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.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 the model card: Data: 6.21M decision examples.

Mix: 58.3% core decision skills, 16.1% safety, 16.1% judging and expert domains, 9.5% public data.Objective: cross-entropy plus a Brier calibration term, with a trained “I don’t know” option.Recipe: 1 epoch, 43,390 steps, peak learning rate 1e-5, bf16.Hardware: 2 nodes of 8 H100 GPUs.

Calibration lets you set a confidence threshold.Uncertain answers can escalate to elephant-1, mammoth-1 or a human.(function(){var f=document.getElementById('mtp-joey1-frame'); window.addEventListener('message',function(e){if(e.data&&e.data.type==='mtp-joey1-height'&&e.source===f.contentWindow){f.

style.height=e.data.h+'px';}});})(); How fast and cheap is joey-1?Baseten reports a P50 end-to-end latency of 20 ms for an average 2,000-token request.That is about 2.5x faster than elephant-1 (49 ms).The blog’s TL;DR also claims 5x faster than comparable decision models, without naming them.

Model APIs charge $0.01 per 1M input tokens; output and cached input are listed at $0.00.elephant-1 costs $0.035 and mammoth-1 costs $0.10.Baseten’s example: 10M support tickets a month at about 500 tokens each is 5B tokens.That is about $50 on joey-1, versus $500 on mammoth-1.

How does joey-1 score on benchmarks?All scores below are self-reported on Baseten’s model card.On the Jev Decision Index 0.4 public split, joey-1 scores 63.30.That ranks #37 of 230 models and is the top score under 4B.The Jev Decision Index is a community leaderboard on Hugging Face.

On the same split, Cloudflare’s 27B Clef scores 61.71 and Jev 1.14 scores 60.70.joey-1 trails elephant-1 in every area.It beats Jev 1.14 in 4 of 5 areas, including language, tools and retrieval.On Baseten’s replica of the Artificial Analysis Decision Index, joey-1 scores 62.9, versus 65.

9 for mammoth-1.joey-1 vs.other small decision models Featurejoey-1elephant-1Perplexity Decider Flash v1.2Cloudflare Clef-FlashDeveloperBaseten (Base Labs)Baseten (Base Labs)PerplexityCloudflareParameters2B9B4B9BBase modelQwen3.5-2BQwen3.5-9BQwen3.5-4BQwen3.5-9BLicenseMITMITApache-2.0Apache-2.

0InputsText, imagesText, imagesText, imagesText, JSON, images, videoMax context16,384 (Model APIs); 262k on dedicated16k (Model APIs); 262k on dedicated262,144 (trained to about 32k)16,384 defaultAPI price per 1M input tokens$0.01$0.035$0.01Not disclosedJev Decision Index 0.4 (public)63.3072.2460.

06Not disclosedReported latency20 ms P5049 ms P50Not disclosed38.8 ms medianSelf-host hardware1 CUDA GPUNot disclosedAbout 9 GiB BF16 weights on a CUDA GPUTested on 1 H200Weights accessPublicPublicPrivate repo, token requiredPublic Jev 0.4 scores for all models come from Baseten’s joey-1 model card.

Latency figures are not comparable.Baseten measures end-to-end on its platform; Cloudflare reports its own Decision Index 0.2.1 run.How do you deploy joey-1?Model APIs: call decision-models/baseten-joey-2b-latest at /v1/systemone.Zero data retention is on by default.

Dedicated deployments: Baseten says these unlock 262k context, fine-tuned weights and co-location with LLM workloads.Self-hosted: download from Hugging Face and run setup.sh.It needs 1 CUDA GPU and a PyTorch 2.8 / CUDA 12.8 image.

You can fine-tune joey-1 on Baseten Training Jobs using the fine-tuning cookbook.joey-1 is also listed on OpenRouter at $0.01 per 1M input tokens.Key Takeaways joey-1 returns labels plus calibrated probabilities, not text.Baseten reports 20 ms P50 latency at $0.01 per 1M input tokens.63.

30 on Jev Decision Index 0.4 public: top model under 4B.Trails elephant-1 by 8.94 points; best as a first-line router.MIT weights on Hugging Face; self-host on 1 CUDA GPU.Check out the blog post, model card, model library page and fine-tuning cookbook.

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