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Jared Palmer released Kev, a family of four open-source models (0.8B to 27B parameters) designed to answer multiple-choice and yes/no questions by scoring answer options directly rather than generating tokens. The models run on consumer hardware, come with Apache-2.0 weights, and match or exceed the performance of TypeSafe's proprietary Jev API.
- Kev-27B achieves 75.7% accuracy on generalization tasks and 91.8% on policy reasoning, with smaller variants (Kev-4B, Kev-9B) performing within 1-2 percentage points on specific workflows while using far less compute.
- The architecture uses a pointer head to score supplied answers without token generation, and caches document computation across multiple questions—cutting inference time from 9.4 seconds for a new 64k-token document to 0.7 seconds per follow-up question on an H200.
- Kev-4B runs on a 32GB Mac (136ms per question with cached text) and Kev-0.8B on any Apple Silicon, making local deployment and fine-tuning practical; the largest model costs $44.09 per million requests on H200s versus $3.54 for the smallest.
Kev is a family of small language models (0.8B to 9B parameters) built on Qwen3.5 that make yes/no, multiple-choice, and rating decisions on text inputs. You can run them locally on your own hardware with an API compatible with TypeSafe's System One, plus there's a web playground for testing.
- Three model sizes available with published accuracy metrics: Kev-9B reaches 85.2% accuracy on unseen datasets and trails the proprietary Jev model by only 3.5 points, while Kev-4B and Kev-0.8B offer smaller footprints for different use cases.
- All models run on consumer hardware (CUDA, ROCm, Apple Silicon) with the 4B and 9B versions fitting on a 32GB Mac in bf16 precision, plus you can train your own weights using provided code and evaluation data.
- Probabilities are calibrated by default and the models output confidence scores alongside answers, reducing confident errors to 4% on new data compared to 8.7% without calibration.