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Veo 3.1

每次請求:$0.4
Veo 3.1 是 Google 對其 Veo 文本與圖像→影片系列的一次漸進但意義重大的更新,新增更豐富的原生音訊、更長且更可控的影片輸出,以及更精細的編輯與場景層級控制。
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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

探索 Veo 3.1 的核心功能,專為提升效能和可用性而設計。了解這些功能如何為您的專案帶來效益並改善使用者體驗。

Veo 3.1 的定價

探索 Veo 3.1 的競爭性定價,專為滿足各種預算和使用需求而設計。我們靈活的方案確保您只需為實際使用量付費,讓您能夠隨著需求增長輕鬆擴展。了解 Veo 3.1 如何在保持成本可控的同時提升您的專案效果。
彗星價格 (USD / M Tokens)官方價格 (USD / M Tokens)折扣
每次請求:$0.4
每次請求:$0.5
-20%

Veo 3.1 的範例程式碼和 API

存取完整的範例程式碼和 API 資源,以簡化您的 Veo 3.1 整合流程。我們詳盡的文件提供逐步指引,協助您在專案中充分發揮 Veo 3.1 的潛力。
Python
JavaScript
Curl
import os
import time
import requests

# 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/veo/v1/video"

# Create video generation task
create_response = requests.post(
    f"{BASE_URL}/create",
    headers={
        "Authorization": COMETAPI_KEY,
        "Content-Type": "application/json",
    },
    json={
        "prompt": "An orange cat flying in the blue sky with white clouds, sunlight pouring onto its fur, creating a beautiful and dreamlike scene",
        "model": "veo3.1",
        "enhance_prompt": True,
    },
)

task = create_response.json()
task_id = task["id"]
print(f"Task created: {task_id}")
print(f"Status: {task['status']}")

# Poll until video is ready
while True:
    query_response = requests.get(
        f"{BASE_URL}/query/{task_id}",
        headers={
            "Authorization": f"Bearer {COMETAPI_KEY}",
        },
    )

    result = query_response.json()
    status = result["data"]["status"]
    progress = result["data"].get("progress", "")

    print(f"Checking status... {status} {progress}")

    if status == "SUCCESS" or result["data"]["data"]["status"] == "completed":
        video_url = result["data"]["data"]["video_url"]
        print(f"
Video URL: {video_url}")
        break
    elif status == "FAILED":
        print(f"Failed: {result['data'].get('fail_reason', 'Unknown error')}")
        break

    time.sleep(10)

Veo 3.1的版本

Veo 3.1擁有多個快照的原因可能包括:更新後輸出結果存在差異需保留舊版快照以確保一致性、為開發者提供適應與遷移的過渡期,以及不同快照對應全球或區域端點以優化使用者體驗等潛在因素。各版本間的具體差異請參閱官方文件說明。
version
veo3.1
veo3.1-pro

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