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Jev is a new AI model that classifies data and answers specific questions 193x faster and 444x cheaper than LLMs, but it's fundamentally different—it's a modernized BERT-style classifier, not a generative model. The author tested it on Croatian text classification and found it matched a fine-tuned model's accuracy while costing pennies and taking minutes.
- Jev achieves 97.1% accuracy on zero-shot binary classification tasks, essentially matching fine-tuned BERT models without requiring retraining
- It costs around 20 cents for 3,000 classifications and completes in about a minute, making it practical for real-world workloads where LLMs are overkill
- The model appears more resistant to prompt injection attacks because instructions are separated from input data during training, unlike LLMs where everything blends together
- Jev's "can't hallucinate" claim means it can't generate outputs outside its specified structure (type-safe), but it can still produce incorrect answers within that structure