MiMo-V2.6-Flash Image Understanding Inputs and Multi-image Workflow

One-sentence takeaway

The official documentation lists mimo-v2.6-flash as a supported image-understanding model. Images can be provided through a public URL or Base64, and multiple images can be compared; the documentation does not provide a Flash-specific response, so the Pro example output, token usage, and results shown on the page cannot be extrapolated to Flash.

Use cases

• Suitable tasks: Image description, classification, single-image question answering, and comparing the relationships and differences between multiple images in the same request.

• Unsuitable tasks: Workflows that depend on directly uploading local files; the official FAQ clearly states that local image file uploads are not currently supported. Do not treat the Pro example results as a guarantee of Flash performance.

• Applicable model versions: mimo-v2.6-flash. The source's supported-model list also includes mimo-v2.6-pro, mimo-v2.6-pro-ultraspeed, and mimo-v2.5; these models are recorded only as version boundaries.

• Applicable client, Agent, or API: The MiMo API's OpenAI Chat Completions API; the source also provides the image content format for the Anthropic Messages API. The examples use the Python SDK and Curl.

• Recommended reasoning tier and parameters: The source example uses max_completion_tokens=1024, but the example model is Pro, so this does not represent Flash's optimal value. Set it according to the required output length, and control the input size based on the number of images and context length.

Ready-to-use content

The following is a reproduction skeleton with the request model set to mimo-v2.6-flash according to the official supported-model list. The complete code and response examples on the official page all use mimo-v2.6-pro, so this code expresses Flash's official support scope and request format, not an officially provided Flash test response.

Public URL input (OpenAI Chat Completions)

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": "system",
            "content": "You are MiMo, an AI assistant developed by Xiaomi.",
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.png",
                    },
                },
                {
                    "type": "text",
                    "text": "Please describe the content of the image.",
                },
            ],
        },
    ],
    max_completion_tokens=1024,
)

print(completion.model_dump_json())

Base64 input

When a public URL is unavailable, first read and encode the image, then pass the following format as image_url.url:

data:{MIME_TYPE};base64,$BASE64_IMAGE

Replace {MIME_TYPE} with the actual MIME type. $BASE64_IMAGE should contain only the plain Base64 content of the image file; do not add the prefix again. The Anthropic Messages API instead uses the fields type: "image", source.type: "base64", source.media_type, and source.data.

Multi-image input

Place multiple image_url content blocks in the content array of the same user message; the images can mix public URLs and Base64. Then add a clear comparison task, such as:

Please describe the connections and differences between these two pictures.

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 the input method based on the data conditions: use a public URL for an existing publicly accessible resource; use a Base64 Data URL with a MIME prefix for images that cannot be accessed publicly.

4. Place the image content blocks and the text task in order within the same user message; for multi-image tasks, clearly specify what should be described, classified, compared, or extracted.

5. Send the request and read the response; when stable downstream processing is needed, constrain the output format first, then parse and validate it on the client.

6. Record the actual usage from the API response on the client; do not use an estimate in place of the actual image-token consumption.

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: Publicly accessible image URLs and Base64 encoding are supported; multiple images can be provided using URLs or Base64 at the same time.

• Image formats: JPEG, PNG, GIF, WebP, BMP.

• Single-image size: For the URL method, a single image must not exceed 50 MB; for the Base64 method, the Base64 string for a single image must not exceed 50 MB.

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

• Local files: The FAQ clearly states that mimo-v2.6-flash, mimo-v2.6-pro, mimo-v2.6-pro-ultraspeed, and mimo-v2.5 currently do not support uploading local image files.

• Version boundary of official examples: The model in the Quick Start, URL, Base64, and multi-image code examples is mimo-v2.6-pro; the model in the example response is also mimo-v2.6-pro. Its completion_tokens, prompt_tokens, image_tokens, and description text are not Flash data.

• Image-token estimate: The source gives PATCH_SIZE=16, SPATIAL_MERGE_SIZE=2, IMAGE_MIN_PIXELS=8192, and IMAGE_MAX_PIXELS=8388608. Image dimensions are rounded to a multiple of 32; if they exceed the upper limit, they are reduced, and if they fall below the lower limit, they are enlarged. The estimate then uses num_tokens = (grid_t * grid_h * grid_w) / (SPATIAL_MERGE_SIZE ** 2); actual consumption is subject to the API response.

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

Scope and limitations

• The evidence for mimo-v2.6-flash is the official supported-model list; the page provides no Flash-specific input, response, latency, quality, or token baseline.

• max_completion_tokens=1024 appears only in the Pro example, so it cannot be claimed as a recommended parameter for Flash.

• An image URL must be accessible to the server; a browser-local path cannot be used as a public URL.

• The 50 MB Base64 limit applies to the encoded string; encoding increases the size, so check the actual size before sending.

• Multi-image requests are also constrained by context length; sendability cannot be determined solely by the size of each image.

• The image-token formula is an estimation tool and cannot replace the actual measurement returned by the API.

• Do not extrapolate the output quality, reasoning tokens, or performance of mimo-v2.6-pro, mimo-v2.6-pro-ultraspeed, or mimo-v2.5 to mimo-v2.6-flash.

Reproduction notes

1. Open the original URL, confirm that mimo-v2.6-flash is still included under Supported models, and verify the page update time.

2. Prepare a public image 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 description or comparison task.

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

5. If image tokens need to be estimated, calculate them using the source code and then compare them with the returned API value; use the API value as the authoritative value.