El nuevo modelo joey-1 de Baseten ofrece clasificación ultrabarata y de alta velocidad para el enrutamiento y las comprobaciones de seguridad, reduciendo significativamente los costos de las aplicaciones de IA de alto volumen.
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Baseten has released joey-1, a 2B model from its in-house research lab, Base Labs. The joey-1 decision model returns a label and a calibrated probability instead of generated text. It targets high-volume routing, classification and guardrail checks. Baseten prices it at $0.01 per million input tokens, with open weights on Hugging Face under MIT. TL;DR Size: 2B parameters, post-trained from Qwen3.5-2B. 16,384-token max sequence (Baseten says 262k on dedicated deployments). Runs on: Baseten Model APIs, or self-hosted on 1 CUDA GPU (VRAM not disclosed). No official quantized builds listed. Performance: Top model under 4B on the Jev Decision Index 0.4 public split, per Baseten. Best: 63.30 on Jev Decision Index 0.4 (public split), ranked #37 of 230 models. Worst: Trails its 9B sibling elephant-1 by 8.94 points on the same split (63.30 vs 72.24). Bottom line: Best: 20 ms P50 decisions for $0.01 per 1M tokens. Worst: less general knowledge than its larger siblings. What is joey-1? joey-1 is a 2B decision model from Baseten’s Base Labs that answers typed questions in 1 forward pass. You send a state (text or images) and 1 or more questions. The model returns a probability for every option of every question. It does not write free-form text that your application must parse. It supports 3 question types: choice: pick 1 named option, such as which team owns a ticket. yes/no (noul in the API): returns the probability of yes. score: ordered levels, lowest first, such as urgency. joey-1 is the third member of a family. Baseten shipped mammoth-1 (27B) and elephant-1 (9B) last week. All 3 share training data and the /v1/systemone API, so switching is a 1-line change. How does joey-1 work? Base Labs post-trained Qwen3.5-2B with a full fine-tune of the language model. The vision encoder stays frozen. Full-attention layers use a block-bidirectional mask. All questions are answered from 1 shared-prefix prefill, so extra questions add little cost. Training details from
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