MiMo-V2.6-Flash Audio Understanding Workflow: URL and Base64 Input Configuration

One-sentence takeaway

mimo-v2.6-flash supports audio understanding: place the audio as input_audio in the user message, then append a text task instruction. The audio can be provided through a public URL or Base64 with a MIME prefix; local files cannot be uploaded directly.

Use cases

• Suitable tasks: Audio content description, speech content analysis, and multimodal Q&A that requires reading both audio and text instructions.

• Unsuitable tasks: Directly uploading local audio; assuming without testing that all audio format variants can be recognized; audio tasks that exceed input-size or context limits.

• Applicable model version: mimo-v2.6-flash. The original page also lists mimo-v2.6-pro, mimo-v2.6-pro-ultraspeed, and mimo-v2.5; this article does not attribute those models' example responses to Flash.

• Applicable client, Agent, or API: MiMo API's OpenAI-compatible Chat Completions; the official examples use the OpenAI Python SDK and Curl, with the API endpoint https://api.xiaomimimo.com/v1.

• Recommended reasoning tier and parameters: The documentation does not provide a Flash-specific reasoning tier or fixed parameter recommendations. max_completion_tokens=1024 appears only in the official Pro example; when using Flash, adjust it according to the required output length.

Ready-to-use content

The following is a Flash configuration skeleton organized according to the official request structure. The code on the official page sets model to mimo-v2.6-pro; here, only the model ID is changed to mimo-v2.6-flash based on the Supported models list on the same page. This does not mean that the official documentation provides a Flash-specific response.

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": "input_audio",
                    "input_audio": {
                        "data": "https://example.com/audio/example.wav"
                    },
                },
                {
                    "type": "text",
                    "text": "Please describe the content of this audio",
                },
            ],
        }
    ],
    max_completion_tokens=1024,
)

print(completion.model_dump_json())

If the audio cannot be accessed over the public internet, replace data with a Data URI containing the actual MIME type:

"data": "data:{MIME_TYPE};base64,$BASE64_AUDIO"

Replace {MIME_TYPE} with the audio's actual MIME type. $BASE64_AUDIO may contain only the raw Base64 string; do not add the prefix again. The please describe the content of the audio example on the official page is a replaceable text task instruction, not a Flash-specific prompt.

Testing or workflow steps

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

2. Set the request model to mimo-v2.6-flash.

3. Choose one input method:

   • Public URL: Provide a publicly accessible audio address; a single file must not exceed 100 MB.

   • Base64: Encode the audio first, then use data:{MIME_TYPE};base64,$BASE64_AUDIO; the Base64 string for a single audio file must not exceed 50 MB.

4. Put input_audio and the text task instruction in the content array of the same user message.

5. Send a Chat Completions request and read the returned message.content; in production, also record the actual usage returned by the API.

6. First verify recognizability with an actual file, then expand the range of formats, durations, and multi-audio inputs.

Raw evidence and data

• The page overview states that the audio understanding model can answer based on the provided audio, supporting both audio URL and Base64 input methods.

• Supported models explicitly lists mimo-v2.6-flash, along with mimo-v2.6-pro, mimo-v2.6-pro-ultraspeed, and mimo-v2.5.

• The official code passes type: "input_audio" in the content array, followed by a type: "text" audio task instruction.

• Supported audio formats are MP3, WAV, FLAC, M4A, and OGG. The page notes that there are many format variants and does not guarantee that all of them can be recognized; this should be confirmed through testing.

• URL input requires a publicly accessible address, and a single file must not exceed 100 MB. A single audio string in Base64 input must not exceed 50 MB.

• The number of multiple audio inputs is limited by the model's context length; the total number of tokens for all audio and text must be below the model's context length.

• The page gives the following audio-token estimate: Total token count ≈ audio duration (seconds) × 6.25; this is an estimate, and actual usage is determined by the API response.

• Charges are calculated based on input tokens, cache-hit input tokens, and output tokens; billing and usage can be viewed on the Console's Billing page.

• The FAQ explicitly states that mimo-v2.6-flash, Pro, Pro UltraSpeed, and mimo-v2.5 currently do not support uploading local audio files.

• The Quick Start, URL, Base64 Python/Curl examples, and responses on the page all use mimo-v2.6-pro; the content, reasoning_content, token usage, and model fields in the responses are not results measured with Flash.

Applicable boundaries

• The official statement that mimo-v2.6-flash is supported only proves that the model is included in the supported list; it does not mean that the page provides Flash-specific accuracy, latency, response, or token baselines.

• The URL must be accessible to the service. Private network addresses, local paths, or non-public object-storage addresses cannot be used directly with this workflow.

• The 50 MB Base64 limit applies to the encoded string, not the original audio file size; after encoding, also confirm that the total context length is sufficient.

• “Supported formats” does not guarantee recognition of all encoding parameters, container variants, or corrupted files. The application should handle recognition failures and retain diagnosable error information.

• The audio-token estimate cannot replace actual API metering, nor can it be used to infer price, speed, or quality differences between Flash and Pro, Pro UltraSpeed, or older model versions.

Reproduction notes

1. Open the original article, confirm that Supported models includes mimo-v2.6-flash, and confirm that the page update date is 2026-09-22.

2. Prepare publicly accessible test audio, or generate a Base64 Data URI with the actual MIME type.

3. Call mimo-v2.6-flash using the request structure above, change the text task instruction, and record the returned content, errors, and API usage.

4. Test URL, Base64, supported formats, and multi-audio input separately; keep the Pro examples in the public documentation strictly separate from Flash's actual responses.

5. This article only organizes the official workflow and does not claim to have completed independent Flash API measurements.