a scalable way to label and understand content at scale without building custom training pipelines for each policy change, addressing the challenge of moderating exponentially growing content volumes.