Baseten Releases joey-1, a 2B Decision Model at $0.01 per 1M Tokens

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.

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