AI model
Jev is TypeSafe AI's model that selects one option from a provided list based on a natural-language state description, outputting structured choices only.
AWS open-sourced Strands Decider 2B, a 2B-parameter decision model it says makes a decision in a median of about 115 ms on an RTX 3090. Strands Box, the sandbox released with it, enforces agent rules in a kernel-level policy layer outside the model.
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IBM says its Granite Switch 4.1 8B and 30B models run chat, a decision adapter, a guardian adapter and speech recognition in about one base model's VRAM. The memory claim is IBM's own, so any GPU saving depends on what a team serves today.
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Sayora's receptionist test found Jev gave the LLM a wrong page on 3% of 168 questions, against 33% for its embeddings. The figure is Sayora's own, from four small-business sites, and transfers only to sites built from single-topic pages.
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Workday's Joel Hellermark says its agents pair reasoning models with deterministic guardrails, itwire reported. Sana, bought for US$1.1 billion, now orchestrates every Workday agent, so the model-driven half of that split runs in one acquired product.
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Subquadratic's SubQ Snap decision model accepts 384,000 tokens per request, 12 times Jev's 32K limit, according to the company. CEO Alex Whedon says the cap reflects preview compute capacity. The accuracy and latency figures are Subquadratic's own.
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Workday gave TypeSafe's Jev every atomic component in its design system and lets the model pick what Sana shows, said chief AI officer Joel Hellermark. TypeSafe claims 70 to 500 milliseconds per answer; whether Workday sees that depends on how many screens reduce to closed questions.
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OpenAI, Microsoft and Perplexity launched decision-model APIs within nine days at $0.10, $0.042 and $0.02 per million input tokens. Perplexity's 27B weights are Apache 2.0, so a buyer with a roughly 49 GiB GPU can self-host v1.1.
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Microsoft has put Decision-1, a model that scores fixed choices, in Foundry at $0.042 per million input tokens. Agent teams now have to test whether its scores, so far benchmarked only by Microsoft, are calibrated well enough to decide when a case goes to a human.
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Earendil's Pi 1.0, an agent harness whose website once advertised "No MCP", now builds MCP into its core. The team ties the reversal to Codemode, a JavaScript interpreter beside the harness loop that lets the model batch MCP calls into one script.
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Andreessen Horowitz led a financing of about $870 million in TypeSafe at a $7.5 billion valuation, Bloomberg reported. The money backs Jev, a model that hands software a choice plus a probability, and its use to builders depends on those probabilities matching how often it is right.
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Cloudflare's Clef-omni takes audio and video alongside text and images and returns schema-bound decisions in one API call. Runtimewire reported that the same release cut hosted Clef-flash's context window from 64,000 to 24,000 tokens, so longer prompts now go to Clef or to self-hosted weights.
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Cloudflare and AWS have followed TypeSafe's Jev with Clef and Strands Decider 2B, small models built to pick an agent's next tool or step. The pitch is a smaller token bill, though the practitioners dev.to cites say calibration and the cost of a wrong pick decide whether any saving survives.
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AWS, Upstage and Ollama now serve decision models through TypeSafe's System One request format, within three weeks of TypeSafe launching Jev. OpenAI has not signed on, so teams wiring decision calls into agents are picking an interface while the standard is still forming.
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OpenAI launched its Decisions API on October 6, three weeks after startup TypeSafe's cheap classification model Jev went viral with developers. Both chase the routine judgments a business automates by the thousand, where cost per decision decides what is worth doing.
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Cloudflare released Clef, open-weight 9B and 27B models that return a probability for each allowed option in a single forward pass. An agent's routing or escalation call becomes a score it can threshold, and so far the only latency figures come from Cloudflare.
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Diogo Almeida left OpenAI two years ago convinced that human language is the wrong output for automation. The model his startup shipped this week returns probabilities, and one team testing it clocked classification 5 to 18 times faster.
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OpenAI's Decisions API answers yes/no, pick-one and scale questions for $0.10 per million input tokens, with output free. Jev sells the same three question types for text at less than half that, so OpenAI's case rests on image input and compliance terms.
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Ollama and llama.cpp added local endpoints within a week for Jev decision models, 144M-to-27B checkpoints that return a label and a probability. Routing calls can move off hosted models once teams check each checkpoint's licence and test it on their own labels.
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OpenAI's Ultrafast tier, unveiled at DevDay 2026, sells faster GPT-6 Astra tokens at six times the standard API rate. Speed is now a separate line on the bill, so teams building coding pipelines have to decide which jobs are worth it.
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TypeSafe's Jev 1.13 matched no-CoT GPT-5.5 and Opus 4.7 on agent-monitoring AUROC at about 1/200th of Opus 4.7's cost, a LessWrong benchmark found. It answers in one step without writing text, so its calls leave no reasoning trace to audit.
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