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Veo 3.1

每次請求:$0.4
Veo 3.1 是 Google 针对其 Veo 文本与图像→视频系列的渐进但意义重大的更新，新增更丰富的原生音频、更长且可控性更高的视频输出，以及更精细的编辑与场景级控制。
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Core features

Veo 3.1 focuses on practical content creation features:

  • Native audio generation (dialogue, ambient sound, SFX) integrated in outputs. Veo 3.1 generates native audio (dialogue + ambience + SFX) aligned to the visual timeline; the model aims to preserve lip sync and audio–visual alignment for dialogue and scene cues.
  • Longer outputs (support for up to ~60 seconds / 1080p versus Veo 3’s very short clips,8s), and multi-prompt multi-shot sequences for narrative continuity.
  • Scene Extension and First/Last Frame modes that extend or interpolate footage between key frames.
  • Object insertion and (coming) object removal and editing primitives inside Flow.

Each bullet above is designed to reduce manual VFX work: audio and scene continuity are now first-class outputs rather than afterthoughts.

Technical details (model behavior & inputs)

Model family & variants: Veo belongs to Google’s Veo-3 family; the preview model ID is typically veo3.1-pro; veo3.1 (CometAPI doc). It accepts text prompts, image references (single frame or sequences), and structured multi-prompt layouts for multi-shot generation.

Resolution & duration: Preview documentation describes outputs at 720p/1080p with options for longer durations (up to ~60s in certain preview settings) and higher fidelity than earlier Veo variants.

Aspect ratios: 16:9 (supported) and 9:16 (supported except in some reference-image flows).

Prompt language: English (preview).

API limits: typical preview limits include max 10 API requests/min per project, max 4 videos per request, and video lengths selectable among 4, 6, or 8 seconds (reference-image flows support 8s).

Benchmark performance

Google’s internal and publicly summarized evaluations report strong preference for Veo 3.1 outputs across human rater comparisons on metrics such as text alignment, visual quality, and audio–visual coherence (text→video and image→video tasks).

Veo 3.1 achieved state-of-the-art results on internal human-rater comparisons across several objective axes — overall preference, prompt alignment (text→video and image→video), visual quality, audio-video alignment, and “visually realistic physics” on benchmark datasets such as MovieGenBench and VBench.

Limitations & safety considerations

Limitations:

  • Artifacts & inconsistency: despite improvements, certain lighting, fine-grained physics, and complex occlusions can still yield artifacts; image→video consistency (especially over long durations) is improved but not perfect.
  • Misinformation / deepfake risk: richer audio + object insertion/removal increases misuse risk (realistic fake audio and extended clips). Google notes mitigations (policy, safeguards) and earlier Veo launches referenced watermarking/SynthID to aid provenance; however technical safeguards do not eliminate misuse risk.
  • Cost & throughput constraints: high-resolution, long videos are computationally expensive and currently gated in a paid preview—expect higher latency and cost compared with image models. Community posts and Google forum threads discuss availability windows and fallback strategies.

Safety controls: Veo3.1 has integrated content policies, watermarking/synthID signaling in earlier Veo releases, and preview access controls; customers are advised to follow platform policy and implement human review for high-risk outputs.

Practical use cases

  • Rapid prototyping for creatives: storyboards → multi-shot clips and animatics with native dialogue for early creative review.
  • Marketing & short form content: 15–60s product spots, social clips, and concept teasers where speed matters more than perfect photorealism.
  • Image→video adaptation: turning illustrations, characters, or two frames into smooth transitions or animated scenes via First/Last Frame and Scene Extension.
  • Tooling augmentation: integrated into Flow for iterative editing (object insertion/removal, lighting presets) that reduces manual VFX passes.

Comparison with other leading models

Veo 3.1 vs Veo 3 (predecessor): Veo 3.1 focuses on improved prompt adherence, audio quality, and multi-shot consistency — incremental but impactful updates aimed at reducing artifacts and improving editability.

Veo 3.1 vs OpenAI Sora 2: tradeoffs reported in press: Veo 3.1 emphasizes longer-form narrative control, integrated audio, and Flow editing integration; Sora 2 (when compared in press) focuses on different strengths (speed, different editing pipelines). TechRadar and other outlets frame Veo 3.1 as Google’s targeted competitor to Sora 2 for narrative and longer video support. Independent side-by-side testing remains limited.

Veo 3.1 的功能

了解 Veo 3.1 的核心能力，帮助提升性能与可用性，并改善整体体验。

Veo 3.1 的定价

查看 Veo 3.1 的竞争性定价，满足不同预算与使用需求，灵活方案确保随需求扩展。

veo3.1（videos）

Model nameTagsCalculate price
veo3.1-allvideos$0.20000
veo3.1videos$0.40000

Veo 3.1 的示例代码与 API

获取完整示例代码与 API 资源，简化 Veo 3.1 的集成流程，我们提供逐步指导，助你发挥模型潜能。
Python
JavaScript
Curl
import os
import requests
import json

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

headers = {
    "Authorization": COMETAPI_KEY,
}

# ============================================================
# Step 1: Download Reference Image
# ============================================================
print("Step 1: Downloading reference image...")

image_url = "https://images.unsplash.com/photo-1506905925346-21bda4d32df4?w=1280"
image_response = requests.get(image_url)
image_path = "/tmp/veo3.1_reference.jpg"
with open(image_path, "wb") as f:
    f.write(image_response.content)
print(f"Reference image saved to: {image_path}")

# ============================================================
# Step 2: Create Video Generation Task (form-data with image upload)
# ============================================================
print("
Step 2: Creating video generation task...")

with open(image_path, "rb") as image_file:
    files = {
        "input_reference": ("reference.jpg", image_file, "image/jpeg"),
    }
    data = {
        "prompt": "A breathtaking mountain landscape with clouds flowing through valleys, cinematic aerial shot",
        "model": "veo3.1",
        "size": "16x9",
    }
    create_response = requests.post(
        f"{BASE_URL}/videos", headers=headers, data=data, files=files
    )

create_result = create_response.json()
print("Create response:", json.dumps(create_result, indent=2))

task_id = create_result.get("id")
if not task_id:
    print("Error: Failed to get task_id from response")
    exit(1)
print(f"Task ID: {task_id}")

# ============================================================
# Step 3: Query Task Status
# ============================================================
print("
Step 3: Querying task status...")

query_response = requests.get(f"{BASE_URL}/videos/{task_id}", headers=headers)
query_result = query_response.json()
print("Query response:", json.dumps(query_result, indent=2))

task_status = query_result.get("data", {}).get("status")
print(f"Task status: {task_status}")

Veo 3.1 的版本

Veo 3.1 可能存在多个快照，原因包括：更新后保持一致性需要保留旧版、给开发者留出迁移窗口，以及全球/区域端点提供的优化差异。具体差异请参考官方文档。
Model iddescriptionAvailabilityPriceRequst
veo3.1-allThe technology used is unofficial and the generation is unstable etc✅$0.2 / perChat format
veo3.1Recommend, Pointing to the latest model✅$0.4/ perAsync Generation

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