MiMo-V2.6-Flash Video Understanding Input and Workflow

One-sentence takeaway

The official documentation lists mimo-v2.6-flash as a supported model for video understanding. Videos can be supplied through a publicly accessible video URL or a Base64 Data URL with a MIME prefix, while fps and media_resolution control temporal sampling and per-frame detail. The page examples use mimo-v2.6-pro and do not constitute measured results for Flash.

Use cases

• Suitable tasks: Publicly accessible video analysis, content description, understanding actions or temporal changes, and tasks that require video sampling to be adjusted for cost and detail.

• Unsuitable tasks: Workflows that directly upload local video files; the official FAQ explicitly states that local video file uploads are not currently supported. Do not extrapolate example outputs from the Pro, Pro UltraSpeed, or older models to Flash.

• Applicable model version: mimo-v2.6-flash. The source also lists mimo-v2.6-pro, mimo-v2.6-pro-ultraspeed, and mimo-v2.5; this article attributes only the support-list conclusion to Flash.

• Applicable client, Agent, or API: The MiMo API's OpenAI Chat Completions API; the source examples use the Python SDK and Curl.

• Recommended reasoning tier and parameters: The source examples use fps=2, media_resolution="default", and max_completion_tokens=1024, but the example model is Pro, so these cannot be claimed as optimal parameters for Flash. Adjust them based on video duration, action density, detail requirements, and context budget.

Ready-to-use content

The following is a reproduction skeleton that sets the request model to mimo-v2.6-flash based on the official support list. The official page's Quick Start, URL, Base64 code, and response examples all use mimo-v2.6-pro, so the Flash version below expresses the official support scope and request format, not an officially provided measured response for Flash.

Public video URL input

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("MIMO_API_KEY"),
    base_url="https://api.xiaomimimo.com/v1",
)

completion = client.chat.completions.create(
    model="mimo-v2.6-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://example.com/video.mp4",
                    },
                    "fps": 2,
                    "media_resolution": "default",
                },
                {
                    "type": "text",
                    "text": "Please describe the content of the video.",
                },
            ],
        }
    ],
    max_completion_tokens=1024,
)

print(completion.model_dump_json())

The URL must be publicly accessible to the server, and each individual video file must not exceed 300 MB.

Base64 input

When a public video URL is unavailable, first encode the video as Base64, then place the Data URL with its MIME prefix in the same field:

data:{MIME_TYPE};base64,$BASE64_VIDEO

Replace {MIME_TYPE} with the actual video MIME type. $BASE64_VIDEO must contain only the raw Base64 content, without adding the prefix again. The encoded string for a single video must not exceed 50 MB.

Testing or workflow steps

1. Prepare a MiMo API Key and use https://api.xiaomimimo.com/v1.

2. Set model to mimo-v2.6-flash; do not treat responses from Pro, Pro UltraSpeed, or older models as Flash results.

3. Choose an input method: use a URL for an existing publicly accessible resource; use a Base64 Data URL with a MIME prefix for a video that cannot be accessed publicly.

4. Put a video block and the text task in the content array of the user message; make the goal clear, whether it is description, summarization, action recognition, or another task.

5. Adjust fps and media_resolution for the task, send the request, and read usage in the API response.

6. Record the actual video_tokens and audio_tokens; do not replace the API's actual measurement with an estimate.

Original evidence and data

• Model support: The page's Supported models section explicitly lists mimo-v2.6-flash, along with mimo-v2.6-pro, mimo-v2.6-pro-ultraspeed, and mimo-v2.5.

• Input methods: Public video URLs and Base64 encoding are supported; local video file uploads are not currently supported.

• Video formats: MP4, MOV, AVI, and WMV; because there are many format variants, the official guidance requires confirming through actual testing whether a given variant can be recognized correctly.

• Size limits: A single video must not exceed 300 MB when using the URL method; a single encoded string must not exceed 50 MB when using the Base64 method.

• Multiple-video limit: The number of videos is limited by the model's context length; the total tokens for all videos and text must be less than the model's context length.

• Sampling controls: The default value of fps is 2, with a range of [0.1, 10]; a higher value means denser frame extraction, richer temporal detail, and higher token consumption. The default value of media_resolution is default, and it can also be set to max; max is used to improve recognition of small objects and texture details.

• Token estimation: The official documentation provides a Python estimation function. Its visible parameters include video duration, width and height, fps, media_resolution, and whether the video is silent; the estimate is for reference only, and actual consumption is determined by the API response. The approximate relationship given for audio is audio_tokens ≈ audio duration (seconds) × 6.25.

• Version boundary of the official examples: The model in both the example request and response on the page is mimo-v2.6-pro; the kitten description, reasoning content, token usage, and response model name in those examples are not Flash data.

• Page date: The body displays Update Time September 22, 2026; no publication date is separately disclosed.

Applicability boundaries

• The direct evidence for mimo-v2.6-flash is the official support list; the page provides no Flash-specific baseline for input, responses, latency, quality, or tokens.

• fps=2, media_resolution="default", and max_completion_tokens=1024 come from the Pro example and can only serve as starting configurations; they cannot be claimed as recommended values for Flash.

• The URL must be accessible to the MiMo server; a local browser path does not satisfy the URL input requirement. The Base64 limit applies to the encoded string, and encoding increases its size.

• The number of videos is also constrained by context length; the video token estimation function cannot replace the actual measurement returned by the API.

• Do not extrapolate output quality, reasoning tokens, or performance from mimo-v2.6-pro, mimo-v2.6-pro-ultraspeed, or mimo-v2.5 to mimo-v2.6-flash. There is also no evidence that this page applies to MiMo-V2.6-Flash-RL.

Reproduction notes

1. Open the original source, confirm that Supported models still includes mimo-v2.6-flash, and check the page update time.

2. Prepare a public video URL that meets the format and size limits, or generate a Base64 Data URL with a MIME prefix.

3. Use the Chat Completions structure above, keep model set to mimo-v2.6-flash, and submit a clear video description or analysis task.

4. Save the raw response and usage fields, and label them as an independent reproduction result for Flash; do not cite the official Pro example response as a Flash result.

5. To adjust cost or detail, change fps and media_resolution separately, and record the actual token usage along with input conditions such as video format and duration.