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Model details

MiMo-V2.5

MiMo-V2.5 is part of Xiaomi's expanding open-weights MiMo family, a line that has already produced a flagship Pro variant aimed at long-horizon agentic work. Community discussion on NVIDIA's DGX Spark / GB10 forums positioned MiMo-V2.5 as a newly available model whose benchmarks drew strong interest and whose size was seen as workable across a pair of Spark units, suggesting a design intended to stay practical for serious on-prem and developer-hardware deployments. Within the broader family, Xiaomi's flagship sibling is described as capable of independently running professional workflows that would take human experts days or weeks, including sequences of more than a thousand tool calls, signaling that the v2.5 generation is engineered around sustained, tool-driven reasoning rather than short conversational exchanges.

The model's practical fit centers on agent and software-engineering workloads that benefit from very long context and open-weight deployment flexibility. Xiaomi highlights benchmark performance on ClawEval, GDPVal, and SWE-bench Pro for the family, framing MiMo-V2.5 as a strong option for complex coding tasks, evaluation-style analysis, and tool-orchestrated pipelines where a single model must carry state across many steps. Weights for the related Pro variant are hosted on Hugging Face under the XiaomiMiMo organization, reinforcing the family's open-weights posture and making MiMo-V2.5 well suited for teams that want to self-host, fine-tune, or integrate with external agent frameworks while still accessing a model tuned for ambitious, multi-stage tasks.

Deep InfraXiaomiMiMo/MiMo-V2.5mimodeprecated

Quick Info

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Provider
Deep Infra
Model key
XiaomiMiMo/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
16,384 tokens
Context window
262,144 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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