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Baseten's research lab released two open-source decision models, mammoth-1 (27B) and elephant-1 (9B), built to classify and route inputs by returning probability scores instead of generating text. Both are free to try for a week on Baseten's Model APIs.
- mammoth-1 ranks #1 of 208 decision models on the Jev Decision Index, and elephant-1 is the top 9B model there.
- Decision models return a probability over possible answers rather than writing text, which makes them faster and cheaper than generative LLMs for tasks like ticket routing and spam detection.
- Confidence thresholds let you auto-handle high-certainty cases and escalate uncertain ones to a human.
- Both were fine-tuned from Qwen3.5 on 6.21 million decision-making examples, including training to say "I don't know."
This is a conversation between Matt Turck and Diogo Almeida about Jev, a system Almeida describes as built to make decisions rather than generate text. The discussion centers on how that shift from output to action could change the way AI gets used.
- Jev is framed as a decision-making system, distinct from text-generation models that produce answers without committing to outcomes.
- Almeida argues that moving from text to decisions changes what evaluation and trust look like, since the output is an action with consequences.
- The episode positions this as more than a feature update, claiming it alters the basic relationship between people and AI tools.
Cloudflare open-sourced two decision models called Clef and Clef-flash that classify inputs into typed, structured outputs with probabilities — useful for routing customer support tickets, identifying phishing domains, or triggering automated agent actions. The models outperform Typesafe's Jev system on speed and benchmarks while supporting images and a 64k context window.
- Clef processes decisions 2x faster than general LLMs (2.2s vs 4.7s in Cloudflare's threat intelligence workflow) because it scores schema choices in parallel rather than generating text token-by-token.
- The models beat Jev on most Jev Decision Index benchmarks and Typesafe's own eval suite, with Clef-flash offering exceptional speed-to-quality tradeoffs; median latency is 209ms for Clef and 38.8ms for Clef-flash.
- Both models are open-sourced on Hugging Face under Apache 2.0, hosted on Workers AI at Cloudflare's edge for low network latency, and fully API-compatible with Jev for easy switching.
TypeSafe AI released Jev, a model that takes text input but outputs confidence scores and probability distributions instead of generated text. It's designed for classification tasks like spam detection and ranking, costs $0.042 per million input tokens with free output, and has already spawned open-source recreations and creative experiments.
- Jev returns structured probabilistic outputs (yes/no scores, rankings, category choices) rather than text, making it fast and cheap—you only pay for input tokens, not output.
- The model trades interpretability for simplicity: you get a floating-point score but no explanation of which signals drove the decision, raising serious concerns about hidden bias in high-stakes use cases like hiring.
- The community has already built unconventional projects with it (a chat model, a game player, a left-pad implementation) and created open-weight alternatives like Kev within days of release.