MiMo-V2-Flash API

What Is Xiaomi MiMo-V2-Flash API?

Xiaomi MiMo-V2-Flash is an advanced, open-source large language model (LLM) developed by Xiaomi’s MiMo team, designed for high-performance AI applications including reasoning, coding, general text generation, and agentic workflows.

Scalable MoE Architecture with Massive Capacity

MiMo-V2-Flash uses a Mixture-of-Experts (MoE) design. While the model contains hundreds of billions of parameters in total, only a carefully selected subset is activated during inference. This approach enables large-model intelligence with the computational footprint of a much smaller system.

The result is a rare balance: the reasoning depth and representational power of a very large model, combined with the efficiency required for practical deployment in real products.

Long-Context Understanding

MiMo-V2-Flash is built for long-form intelligence. With support for context windows up to 256,000 tokens, the model can process entire codebases, extensive technical documentation, multi-chapter reports, or long conversational histories in a single pass.

To achieve this, Xiaomi combines different attention strategies within the same model, ensuring that both local details and global structure are preserved. This makes MiMo-V2-Flash particularly effective for tasks that demand continuity, memory, and deep contextual awareness.

Strong Reasoning and Engineering Performance

Beyond speed, MiMo-V2-Flash is designed to excel at structured thinking. It performs reliably on complex reasoning tasks, multi-step problem solving, and software engineering workflows. This makes it a strong choice for applications such as code generation, debugging assistance, planning agents, and analytical tools that require consistent logic over long sequences.

Key Features

• Hybrid SWA Architecture: Combines aggressive 128-token SWA with global attention for memory-efficient long-context processing, outperforming linear attention in general tasks.​
• Multi-Token Prediction (MTP): Native self-speculative decoding generates 2.8-3.6 draft tokens per step, verified in parallel for 2-2.6x throughput without extra KV cache costs.​
• MOPD Post-Training: Multi-Teacher Online Policy Distillation uses token-level rewards from expert teachers, achieving teacher-level performance with 1/50th the compute of traditional RL.​

vs. Kimi K2

MiMo-V2-Flash matches performance on most reasoning benchmarks with a fraction of active parameters (15B vs. much larger), plus superior long-context results via hybrid SWA. It delivers 3x faster inference at 150 t/s while costing less, making it preferable for high-throughput agentic workflows.​

vs. DeepSeek V3.2

Comparable reasoning scores across math and coding, but MiMo-V2-Flash leads open-source on SWE-Bench Multilingual (71.7%) and agentic tasks. Its MTP boosts speed 2-3x over DeepSeek's denser architecture, ideal for real-time applications.

vs. Claude Sonnet 4.5

‍Approaches Claude's agentic performance (e.g., 73.4% SWE-Bench) as an open-source alternative, with 6x KV savings and MTP for faster deployment. Excels in multilingual coding where Claude shows inconsistencies.

Capabilities

• Function calling
• Streaming
• Structured outputs