视频理解

视频理解模型可以根据您传入的视频进行回答，支持视频 URL 和 Base64 编码两种传入方式，适用于视频分析等场景。

快速开始

注意：获取 API Key 等准备工作，请参考 首次调用API。

通过视频 URL 方式传入模型快速体验视频理解效果，示例代码如下。

Curl

curl --location --request POST 'https://api.xiaomimimo.com/v1/chat/completions' \
--header "api-key: $MIMO_API_KEY" \
--header "Content-Type: application/json" \
--data-raw '{
    "model": "mimo-v2.5",
    "messages": [
        {
            "role": "system",
            "content": "You are MiMo, an AI assistant developed by Xiaomi. Today is date: Tuesday, December 16, 2025. Your knowledge cutoff date is December 2024."
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://example-files.cnbj1.mi-fds.com/example-files/video/video_example.mp4"
                    },
                    "fps": 2,
                    "media_resolution": "default"
                },
                {
                    "type": "text",
                    "text": "please describe the content of the video"
                }
            ]
        }
    ],
    "max_completion_tokens": 1024
}'

Python

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-v2.5",
    messages=[
        {
            "role": "system",
            "content": "You are MiMo, an AI assistant developed by Xiaomi. Today is date: Tuesday, December 16, 2025. Your knowledge cutoff date is December 2024."
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://example-files.cnbj1.mi-fds.com/example-files/video/video_example.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())

响应结果

{
    "id": "a7a6fe52ffeb40ab9e699fe018592b16",
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "content": "The video features a small, fluffy white kitten with bright blue eyes sitting on a polished wooden floor. The kitten is positioned near a window draped with sheer, light-colored curtains, through which sunlight is streaming, casting warm shadows on the floorboards. In the background to the right, a potted green plant, resembling a Monstera, adds a touch of color to the room.\\n\\nThroughout the clip, the kitten is meowing repeatedly. It opens its mouth wide to let out high-pitched meows, looking slightly to its left before turning its gaze directly toward the camera. The audio consists entirely of the kitten's vocalizations.",
                "role": "assistant",
                "tool_calls": null,
                "reasoning_content": "The user wants me to describe the video provided.\\n\\n1.  **Analyze the video:**\\n*   **Subject:** A small white kitten with striking blue eyes.\\n*   **Action:** The kitten is sitting on a wooden floor near a window. It is meowing. Its mouth opens and closes repeatedly. It looks slightly to its left and then more directly forward.\\n*   **Setting:** Indoors. There's a window on the left with light-colored curtains. Sunlight is streaming in, casting shadows on the floor. In the background, there's a green plant (looks like a Monstera) in a pot. The walls are light-colored.\\n*   **Audio:** The audio consists of the kitten meowing. It's a series of high-pitched meows.\\n*   **Visual details:** The kitten is fluffy. The floor is polished wood. The lighting is warm and natural.\\n\\n2.  **Break down the description chronologically:**\\n*   **Start (00:00 - 00:01):** The video opens with a white kitten sitting on a wooden floor. It's positioned near a window with sheer curtains. Sunlight is hitting the floor. The kitten is looking slightly to the side.\\n*   **Middle (00:01 - 00:02):** The kitten opens its mouth wide to meow. It does this multiple times. Its gaze shifts a bit. The sunlight highlights its white fur.\\n*   **End (00:02 - 00:03):** The kitten continues to meow. It looks directly at the camera/viewer. The background remains consistent with the plant and the wall.\\n\\n3.  **Synthesize the description:**\\n*   Start with the main subject: A white kitten with blue eyes.\\n*   Describe the environment: Sitting on a wooden floor near a window with curtains. Sunlight is present. A plant is in the background.\\n*   Describe the action: The kitten is meowing repeatedly. It looks around and then at the camera.\\n*   Mention the audio: The sound of the kitten meowing.\\n\\n4.  **Refine the language:**\\n*   \\\"A white kitten with blue eyes is sitting on a wooden floor.\\\"\\n*   \\\"It is positioned next to a window with light curtains.\\\"\\n*   \\\"Sunlight is streaming in, creating shadows.\\\"\\n*   \\\"The kitten is meowing, opening its mouth wide.\\\"\\n*   \\\"In the background, there is a potted green plant.\\\"\\n*   \\\"The kitten looks to the side and then towards the camera while meowing.\\\"\\n\\n5.  **Final Polish:** Combine these points into a coherent paragraph or a list of key observations. The prompt asks to \\\"describe the content of the video,\\\" so a narrative description is appropriate.\\n\\n*   *Draft:* The video shows a cute white kitten with blue eyes sitting on a polished wooden floor. It's near a window with white curtains that are letting in bright sunlight. To the right, in the background, there is a large green plant. The kitten is meowing repeatedly, opening its mouth wide. It looks around a bit and then stares directly at the camera. The audio is just the sound of the kitten's meows.\\n\\n*   *Structure:*\\n*   **Scene:** White kitten, wooden floor, window, sunlight, plant.\\n*   **Action:** Meowing, looking around.\\n*   **Audio:** Meowing sounds.\\n\\nThis covers all the essential aspects of the video."
            }
        }
    ],
    "created": 1776850708,
    "model": "mimo-v2.5",
    "object": "chat.completion",
    "usage": {
        "completion_tokens": 849,
        "prompt_tokens": 1260,
        "total_tokens": 2109,
        "completion_tokens_details": {
            "reasoning_tokens": 719
        },
        "prompt_tokens_details": {
            "audio_tokens": 19,
            "cached_tokens": 1256,
            "video_tokens": 1144
        }
    }
}

支持的模型列表

当前仅支持 mimo-v2.5，mimo-v2-omni 模型。

视频传入方式

支持的视频传入方式如下：

  • 视频 URL 传入：需提供公网可访问的视频 URL 地址。

  • Base64 编码传入：将视频转换为 Base64 编码字符串后再传入。

视频 URL 传入

通过公网可访问的视频 URL 地址直接传入视频，适用于视频已存储在公网可访问环境的场景。单个视频文件大小不能超过 300 MB。

Curl

curl --location --request POST 'https://api.xiaomimimo.com/v1/chat/completions' \
--header "api-key: $MIMO_API_KEY" \
--header "Content-Type: application/json" \
--data-raw '{
    "model": "mimo-v2.5",
    "messages": [
        {
            "role": "system",
            "content": "You are MiMo, an AI assistant developed by Xiaomi. Today is date: Tuesday, December 16, 2025. Your knowledge cutoff date is December 2024."
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://example-files.cnbj1.mi-fds.com/example-files/video/video_example.mp4"
                    },
                    "fps": 2,
                    "media_resolution": "default"
                },
                {
                    "type": "text",
                    "text": "please describe the content of the video"
                }
            ]
        }
    ],
    "max_completion_tokens": 1024
}'

Python

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-v2.5",
    messages=[
        {
            "role": "system",
            "content": "You are MiMo, an AI assistant developed by Xiaomi. Today is date: Tuesday, December 16, 2025. Your knowledge cutoff date is December 2024."
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://example-files.cnbj1.mi-fds.com/example-files/video/video_example.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())

Base64 编码传入

将视频文件转换为 Base64 编码字符串后传入，适用于视频无法通过公网 URL 访问的场景。转换后的 Base64 编码的字符串大小不能超过 50 MB。

请在 Base64 编码前携带前缀：data:{MIME_TYPE};base64,$BASE64_VIDEO

  • {MIME_TYPE}：视频的 MIME 类型（媒体类型），用于标识视频格式，需替换为实际视频对应的 MIME 值。
  • $BASE64_VIDEO：视频文件的纯 Base64 编码字符串（不含任何前缀）。

Curl

curl --location --request POST 'https://api.xiaomimimo.com/v1/chat/completions' \
--header "api-key: $MIMO_API_KEY" \
--header "Content-Type: application/json" \
--data-raw '{
    "model": "mimo-v2.5",
    "messages": [
        {
            "role": "system",
            "content": "You are MiMo, an AI assistant developed by Xiaomi. Today is date: Tuesday, December 16, 2025. Your knowledge cutoff date is December 2024."
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "data:{MIME_TYPE};base64,$BASE64_VIDEO"
                    },
                    "fps": 2,
                    "media_resolution": "default"
                },
                {
                    "type": "text",
                    "text": "please describe the content of the video"
                }
            ]
        }
    ],
    "max_completion_tokens": 1024
}'

Python

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-v2.5",
    messages=[
        {
            "role": "system",
            "content": "You are MiMo, an AI assistant developed by Xiaomi. Today is date: Tuesday, December 16, 2025. Your knowledge cutoff date is December 2024."
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "data:{MIME_TYPE};base64,$BASE64_VIDEO"
                    },
                    "fps": 2,
                    "media_resolution": "default"
                },
                {
                    "type": "text",
                    "text": "please describe the content of the video"
                }
            ]
        }
    ],
    max_completion_tokens=1024
)

print(completion.model_dump_json())

使用说明

视频限制

  • 视频格式：MP4，MOV，AVI，WMV。

视频文件格式变种较多，不能保证所有文件都能被识别，请通过测试验证文件能够被正常识别。

  • 视频大小：

    • 以 URL 方式传入时：单个视频文件大小不超过 300 MB。

    • 以 Base64 编码传入时：单个视频的 Base64 编码字符串大小不超过 50 MB。

  • 视频数量：传入多个视频时，视频数量受模型上下文长度限制，所有音频和文本的总 Token 数必须小于模型的上下文长度。

注：计算视频的 Token 请参考 视频 Token 用量说明。模型上下文长度请参考 定价与限速。

控制视频理解的精细度

您可以分别通过 fps 和 media_resolution 两个字段来控制视频理解的精细度。

  1. fps 即每秒从视频中抽取图像的帧数，用于控制视频时间维度的理解精细度。默认值为 2，范围为 [0.1, 10]。

    • 数值越高，抽帧越密集，模型对画面变化、动作、时序细节的感知越精细；

    • 数值越低，抽帧越稀疏，处理速度越快，Token 消耗越少。

  2. media_resolution 即视频帧的解析分辨率档次，用于控制单帧画面的视觉理解精细度。默认值为 default。

    • default：默认档次，平衡识别效果与处理效率；

    • max：最高分辨率档次，提升对小物体、细节纹理的识别能力。

视频 Token 用量说明

视频的 Token 分为 video_tokens（视觉）与 audio_tokens（音频）。

  • video_tokens 计算请参考以下代码。估算结果仅供参考，实际用量以 API 响应为准。

    """
根据视频的时长、分辨率，估算 API 调用所消耗的 Token 数。
用户可通过 fps 和 media_resolution 两个参数控制精细度：
  - fps: 每秒抽帧数，默认 2，范围 [0.1, 10]。越高时序越精细，Token 越多。
  - media_resolution: 单帧分辨率档次。"default" 平衡效果与效率；"max" 提升细节识别。
"""

import math

def estimate_video_tokens(
    duration: float,
    width: int,
    height: int,
    fps: float = 2.0,
    media_resolution: str = "default",
    mute: bool = False,
) -> int:
    """
    估算视频输入的 Token 数。

    Args:
        duration:         视频时长（秒）
        width:            视频宽度（像素）
        height:           视频高度（像素）
        fps:              抽帧帧率，默认 2，范围 [0.1, 10]
        media_resolution: "default" 或 "max"
        mute:             True 则不计算音频 Token

    Returns:
        预估总 Token 数
    """
    # ---- 常量 ----
    PATCH, MERGE, T_PATCH = 16, 2, 2
    SPATIAL = PATCH * MERGE                         # 32
    PIX_PER_TOKEN = SPATIAL ** 2                    # 1024
    MAX_TOTAL_TOKENS = 131072
    TOTAL_MAX_PIX = MAX_TOTAL_TOKENS * PIX_PER_TOKEN
    MIN_PIX, MAX_PIX = 8192, 8388608
    MAX_FRAMES = 2048
    DEFAULT_MAX_FRAME_TOKEN = 300

    # ---- 1. 抽帧数 ----
    nframes = math.ceil(duration * fps)
    nframes = min(nframes, MAX_FRAMES)
    nframes = max(math.ceil(nframes / T_PATCH) * T_PATCH, T_PATCH)

    # ---- 2. 单帧像素预算 ----
    max_pix = TOTAL_MAX_PIX * T_PATCH // nframes
    if media_resolution != "max":
        max_pix = min(max_pix, DEFAULT_MAX_FRAME_TOKEN * PIX_PER_TOKEN)
    max_pix = max(MIN_PIX, min(max_pix, MAX_PIX))

    # ---- 3. 缩放分辨率 ----
    h, w = height, width
    if min(h, w) < SPATIAL:
        if h < w:
            w = int(w * SPATIAL / h); h = SPATIAL
        else:
            h = int(h * SPATIAL / w); w = SPATIAL
    h_bar = round(h / SPATIAL) * SPATIAL
    w_bar = round(w / SPATIAL) * SPATIAL
    if h_bar * w_bar > max_pix:
        beta = math.sqrt(h * w / max_pix)
        h_bar = math.floor(h / beta / SPATIAL) * SPATIAL
        w_bar = math.floor(w / beta / SPATIAL) * SPATIAL
    elif h_bar * w_bar < MIN_PIX:
        beta = math.sqrt(MIN_PIX / (h * w))
        h_bar = math.ceil(h * beta / SPATIAL) * SPATIAL
        w_bar = math.ceil(w * beta / SPATIAL) * SPATIAL

    # ---- 4. Token 计算 ----
    grids = nframes // T_PATCH                       # 时序网格数
    tokens_per_grid = (h_bar // PATCH) * (w_bar // PATCH) // (MERGE ** 2)
    vision = grids * tokens_per_grid
    timestamps = grids * (5 if fps > 2 else 3)       # 时间戳文本 token
    special = grids * 2 + 2                           # 特殊标记

    # ---- 5. 音频 Token ----
    audio = 0
    if not mute:
        spec_len = int(duration * 24000) // 240 + 1
        t = (spec_len - 1) // 2 + 1
        t = t // 2 + int(t % 2 != 0)
        audio = math.ceil(t / 4) + 2                 # +2 for audio special tokens

    return vision + timestamps + special + audio

# ============ 示例 ============
if __name__ == "__main__":
    # 一个 1080p、60 秒的视频
    tokens = estimate_video_tokens(duration=60, width=1920, height=1080)
    print(f"默认参数 (fps=2, default): {tokens:,} tokens")

    tokens = estimate_video_tokens(duration=60, width=1920, height=1080, fps=5)
    print(f"高帧率  (fps=5, default): {tokens:,} tokens")

    tokens = estimate_video_tokens(duration=60, width=1920, height=1080, media_resolution="max")
    print(f"高分辨率 (fps=2, max):     {tokens:,} tokens")

    tokens = estimate_video_tokens(duration=60, width=1920, height=1080, mute=True)
    print(f"静音    (fps=2, mute):    {tokens:,} tokens")

  • audio_tokens 计算请参考以下代码。估算结果仅供参考，实际用量以 API 响应为准。

    总 Tokens 数 ≈ 音频时长（单位：秒）* 6.25

计费说明

  • 计费：总费用根据输入、输入（命中缓存）和输出 Token 数计算；价格请参考 定价与限速。

    • 可通过 视频 Token 用量说明 计算视频的 Token 消耗。估算结果仅供参考，实际用量以 API 响应为准。

  • 查看账单：您可以在控制台的 账单明细 页面查看账单及用量。

常见问题

是否支持本地文件上传？

mimo-v2.5 和 mimo-v2-omni 模型暂不支持视频本地文件上传。支持的上传方式请参考 视频传入方式。

更新时间 2026 年 04 月 22 日
音频理解语音识别（MiMo-V2.5-ASR）
We use cookies and similar technologies of our own to ensure the proper functioning of the website, customize content according to user preferences and analyze users' interactions on the website, as well as their browsing habits. You can find more information in our Cookie Policy. Select an option or go to Cookie Settings to manage your preferences. Learn More.