# Xiaomi MiMo-V2.5-Pro (1.02T)

> Trillion-parameter sparse MoE model for long-horizon agentic and software-engineering tasks.

| Property | Value |
| --- | --- |
| Lab | Xiaomi (China) |
| Released | 2026-04-22 |
| Decoder type | Sparse MoE |
| Family | Sparse MoE |
| Scale | 1.02T total, 42B active (4.1% active) |
| Context window | 1,048,576 tokens |
| Attention | GQA with 6:1 sliding-window/global attention |
| Layer mix | 60 sliding-window + 10 global |
| KV cache / token (bf16) | 350 KiB (Very high) |
| License | MIT License |

**Key detail:** Scales the MiMo-V2.5 Pro stack to 70 layers, 384 routed experts, top-8 routing, and a 1M-token context window.

## Architecture concepts

- [Grouped-Query Attention (GQA)](https://llm-atlas-7or.pages.dev/concepts/gqa.md): Several query heads share one key/value head, shrinking the KV cache with little quality loss.
- [Sliding Window Attention (SWA)](https://llm-atlas-7or.pages.dev/concepts/swa.md): Some layers only attend to a local window of recent tokens instead of the whole context.
- [Mixture of Experts (MoE)](https://llm-atlas-7or.pages.dev/concepts/moe.md): Many feed-forward experts, but a router sends each token to only a few of them.

## Benchmark (Artificial Analysis Intelligence Index)

- AA Intelligence Index v4.3.2: total 26 (General 34.6, Scientific 19.8, Coding 25.3, Agents 22.2)

## Links

- Architecture diagram: https://sebastianraschka.com/llm-architecture-gallery/images/architectures/xiaomi-mimo25-pro.webp
- config.json: https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro/blob/main/config.json
- Tech report: https://mimo.xiaomi.com/mimo-v2-5
- Source card (source of truth): https://sebastianraschka.com/llm-architecture-gallery/#card-xiaomi-mimo-v2-5-pro-1-02t
- Web page: https://llm-atlas-7or.pages.dev/models/xiaomi-mimo-v2-5-pro-1-02t/
- JSON: https://llm-atlas-7or.pages.dev/api/models/xiaomi-mimo-v2-5-pro-1-02t.json

_Data from Sebastian Raschka's LLM Architecture Gallery (https://sebastianraschka.com/llm-architecture-gallery/). Synced 2026-11-04._