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Model details

MiMo-V2.5

MiMo-V2.5 has emerged as a fresh entry in the broader MiMo lineup, drawing immediate attention from enthusiasts running compact NVIDIA DGX Spark and GB10 workstations. Community members in the dedicated DGX Spark user forum flagged it as a new model shortly after launch and noted that some early benchmarks looked promising enough to fit within a two-node Spark setup, hinting at a weight footprint aimed at high-end desktop inference rather than hyperscale clusters. The same community space has since grown into an active discussion thread, reflecting real practitioner interest in evaluating the family on consumer-grade accelerated hardware.

A closely related Omni-branded sibling, MiMo V2.5 Omni, has been demonstrated running across three DGX Spark nodes using tensor parallelism together with multi-token prediction, sustaining around 39 tokens per second at a one-the cataloged API limit. That deployment profile, tagged as an agentic-AI project, suggests the V2.5 generation is being explored for long-context assistant and tool-using workloads where extended memory and steady throughput matter more than raw single-request latency. Together, the new-model announcement and the Omni long-context demonstration point to a family oriented toward accessible, locally hosted reasoning rather than purely cloud-scale serving.

CrossModelxiaomi/mimo-v2.5mimo

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CrossModel
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
$0.16
Output token cost
$0.32

Limits

Output tokens
128,000 tokens
Context window
1,000,000 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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