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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."
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.
Researchers tested an AI system called Jev on 2,029 real phone calls stripped down to pure call structure—no audio or transcripts—and it learned to predict which calls would result in bookings with 94% accuracy by the end, all for $3 in compute costs.
- Jev made 38,012 turn-level forecasts across the calls at 118ms median latency, processing thousands of calls in seconds without any fine-tuning or examples from the company's data.
- The system achieved 0.78 AUC by the halfway point and reached 94% ranking accuracy near the end, learning to separate bookable from non-bookable calls based solely on call structure, timing, and workflow patterns.
- The experiment revealed the AI could extract predictive signal from call metadata alone—turns, tool calls, workflow stages—demonstrating that meaningful patterns exist independent of what's actually said.
Jev is a new AI model that classifies data and answers specific questions 193x faster and 444x cheaper than LLMs, but it's fundamentally different—it's a modernized BERT-style classifier, not a generative model. The author tested it on Croatian text classification and found it matched a fine-tuned model's accuracy while costing pennies and taking minutes.
- Jev achieves 97.1% accuracy on zero-shot binary classification tasks, essentially matching fine-tuned BERT models without requiring retraining
- It costs around 20 cents for 3,000 classifications and completes in about a minute, making it practical for real-world workloads where LLMs are overkill
- The model appears more resistant to prompt injection attacks because instructions are separated from input data during training, unlike LLMs where everything blends together
- Jev's "can't hallucinate" claim means it can't generate outputs outside its specified structure (type-safe), but it can still produce incorrect answers within that structure
New lightweight AI models like Jev and SemIf outperform expensive general-purpose language models on straightforward classification tasks—cutting costs by 76-209x while improving accuracy. These tools skip the expensive token-generation process and instead directly evaluate a handful of predefined options, suggesting a future where production systems use cheap specialized models rather than frontier AI.
- Jev and SemIf reduced classification costs by 76-209x compared to frontier models while jumping accuracy from 47% to 80-82% on email thread categorization
- These models work by running attention once and evaluating candidate answers directly from logits, skipping the expensive autoregressive token generation that general LLMs use
- The author replaced roughly 25% of AI calls in their own code with these specialized deciders, suggesting if-then logic is a ripe target for optimization—and hinting other programming primitives could follow
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.