Cost Optimized Reasoning For Stem Applications

via “compact reasoning model with stem optimization”

Latest compact reasoning model with native tool use.

Unique: Domain-specific distillation trained on curated STEM datasets rather than general reasoning; uses sparse attention and quantized embeddings to compress reasoning capability into a mini-class model, achieving 10-50x cost reduction vs. o1/o3 while maintaining domain-specific reasoning quality.

vs others: Cheaper and faster than o1/o3 for STEM workloads (estimated 5-10x cost reduction, 3-5x latency reduction) but with narrower reasoning scope; stronger than GPT-4o on math/physics but weaker on general reasoning tasks requiring cross-domain knowledge.