OpenAI Decisions API vs Jev vs Parallel: a head-to-head comparison

OpenAI’s Decisions API, TypeSafe’s Jev, and Parallel’s Decisions API provide structured, probability-based answers to typed questions, but differ primarily in input formats, pricing, capabilities, and access to current information. OpenAI and Jev answer using supplied context, with Jev costing $0.042 per million input tokens and OpenAI $0.10, while OpenAI supports up to 400 questions, prompt caching, images, JSON, and GPT-Live integration, and Jev emphasizes calibrated decision-making, supports up to 255 choice options, and offers image and object-location features. Parallel charges $1 per 1,000 requests and distinguishes itself by searching the live web, sharing retrieval across questions, and returning citations, which can make it preferable for high-volume decisions involving current public facts. Benchmarks cited in the comparison suggest the systems perform similarly when answers are contained in the supplied input, whereas Parallel performs better than the input-only endpoints on recent web-based questions, though a web-enabled GPT-6 Luna configuration achieved higher accuracy at substantially greater cost and latency. The comparison recommends OpenAI or Jev for decisions based on existing internal evidence, Parallel for fast cited decisions requiring current web information, and hybrid workflows that route different question types to the appropriate service.

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