OpenAI launches Decisions API that reduces complex evaluations to yes, no, or pick one
OpenAI released a Decisions API that classifies text and images roughly ten times faster than its Responses API, returning yes/no, category, or scale ratings at $0.10 per million input tokens. OpenAI also consolidated its paid API tiers from five to three.
Key Takeaways
- Key Highlight：OpenAI released a Decisions API that classifies text and images roughly ten times faster than its Responses API, returning yes/no, category, or scale ratings at $0.10 per million input tokens. OpenAI also consolidated its paid API tiers from five to three.
- Innovation & Tech：Highlights advancements in OpenAI, API, Decisions, demonstrating rapid progress in model capabilities.
- Industry Impact：Reported via The Decoder, offering actionable signals for developers and technology leaders.
OpenAI has introduced a new Decisions API designed to simplify how developers build classification and evaluation tasks. Instead of generating free-form text, the endpoint returns structured outputs such as yes/no probabilities, category selections, or scale ratings.
The API processes both text and images, and OpenAI reports it runs about ten times faster than the existing Responses API. Priced at $0.10 per million input tokens, it targets use cases where speed and cost matter more than open-ended generation.
By reducing complex evaluations to constrained choices, the API could lower latency and inference costs for applications like content moderation, routing, and automated decision pipelines. It also reflects a broader trend of offering task-specific inference endpoints rather than relying solely on general-purpose chat models.
Alongside the launch, OpenAI streamlined its paid API tiers from five down to three, simplifying its developer pricing structure.
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Industry Insights & Analysis
As artificial intelligence rapidly evolves, breakthroughs surrounding OpenAI, API, Decisions, Responses are shifting toward scalable, robust real-world implementations.
Driven by both open-source ecosystems and proprietary model architectures, the integration between compute optimization, data engineering, and agentic workflows is accelerating. This development provides a strategic benchmark for upcoming AI tooling and developer workflows.