HomeXiaomi MiMo-V2.5 Series API Gets Permanent Price Cut, Up to 99% Off
Amid the escalating AI model price wars, Xiaomi officially announced on May 27 that its MiMo large model would permanently reduce prices for the MiMo-V2.5 series API while simultaneously optimizing the billing system to further lower developers' calling costs through technological advancements.
I. Significant API Price Cuts — Up to 99% Off
The price change took effect globally at 00:00 Beijing Time on May 27. It applies to the two core versions, MiMo-V2.5 and MiMo-V2.5Pro, and no longer differentiates based on context window length, simplifying the pricing structure for greater transparency.
Model VersionInput Cache Hit PriceMaximum DiscountOutput PriceMaximum DiscountMiMo-V2.5Pro0.025 yuan per million tokens, up to 99% off; output: 6 yuan per million tokens, up to 86% offMiMo-V2.50.02 yuan per million tokens, up to 98% off; output: 2 yuan per million tokens, up to 93% offII. Billing System Upgrade — More Value at No Extra Cost
Beyond the direct API price cuts, Xiaomi has heavily optimized its Token Plan billing system:
Quadrupled Quota: Under the original pricing, the actual token usage quota has increased to 5 to 8 times the previous amount.
Simplified Rules: The introduction of Credits replaces the previous complex billing methods, making token consumption and cost calculation more intuitive for developers.
III. Technical Foundation — How Can It Keep Lowering Prices?
Xiaomi's official statement attributes these deep price cuts to technical breakthroughs in its underlying inference system architecture:
SWA Inference Optimization: By leveraging SGLang HiCache with full support for SWA (Sliding Window Attention Mechanism), the data transfer among GPU memory, CPU memory, and SSD has been reduced to one-seventh of the previous volume.
Improved Cache Efficiency: The number of cacheable tokens has increased nearly fivefold compared to the earlier optimized version, boosting cache hit rates and dramatically lowering per-inference cost.
Cluster Throughput Optimization: With the introduction of expert parallel (MoE) and input length bucketing strategies, the cluster's input throughput has seen a qualitative leap, maintaining high service quality while steadily reducing cost per token.
Xiaomi's move is seen as a proactive response to the current intense competition in large model commercialization. As price barriers continue to drop, the MiMo series' cost-effectiveness will become even more pronounced, accelerating the deep integration of AI capabilities across vertical industries and developer workflows.
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Amid the escalating AI model price wars, Xiaomi officially announced on May 27 that its MiMo large model would permanently reduce prices for the MiMo-V2.5 series API while simultaneously optimizing the billing system to further lower developers' calling costs through technological advancements.
I. Significant API Price Cuts — Up to 99% Off
The price change took effect globally at 00:00 Beijing Time on May 27. It applies to the two core versions, MiMo-V2.5 and MiMo-V2.5Pro, and no longer differentiates based on context window length, simplifying the pricing structure for greater transparency.
Model VersionInput Cache Hit PriceMaximum DiscountOutput PriceMaximum DiscountMiMo-V2.5Pro0.025 yuan per million tokens, up to 99% off; output: 6 yuan per million tokens, up to 86% offMiMo-V2.50.02 yuan per million tokens, up to 98% off; output: 2 yuan per million tokens, up to 93% offII. Billing System Upgrade — More Value at No Extra Cost
Beyond the direct API price cuts, Xiaomi has heavily optimized its Token Plan billing system:
Quadrupled Quota: Under the original pricing, the actual token usage quota has increased to 5 to 8 times the previous amount.
Simplified Rules: The introduction of Credits replaces the previous complex billing methods, making token consumption and cost calculation more intuitive for developers.
III. Technical Foundation — How Can It Keep Lowering Prices?
Xiaomi's official statement attributes these deep price cuts to technical breakthroughs in its underlying inference system architecture:
SWA Inference Optimization: By leveraging SGLang HiCache with full support for SWA (Sliding Window Attention Mechanism), the data transfer among GPU memory, CPU memory, and SSD has been reduced to one-seventh of the previous volume.
Improved Cache Efficiency: The number of cacheable tokens has increased nearly fivefold compared to the earlier optimized version, boosting cache hit rates and dramatically lowering per-inference cost.
Cluster Throughput Optimization: With the introduction of expert parallel (MoE) and input length bucketing strategies, the cluster's input throughput has seen a qualitative leap, maintaining high service quality while steadily reducing cost per token.
Xiaomi's move is seen as a proactive response to the current intense competition in large model commercialization. As price barriers continue to drop, the MiMo series' cost-effectiveness will become even more pronounced, accelerating the deep integration of AI capabilities across vertical industries and developer workflows.
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