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Model details

MiMo-V2.5

MiMo-V2.5 sits within Xiaomi's open MiMo family and is positioned as a flagship model for general agentic work, complex software engineering, and long-horizon tasks. The variant described on public router listings is described as capable of independently completing professional tasks that would take human experts days or weeks, including workflows that involve more than a thousand tool calls, which signals a strong emphasis on autonomous agent behavior rather than single-turn question answering. Its very long context window of up to one million tokens makes it a natural fit for integration with a wide range of agent frameworks that need to keep large codebases, document collections, or tool histories in working memory at once.

In practical deployments the model has been picked up by the community for serious hardware experiments, with an active NVIDIA developer forums thread running across June and July 2026 focused on running a MiMo V2.5 Flash variant across two DGX Spark GB10 nodes, suggesting that the weights are genuinely portable across consumer and workstation-class accelerators rather than being locked to a single inference stack. The MiMo-V2.5-Pro weights are openly hosted on Hugging Face under the XiaomiMiMo organization, reinforcing that this is an open-weight release intended for self-hosting, fine-tuning, and integration into bespoke pipelines rather than a closed API-only product.

Impossiblxiaomi/mimo-v2.5mimo

Quick Info

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Provider
Impossibl
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
$0.14
Output token cost
$0.28

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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