MiMo-V2.6-Flash

Overview

MiMo-V2.6-Flash is an open-weight foundation model from Xiaomi, released on September 21, 2026. Its Mixture-of-Experts design has 309B total parameters, with 15B activated per token. The listed model ID is xiaomi/mimo-v2.6-flash, and the Hugging Face ID is XiaomiMiMo/MiMo-V2.6-Flash-RL.

For teams evaluating model fit, the practical outline is broad input support, image and vision capability, reasoning, and a context window of 1,050,000 tokens. Outputs are text, with a maximum output length of 131,072 tokens. The available details establish the model's architecture and interface profile, but do not describe specific benchmark results or task-level performance.

It sits within Xiaomi models, and is also listed among open-weight models, newest models, reasoning models, vision language models, and longest-context models.

Key features

Mixture-of-Experts architecture

The model has 309B total parameters, while 15B are activated per token. That distinction describes its MoE structure; it should not be read as a claim about measured speed or quality.

Long context and output

A context window of 1,050,000 tokens gives the model a large stated context capacity. Its maximum output is 131,072 tokens. These are separate limits: the output allowance is not the same as the full context window.

Multimodal input, text output

Inputs are listed as text, image, video, and audio, while the output format is text. Vision is explicitly supported, and reasoning and tools are both marked as available. No further detail is provided about tool integrations or the handling of each input modality.

Pricing

Listed API pricing is USD 0.14 per 1M input tokens and USD 0.28 per 1M output tokens. Input and output are billed at different rates, so teams estimating spend should account for both token directions rather than treating the model as a single-rate service.

Platforms

The model is identified by the API-style name xiaomi/mimo-v2.6-flash, and the associated open-weight listing is XiaomiMiMo/MiMo-V2.6-Flash-RL. The supplied details do not specify hosting options, deployment requirements, or supported runtime platforms, so those should be checked against the needs of a target stack before adoption.

Who it's for

MiMo-V2.6-Flash is worth evaluating for teams that need a Xiaomi open-weight model with broad listed input modalities, reasoning capability, and a very large context window. Its relatively distinct input and output prices also make token mix relevant when comparing expected operating cost. Teams that require verified performance on a particular workload, specific tool behavior, or deployment guidance will need information beyond the stated model profile.

Pros and cons

Pros

• Open weights, with an identified Hugging Face model ID.
• Text, image, video, and audio are listed as inputs, with vision support and text output.
• A 1,050,000-token context window and a 131,072-token maximum output are specified.
• Input and output pricing are clearly stated per 1M tokens.

Cons

• The available facts do not provide benchmark results or evidence of performance on specific tasks.
• Hosting, deployment requirements, and detailed tool integrations are not specified.
• The 309B total parameter count is substantial, but no infrastructure or serving guidance is given.

Alternatives

Within Xiaomi's lineup, MiMo-V2.6-Pro and MiMo-V2.6-Pro-UltraSpeed are adjacent options to compare. Earlier family entries include MiMo-V2.5 and MiMo-V2.5-Pro. The supplied facts do not establish differences in their capabilities or prices, so selection among them calls for a direct review of their current specifications.

Verdict

MiMo-V2.6-Flash presents a clear profile for evaluation: Xiaomi open weights, MoE architecture, multiple listed input modalities, reasoning and tools, a 1,050,000-token context window, and token-based API prices. That is enough to shortlist it when those characteristics match a team's needs. It is not enough to conclude that it will outperform another model or run conveniently in a particular environment; those decisions depend on performance and deployment details not included here.