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Minicpm V Video

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MiniCPM-V 4.5 is the latest, most capable MiniCPM-V model for AI video understanding and analysis. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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The video presents a soldier in full combat gear, including helmet and visor goggles, standing before an illuminated control panel. The scene is set within what appears to be the interior of a spacecraft or advanced military command center, characterized by dim lighting with various glowing screens scattered throughout. Initially, we see him pointing at different locations on one particular screen that displays maps—first towards South America then shifting focus northward through Central American territories until eventually zeroing onto Mexico's territory as well parts beyond it into North Americas broader region such as United States Canada etc.. His actions suggest he might be analyzing strategic points possibly planning operations across these regions using this high-tech interface which glows brightly against darker backdrop emphasizing its importance during analysis process. As time progresses without significant changes occurring around except subtle shifts like slight movements from background figures indicating ongoing activity nearby but not directly involving main subject; our protagonist continues interacting closely examining details presented via holographic display while maintaining steady posture signifying concentration amidst complex data flow surrounding them creating immersive futuristic atmosphere blending technology human element seamlessly together underlining narrative focused exploration tactical decision-making crucial for success missions ahead.

$0.015每次運行·~66 / $1

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相關模型

README

MiniCPM-V Video

MiniCPM-V Video is an advanced video understanding model that analyzes video content and generates detailed descriptions, summaries, or answers to your questions. Simply upload a video and let the AI understand what's happening on screen.

Why It Stands Out

  • Video understanding: Analyzes visual content, actions, and scenes in videos.
  • Preset prompts: Choose from ready-to-use prompts like "describe" for quick analysis.
  • Custom prompts: Ask specific questions or request particular types of analysis.
  • Affordable pricing: Get video insights at a low cost per analysis.
  • Reproducibility: Use the seed parameter to get consistent results.

Parameters

ParameterRequiredDescription
videoYesSource video (upload or public URL).
preset_promptNoPre-defined prompt type (e.g., describe & caption).
custom_promptNoYour own question or instruction about the video.
seedNoSet for reproducibility; -1 for random.
enable_sync_modeNoWait for result before returning response (API only).

How to Use

  1. Upload your video — drag and drop a file or paste a public URL.
  2. Select a preset prompt — choose "describe" or other available options for quick analysis.
  3. Add a custom prompt (optional) — ask specific questions about the video content.
  4. Click Run and wait for analysis.
  5. Review the output — get detailed descriptions or answers about your video.

Best Use Cases

  • Content Analysis — Understand what's happening in videos without watching them.
  • Video Cataloging — Generate descriptions for video libraries and archives.
  • Accessibility — Create text descriptions of video content for accessibility purposes.
  • Content Moderation — Analyze video content for review and categorization.
  • Research & Analysis — Extract insights from video data at scale.
  • Social Media — Generate captions and descriptions for video posts.

Pricing

OutputPrice
Per video$0.015

Pro Tips for Best Quality

  • Use videos with clear visuals for more accurate analysis.
  • Use preset prompts for quick, general descriptions.
  • Use custom prompts to ask specific questions like "What objects are in this video?" or "Describe the main action."
  • Keep videos reasonably short for faster processing.
  • Fix the seed when you need consistent results across multiple runs.

Notes

  • Ensure uploaded video URLs are publicly accessible.
  • Processing time varies based on video length and current queue load.
  • Please ensure your content complies with usage guidelines.
提示:本網站部分功能由第三方 AI 模型提供支援。

Minicpm V Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/minicpm-v/video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Minicpm V Video below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "preset_prompt": "describe",
    "seed": -1
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/minicpm-v/video" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/minicpm-v/video";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "preset_prompt": "describe",
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "preset_prompt": "describe",
    "seed": -1
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/minicpm-v/video", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Minicpm V Video API — Frequently asked questions

What is the Minicpm V Video API?

Minicpm V Video is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. MiniCPM-V 4.5 is the latest, most capable MiniCPM-V model for AI video understanding and analysis. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Minicpm V Video API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/minicpm-v-video.

How much does Minicpm V Video cost per run?

Minicpm V Video starts at $0.015 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Minicpm V Video accept?

Key inputs: `video`, `seed`, `custom_prompt`, `enable_sync_mode`, `preset_prompt`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/minicpm-v-video.

How long does Minicpm V Video take to generate?

Average end-to-end generation time on WaveSpeedAI is around 478 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Minicpm V Video outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Minicpm V Video | AI Video Understanding API | WaveSpeedAI