Baseten releases 2B decision model joey-1

Oct 30

1. 05:20

   MarkTechPost

   Baseten Releases 2B Decision Model joey-1, Priced at $0.01 per Million Tokens

   Baseten's Base Labs has released a 2B decision model, joey-1, post-trained on Qwen3.5-2B, which directly returns labels and calibrated probabilities instead of generating text, targeting high-concurrency routing, classification, and guardrail checks. The model scores 63.30 on the public Jev Decision Index 0.4 set, the highest among models under 4B, but 8.94 points lower than the 9B elephant-1. Model APIs are priced at $0.01 per million input tokens, with a P50 latency of 20 ms for an average 2000-token request; the weights are open-sourced under the MIT license on Hugging Face and can be self-hosted on a single CUDA GPU.

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2. 01:49

   Unite.AI

   Baseten Releases 2B Open-Weight Decision Model joey-1

   On October 29, Baseten released joey-1, a 2B-parameter decision model, priced at $0.01 per million tokens. It is now available on Baseten Model APIs and has open weights on Hugging Face. The model was post-trained by the in-house lab Base Labs based on Qwen3.5-2B, and shares the same data recipe and API with mammoth-1 (27B) and elephant-1 (9B), so switching requires only a one-line change. The model card shows that joey-1 scored 63.30 on the public Jev Decision Index 0.4 leaderboard, ranking 37th among 230 models and the highest score among models with fewer than 4B parameters; Baseten says its P50 end-to-end latency for 2000-token requests in production is 20 ms.

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3. 01:24

   Baseten Engineering BlogFirst-partySelected

   Baseten releases decision model joey-1, with P50 latency of 20ms and $0.01 per million tokens

   Baseten's Base Labs has released joey-1, a 2B decision model that directly outputs a single-token label and calibrated confidence, with a P50 end-to-end latency of 20ms, about 2.5 times faster than elephant-1 (49ms), at a price of $0.01 per million tokens, one-tenth that of mammoth-1. joey-1 is post-trained from Qwen3.5-2B and shares the same data recipe and API with elephant-1 and mammoth-1, so switching requires changing only one line. The model is now available on Baseten Model APIs, with zero data retention by default, and its weights are open-sourced on Hugging Face; Model APIs supports a 16k context window, while dedicated deployments can unlock a 262k context window and support fine-tuned weights.

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