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Supersonic Labs, a small AI lab from Brazil, has released Julia 1. It is a compact decision model, not a chatbot. You pass it context, a question, and 2 to 20 candidate answers. It picks one and returns a probability for every option. The model has 144.3M parameters and runs on a plain CPU. Is it deployable? Yes. The weights are on Hugging Face under Apache 2.0 and run locally with Python 3.11+ on CPU or a BF16-capable GPU. An ONNX build also runs in the browser via WebGPU. A hosted API is announced but not open yet. What Julia 1 Does Julia 1 handles three decision types through one API: choice: pick one label from 2 to 20 described options (classification, routing). score: return the expected index on an ordered rubric, such as low, medium, high. noul: return the probability that a yes-or-no statement is true. Results come back in the caller’s option order with full softmax probabilities. Caller IDs such as billing are returned unchanged. The model does not generate text. Architecture and Training Budget Julia 1 starts from JHU CLSP’s mmBERT-small, a 140M-parameter multilingual ModernBERT encoder trained on 1,800+ languages. Supersonic Labs kept the encoder and tokenizer, added a decision head, and trained on decision-format examples. The lab states Julia 1 is not a fine-tuned Qwen model. The runtime supports 8,192 combined tokens, but published benchmarks used a 1,024-token limit. Total cloud GPU spend for training and experiments was about R$540 (US$104.08). The FP32 weights occupy 550.5 MiB. The private training pipeline is not released. Julia 2, with the lab’s own foundation architecture, is in development. Benchmark Results The September 24, 2026 evaluation ran on H200 BF16 with strict encoding. The comparison baseline is TypeSafe’s Jev, using reference values from the Jev benchmark protocol, not a new Jev run. Typed Decisions: 73.15% (1,463/2,000) vs 72.70% reference. AG News, 4 labels: 94/100 vs 91% reference. DAIR Emotion, 6 labels: 86/100 vs 48% reference. Banking77, 72 labels: 64/100 vs 87% reference. This is the clear failure. MASSIVE, 18 scenarios: 71.50% macro accuracy across 52 locales; 86.25% pt-PT, 86.75% en-US. The classification pilots use only 100 examples each. A September 25 CPU run reproduced most numbers: 72.55% on Typed Decisions and 60/100 on Banking77 with 3 abstentions. On-Device Latency The lab published per-device measurements. On an Apple M4, one decision per call took a 33.15 ms median. On a Samsung SM-X510 tablet via ONNX Runtime, the median was 203 ms with 393.1 MB peak RSS. On an Intel Core i5-1235U, AG News decisions took a 107.83 ms median. Banking77 took 3,713.54 ms because it narrows 72 labels first. On X, @supersonicai claims Julia 1 classifies 5x faster than Jev on an i5 laptop. Treat that carefully. The Jev pilot measured Jev as a hosted service called from France, so latencies are not like-for-like. Introducing Julia-1: Our first classification model that runs on almost anything. Learn more https://t.co/YJCdEeIBSo pic.twitter.com/3cizsbG9ZB — Supersonic Labs (@supersonicai) September 26, 2026 Interactive Explainer Julia 1 vs Closest Competitors FeatureJulia 1TypeSafe JevGLiNER2.5 Multi DeveloperSupersonic LabsTypeSafe AIFastino AccessOpen weightsHosted API, early accessOpen weights LicenseApache 2.0ProprietaryApache 2.0 Parameters144.3MNot disclosed287M Base encodermmBERT-smallNot disclosedmDeBERTa-v3-base Decision typesChoice, score, yes/noTyped structured decisionsClassification, NER, relations, records Options per call2 to 20 (Router for more)Up to 255Label list per schema Runs locally on CPUYesNoYes Input price per 1M tokens$0.025 (planned API)$0.042Free (self-hosted) AG News pilot94%91%70% DAIR Emotion pilot86%48%44% Banking77 pilot64%87%61% Sources: Julia 1 model card, TypeSafe launch post, GLiNER2.5 Multi card, Jev benchmark pilot. Julia 1 pilots ran separately from the Jev and GLiNER runs. Limitations Julia 1 compares the answers you supply. It cannot be counted on for missing facts, algebra, or multi-step calculation. The Router can drop the correct label during narrowing. It is not a drop-in Transformers pipeline, and no Hugging Face inference provider serves it. Supersonic Labs advises evaluating on your own questions and keeping humans in the loop for consequential decisions. Key Takeaways Julia 1 is a 144.3M-parameter, Apache 2.0 decision model that runs on CPU. One API covers choice, ordered score, and yes-or-no decisions over 2 to 20 options. It beat Jev references on 3 of 4 pilots but trailed badly on 72-label Banking77. Median latency hit 33.15 ms per decision on an Apple M4. Training cost about US$104 in cloud GPUs; a $0.025/MTok API is planned. Check out the Model Weights, ONNX/WebGPU build, and Technical details. All credit goes to the researcher of this project. 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