Xiaomi Launches MiMo-V2.5 With 1M Context Window at $0.40 per Million Input Tokens

Xiaomi released MiMo-V2.5 on April 22, 2026, a native omnimodal model featuring a 1,048,576 token context window priced at $0.40 per million input tokens and $2 per million output tokens.

Specifications and Pricing

MiMo-V2.5 offers:

• Context window: 1,048,576 tokens (1M)
• Input pricing: $0.40 per million tokens
• Output pricing: $2 per million tokens
• Release date: April 22, 2026

According to Xiaomi, the model delivers "Pro-level agentic performance at roughly half the inference cost" compared to unspecified alternatives, though the company has not provided independent benchmark scores to verify these claims.

Technical Capabilities

Xiaomi describes MiMo-V2.5 as a "native omnimodal model" designed for multimodal perception across image and video understanding tasks. The company claims the model surpasses its predecessor, MiMo-V2-Omni, in multimodal perception, though specific benchmark comparisons were not disclosed.

The 1M context window is designed to handle complete documents, extended conversations, and complex task contexts in a single inference pass. Xiaomi positions this capability as particularly suited for integration with agent frameworks.

Availability

The model is currently available through OpenRouter, which routes requests across multiple providers to optimize uptime and handle varying prompt sizes. OpenRouter supports the model's reasoning capabilities through a dedicated reasoning parameter that exposes step-by-step thinking processes via a reasoning_details array in API responses.

What This Means

MiMo-V2.5 enters an increasingly competitive omnimodal model market with a clear value proposition: extended context at lower input pricing than many enterprise-tier alternatives. At $0.40 per million input tokens, it undercuts several comparable models while offering a 1M context window—a specification typically reserved for premium tiers.

The focus on agentic workflows suggests Xiaomi is targeting developers building autonomous systems that require sustained reasoning across multimodal inputs. However, without published benchmark scores on standard evaluation sets like MMLU, VQAv2, or video understanding benchmarks, independent assessment of the model's claimed performance advantages remains difficult. The model's effectiveness will ultimately be determined by real-world deployment results in production agent systems.