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Model details

Coding Xiaomi MiMo-V2.5

MiMo-V2.5 is positioned as an open model for multimodal coding agents and long-context automation. Its coding-focused route is presented as a practical model for software-development workflows, where reasoning and tool use can support iterative tasks rather than simple text completion. The available model page explicitly exposes thinking, tools, tool calling, and structured outputs, while the provider listing also supports temperature control.

The strongest fit is for long-running coding and automation workloads that need substantial that quick-info value and programmatic interaction. It combines a very large that quick-info value window with tool-oriented capabilities, making it suitable for agent-style tasks, repository-scale analysis, and workflows that require structured machine-readable results. The supplied evidence does not establish a parameter count, training scale, benchmark leadership, or detailed architecture lineage, so those points should not be inferred.

AIHubMixcoding-xiaomi-mimo-v2.5mimo-v2.5

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Provider
AIHubMix
Model key
coding-xiaomi-mimo-v2.5
Release date
Apr 22, 2026
Last updated
May 13, 2026
Knowledge cutoff
2024-12
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.08
Output token cost
$0.40

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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Coverage

Xiaomi's MiMo V2.5 is an open-weight model family positioned in the 1-trillion-total / 15-42 billion-active MoE configuration class, with a 1-million-token context window and multimodal elements, according to a third-party profile published on DIT (Dara Initiative Tech) dated 24 August 2026. The page frames the family The DIT profile characterizes MiMo V2.5's primary appeal as a coding-value MoE suitable for internal code assistants and agent pipelines, with generalist knowledge and conversation described as solid though not top-of-table on the hardest reasoning benchmarks. The author notes that quantization is expected for most dep

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