Behind the MiMo API Price Reduction： The deepest price cut， up to 99%， is for Input （Cache Hit）. The core reason is our inference framework now supports hierarchical KV cache optimization for SWA. Production inference engine tests show this optimization increases cached token capacity by 5x， equivalent to an 80% reduction in caching costs. Combined with Cache Read Overlap among multiple Full Attention modules in the Hybrid model， actual costs are further reduced.

Prices for Input （Cache Miss） and Output are also reduced by 60%-80%. This mainly benefits from the extreme 1：7 Full：SWA sparsity ratio brought by the model architecture （the prefill compute of the 70-layer MiMo-V2.5-Pro roughly equals a 10-layer GQA model）. This kept our original inference costs well below the industry average， naturally leaving a 2x-3x profit margin in pricing. This price adjustment simply reflects our decision to pass these structural cost efficiencies directly to developers.