Mastering Veo 3.1 API 6 Major Parameter Differences: Flow Reverse vs Vertex Official Forwarding Complete Comparison Guide

As Google DeepMind's latest video generation model, Veo 3.1 offers a few different ways to hook into its API, which can be a bit confusing for developers. What’s the actual difference between the Flow Reverse interface and Vertex Official Forwarding? In this post, we'll break down the differences across 6 core parameters to help you make the best choice.

Key Takeaway: By the end of this article, you'll know exactly which Veo 3.1 access method fits your specific use case and how to optimize your setup around certain parameter limits.

Overview of Veo 3.1 Access Methods

Before we dive into the parameter specifics, let's look at the basic context of these two access methods.

Veo 3.1 Access Method Basics

Veo 3.1 Model Versions

🎯 Tech Tip: During development, we recommend testing your calls through the APIYI (apiyi.com) platform. It provides a unified API interface for the entire Veo 3.1 model lineup, making it much easier to validate your technical approach quickly.

Detailed Breakdown of Veo 3.1 Core Parameter Differences

Next, let's analyze the differences between the two access methods across six core parameters.

Parameter 1: durationSeconds (Video Duration)

Video duration is one of the most fundamental and critical parameters in video generation.

Veo 3.1 durationSeconds Comparison

Vertex Official Example:

from google import genai
from google.genai import types

client = genai.Client()

# Vertex official supports custom duration
operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt="A cinematic shot of ocean waves at sunset",
    config=types.GenerateVideosConfig(
        duration_seconds=6,  # Options: 4/6/8 seconds
        aspect_ratio="16:9",
        resolution="1080p"
    )
)

Flow Reverse Limitations:

The Flow Reverse API currently only outputs fixed 8-second videos. If your use case requires shorter clips, consider these alternatives:

• Post-processing: Generate the 8-second video and then trim it to your desired length using tools like FFmpeg.
• Scene Splicing: Use the Scene Extension feature to generate continuous segments.

💡 Pro Tip: If you need granular control over video length, the Vertex official relay is your best bet. You can easily switch between methods for testing via APIYI (apiyi.com).

Parameter 2: negativePrompt

Negative prompts are used to exclude unwanted elements from your visuals and are essential for fine-tuning the output quality.

Veo 3.1 negativePrompt Comparison

Vertex Official Example:

from google import genai
from google.genai import types

client = genai.Client()

# Vertex official supports negative prompts
operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt="A professional business meeting in modern office",
    config=types.GenerateVideosConfig(
        negative_prompt="cartoon, drawing, low quality, blurry, distorted",
        aspect_ratio="16:9"
    )
)

Flow Reverse Workarounds:

Although Flow Reverse doesn't support the negativePrompt parameter, you can achieve similar results by optimizing your positive prompt:

# Original Prompt
A cat playing in the garden

# Optimized Prompt (with exclusion terms)
A realistic, high-quality video of a cat playing in the garden.
Photorealistic style, sharp focus, natural lighting.
NOT cartoon, NOT animated, NOT low quality.

Prompt Optimization Tips:

Parameter 3: seed

Seeds are used to control the reproducibility of generation results, which is crucial for scenes requiring consistent output.

Veo 3.1 seed Comparison

Important Note: Even with the Vertex Official API, the seed parameter doesn't guarantee 100% determinism; it simply "slightly improves" result consistency.

Vertex Official Example:

from google import genai
from google.genai import types

client = genai.Client()

# Using seed to improve consistency
operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt="A golden retriever running on the beach",
    config=types.GenerateVideosConfig(
        seed=12345678,  # Specify seed value
        aspect_ratio="16:9",
        resolution="720p"
    )
)

Workaround Strategies for Flow Reverse:

Since Flow Reverse doesn't support seeds, if you need similar outputs:

1. Save Successes: Keep track of prompts that produced great results.
2. Batch Generation: Generate multiple results for the same prompt and pick the best one.
3. Reference Image Guidance: Use the Image-to-Video feature to constrain the output with a reference image.

🎯 Recommendation: If your application demands high consistency (like brand videos or serial content), stick with the Vertex official relay. The APIYI (apiyi.com) platform supports quick switching and side-by-side testing for both methods.

Parameter 4: generateAudio

Native audio generation is one of Veo 3.1's standout features, including dialogue, sound effects, and background music.

Veo 3.1 generateAudio Comparison

Vertex Official Example:

from google import genai
from google.genai import types

client = genai.Client()

# You can choose whether to generate audio
operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt="A chef preparing sushi in a Japanese restaurant",
    config=types.GenerateVideosConfig(
        generate_audio=True,  # Enable audio generation
        aspect_ratio="16:9"
    )
)

# If you don't need audio, turn it off to save processing time
operation_silent = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt="Abstract geometric shapes morphing",
    config=types.GenerateVideosConfig(
        generate_audio=False,  # Disable audio
        aspect_ratio="16:9"
    )
)

Handling Audio in Flow Reverse:

Flow Reverse generates video with audio by default. If you don't want the native audio, you'll need to handle it yourself:

# Remove audio track with FFmpeg
ffmpeg -i input_video.mp4 -an -c:v copy output_silent.mp4

# Replace with custom audio
ffmpeg -i video.mp4 -i custom_audio.mp3 -c:v copy -c:a aac output.mp4

Parameter 5: enhancePrompt (AI Prompt Enhancement)

The enhancePrompt parameter allows the AI to automatically optimize and expand the prompt you input.

Veo 3.1 enhancePrompt Comparison

Important Note: According to official Google documentation, the enhancePrompt parameter currently only supports the Veo 2 model. Veo 3.1 doesn't support this parameter yet.

Manual Prompt Optimization Tips:

Since Veo 3.1 doesn't support auto-enhancement, we recommend optimizing your prompts manually:

Prompt Template:

[Shot Type] + [Subject Description] + [Action/State] + [Environment/Background] + [Lighting] + [Style/Mood]

Example:
"Cinematic wide shot of a lone astronaut walking across Mars surface,
orange dust swirling around boots, dramatic backlighting from setting sun,
epic sci-fi atmosphere, film grain texture"

💰 Cost Optimization: For heavy prompt optimization, you can first use a Claude or GPT model to refine your prompt, then call Veo 3.1 to generate the video. The APIYI (apiyi.com) platform lets you call multiple models flexibly to optimize your overall costs.

Parameter 6: Other Key Parameter Comparison

Beyond the five core parameters, here are a few others worth noting.

Complete Comparison of Other Veo 3.1 Parameters

Resolution Support Details:

Veo 3.1 Parameter Comparison Overview

For quick reference, here's a summary of all the parameter differences:

Veo 3.1 Flow Reverse vs. Vertex Official Parameter Matrix

Veo 3.1 Access Method Selection Recommendations

Based on the parameter comparison above, here are our selection recommendations for different scenarios.

Scenario-Based Selection Guide

When to Choose Flow (Reverse Engineering)

When to Choose Official Vertex

Decision Flowchart

Start Selection
    │
    ├─> Is it for production? ──Yes──> Official Vertex
    │         │
    │         No
    │         │
    ├─> Need negativePrompt? ──Yes──> Official Vertex
    │         │
    │         No
    │         │
    ├─> Need 4K resolution? ──Yes──> Official Vertex
    │         │
    │         No
    │         │
    ├─> Is cost the priority? ──Yes──> Flow (Reverse)
    │         │
    │         No
    │         │
    └─> Choose based on specific needs

💡 Pro Tip: Choosing an access method really depends on your specific use case and quality requirements. We recommend running some real-world tests on the APIYI (apiyi.com) platform. It supports a unified interface for both methods, making it super easy to evaluate and switch between them.

Veo 3.1 API Quick Start Guide

Calling Veo 3.1 via the APIYI Platform

No matter which access method you pick, you can use a unified interface format through the APIYI platform:

import requests
import time

# APIYI 统一接口调用 Veo 3.1
def generate_video_via_apiyi(prompt, duration=8, aspect_ratio="16:9"):
    """
    通过 APIYI apiyi.com 调用 Veo 3.1 视频生成
    """
    url = "https://api.apiyi.com/v1/videos/generations"

    headers = {
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    }

    payload = {
        "model": "veo-3.1-generate-preview",
        "prompt": prompt,
        "duration_seconds": duration,
        "aspect_ratio": aspect_ratio,
        "generate_audio": True
    }

    response = requests.post(url, json=payload, headers=headers)
    return response.json()

# 使用示例
result = generate_video_via_apiyi(
    prompt="A serene Japanese garden with cherry blossoms falling gently",
    duration=8,
    aspect_ratio="16:9"
)

print(f"视频生成任务 ID: {result.get('id')}")

import requests
import time
import os

class VeoVideoGenerator:
    """
    Veo 3.1 视频生成器
    通过 APIYI apiyi.com 统一接口调用
    """

    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://api.apiyi.com/v1"
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }

    def generate_video(self, prompt, **kwargs):
        """
        提交视频生成任务

        参数:
            prompt: 视频描述提示词
            duration_seconds: 视频时长 (4/6/8)
            aspect_ratio: 画面比例 (16:9 或 9:16)
            negative_prompt: 负面提示词 (Vertex 模式)
            seed: 随机种子 (Vertex 模式)
            generate_audio: 是否生成音频
            resolution: 分辨率 (720p/1080p)
        """
        url = f"{self.base_url}/videos/generations"

        payload = {
            "model": kwargs.get("model", "veo-3.1-generate-preview"),
            "prompt": prompt,
            "duration_seconds": kwargs.get("duration_seconds", 8),
            "aspect_ratio": kwargs.get("aspect_ratio", "16:9"),
            "generate_audio": kwargs.get("generate_audio", True)
        }

        # 可选参数 (Vertex 模式支持)
        if "negative_prompt" in kwargs:
            payload["negative_prompt"] = kwargs["negative_prompt"]
        if "seed" in kwargs:
            payload["seed"] = kwargs["seed"]
        if "resolution" in kwargs:
            payload["resolution"] = kwargs["resolution"]

        response = requests.post(url, json=payload, headers=self.headers)
        response.raise_for_status()
        return response.json()

    def check_status(self, task_id):
        """检查生成任务状态"""
        url = f"{self.base_url}/videos/generations/{task_id}"
        response = requests.get(url, headers=self.headers)
        response.raise_for_status()
        return response.json()

    def wait_for_completion(self, task_id, timeout=600, interval=10):
        """
        等待视频生成完成

        参数:
            task_id: 任务 ID
            timeout: 超时时间 (秒)
            interval: 轮询间隔 (秒)
        """
        start_time = time.time()

        while time.time() - start_time < timeout:
            status = self.check_status(task_id)
            state = status.get("status", "unknown")

            if state == "completed":
                return status
            elif state == "failed":
                raise Exception(f"视频生成失败: {status.get('error')}")

            print(f"状态: {state}, 已等待 {int(time.time() - start_time)} 秒...")
            time.sleep(interval)

        raise TimeoutError("视频生成超时")

    def download_video(self, video_url, save_path):
        """下载生成的视频"""
        response = requests.get(video_url, stream=True)
        response.raise_for_status()

        with open(save_path, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)

        return save_path


# 使用示例
if __name__ == "__main__":
    # 初始化生成器
    generator = VeoVideoGenerator(api_key="YOUR_API_KEY")

    # 提交生成任务
    task = generator.generate_video(
        prompt="Cinematic aerial shot of a futuristic city at night, "
               "neon lights reflecting on wet streets, flying cars, "
               "cyberpunk atmosphere, high quality",
        duration_seconds=8,
        aspect_ratio="16:9",
        resolution="1080p",
        generate_audio=True
    )

    print(f"任务已提交, ID: {task['id']}")

    # 等待完成
    result = generator.wait_for_completion(task["id"])

    # 下载视频
    video_url = result["video_url"]
    generator.download_video(video_url, "output_video.mp4")

    print("视频已下载: output_video.mp4")

🚀 Getting Started: We recommend using the APIYI (apiyi.com) platform to quickly build your prototype. It offers out-of-the-box API interfaces so you can integrate everything without messing around with complex configurations.

Practical Alternatives for Veo 3.1 Parameter Limits

To address the parameter limits of Flow Reverse, here's a comprehensive summary of alternative solutions.

Alternatives Quick Reference Table

Front-end Prompt Optimization Template

# Universal Optimization Template
[Quality requirements], [Style definition], [Specific scene description].
[Action/Dynamics], [Lighting/Atmosphere].
NOT [Excluded element 1], NOT [Excluded element 2].

# Example: Product Showcase Video
"Professional commercial quality, clean minimalist style,
a sleek smartphone rotating on a white marble surface.
Smooth 360-degree rotation, soft studio lighting with subtle reflections.
NOT blurry, NOT cartoon, NOT low quality, NOT distorted."

# Example: Nature Landscape Video
"Cinematic documentary style, 8K quality,
a majestic waterfall in tropical rainforest at golden hour.
Slow motion water droplets, volumetric light rays through mist.
NOT artificial, NOT oversaturated, NOT CGI looking."

FAQ

Veo 3.1 Parameter Comparison Summary

After diving deep into this comparison, here's the bottom line:

Key Differences at a Glance

1. Duration Control: Vertex supports 4/6/8 seconds, while Flow is fixed at 8 seconds.
2. Negative Prompts: Vertex supports them directly; for Flow, you'll need to use specific prefix prompts instead.
3. Random Seed: Vertex supports it (though it's not perfectly deterministic), while Flow doesn't support it at all.
4. Audio Generation: Vertex is configurable, while Flow has it turned on by default.
5. Resolution: Vertex supports up to 4K, whereas Flow maxes out at 1080p.
6. Batch Generation: Vertex lets you generate 1-4 videos at once, while Flow is limited to 1.

Which One Should You Choose?

No matter which path you take, the APIYI (apiyi.com) platform makes it easy to integrate and switch between options flexibly. We'd suggest starting with some small-scale tests to see what works best for you before committing to a primary method.

References

1. Google Cloud – Veo 3.1 Official Documentation: Full API Parameter Guide

   • Link: docs.cloud.google.com/vertex-ai/generative-ai/docs/models/veo/3-1-generate

2. Google AI for Developers – Gemini API Video Generation: Veo 3.1 Usage Guide

   • Link: ai.google.dev/gemini-api/docs/video

3. Google Developers Blog – Introducing Veo 3.1: New Creative Capabilities

   • Link: developers.googleblog.com/introducing-veo-3-1-and-new-creative-capabilities-in-the-gemini-api

4. Vertex AI – Video Generation API Reference: Complete Parameter List

   • Link: docs.cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo-video-generation

This article was written by the APIYI technical team. For more AI video generation tutorials, please visit the APIYI Help Center: help.apiyi.com