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Model details

MiMo-V2.5

Xiaomi MiMo-V2.5 sits within Xiaomi's emerging foundation-model lineup, with sibling variants in the MiMo family released alongside open-weight checkpoints hosted under the XiaomiMiMo organization on Hugging Face. The release positions the model for production agentic and software-engineering workloads, where long-horizon autonomy is a core design goal. By making weights publicly available, Xiaomi enables self-hosting and deeper integration into custom agent frameworks, distinguishing the MiMo line from closed flagship peers.

The model's flagship framing emphasizes agentic capability and complex software engineering, with marketing claims highlighting top-tier rankings on ClawEval, GDPVal, and SWE-bench Pro, and the ability to chain more than a thousand tool calls on professional tasks that would take human experts days or weeks. A that quick-info value window reaching approximately one million tokens supports integration with a wide range of agent frameworks, making the model well suited for repository-scale code analysis, long-document reasoning, and multi-step planning. Practical fit centers on teams building autonomous coding assistants, research agents, and workflow automation that benefit from extended that quick-info value and open-weight deployment flexibility.

ZenMuxxiaomi/mimo-v2.5mimo

Quick Info

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Provider
ZenMux
Model key
xiaomi/mimo-v2.5
Release date
Apr 22, 2026
Last updated
Apr 22, 2026
Knowledge cutoff
2024-12
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.40
Output token cost
$2.00

Limits

Output tokens
131,072 tokens
Context window
1,048,576 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about MiMo-V2.5

OpenRouter

CoverageBenchmark

A community-authored NVIDIA developer-forums thread (a3refaat, May 2026) documents a reproducible recipe for serving a third-party NVFP4 quantization of MiMo-V2.5 (lukealonso/MiMo-V2.5-NVFP4) on a 2× DGX Spark / GB10 cluster. The setup uses vLLM 0.21.1rc1.dev39 on CUDA 13.2 with tensor parallelism = 2, instanttensor lo Three concrete correctness fixes are detailed: (1) the ModelOpt mixed-precision path must dispatch MXFP8 linear layers to the MXFP8 method rather than falling through when the checkpoint mixes MXFP8 dense layers with NVFP4 experts; (2) the weight_scale_inv field is UE8M0 MXFP8 scale metadata and should not be reciproca

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