Inference Cost Reduction

via “cost-optimized inference with dynamic reasoning depth”

Latest compact reasoning model with native tool use.

Unique: Implements automatic complexity-based reasoning budget allocation via a pre-inference classifier, reducing costs for simple problems without sacrificing quality on complex ones. This differs from fixed-reasoning-depth models (o1/o3) and non-reasoning models (GPT-4o) which don't adapt reasoning investment.

vs others: More cost-efficient than o1/o3 for mixed workloads (estimated 30-50% cost reduction for typical applications) while maintaining reasoning quality; more capable than GPT-4o on complex problems while being cheaper on simple ones.