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Veo 3 Image-to-Video

Create short generative video with Veo 3 Image-to-Video on LLM.API, with resolution-aware pricing and a unified developer workflow.

Start Using API

What is Veo 3 Image-to-Video?

Veo 3 Image-to-Video is a generative video model available through LLM.API as `veo3-i2v`. Video generation model by Muapi. Expect text, image prompts and video outputs, with pricing often tied to seconds and resolution.

Providers

LLM.API routes Veo 3 Image-to-Video to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput
muapi$0 in
$0 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
muapi30% offin —; out $0.313 per second of video——

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test Veo 3 Image-to-Video right here — free to start.

Veo 3 Image-to-Video
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Veo 3 Image-to-Video through the OpenAI-compatible API — Video generation via LLM.API (see docs for media endpoints).

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="veo3-i2v",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)
{
  "model": "veo3-i2v",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Motion coherence

    Targets temporally consistent motion across frames for the requested scene. Reflects video generation positioning for this endpoint.

  • Product visualization

    Helps visualize products and environments before shoot or 3D work. Tuned to how teams typically call Veo 3 Image-to-Video.

  • Text-to-video generation

    Synthesizes short video clips from prompts for ads, explainers, and social. Reflects video generation positioning for this endpoint.

  • Cinematic prompting

    Responds to camera, lighting, and mood language in creative briefs. Tuned to how teams typically call Veo 3 Image-to-Video.

  • Iterative creatives

    Enables prompt-based A/B exploration of motion concepts. Relevant for `veo3-i2v` workloads on LLM.API.

6 Most Valuable Use Cases

  • Social-first product teaser generation with Veo 3 Image-to-Video
  • Short promotional clips from written briefs
  • Internal pitch videos for stakeholders with Veo 3 Image-to-Video
  • Localized motion variants of the same concept
  • UGC-style drafts for performance marketing with Veo 3 Image-to-Video
  • Scene tests for world and environment design

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run Veo 3 Image-to-Video on LLM.API?

  • Unified AI Routing

    Reach Veo 3 Image-to-Video and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Veo 3 Image-to-Video.

  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for Veo 3 Image-to-Video alongside the rest of your stack.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap Veo 3 Image-to-Video for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • You need resolution-tiered pricing for motion content (Veo 3 Image-to-Video)
  • You are building short-form generative video features (Veo 3 Image-to-Video)
  • You can tolerate longer generation times than chat (Veo 3 Image-to-Video)

Avoid if...

  • You need broadcast-length films or guaranteed cinema-grade continuity
  • You only need still images or chat text
  • You require sub-second interactive generation

COMMUNITY

What creators say about AI video models

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Veo 3 Image-to-Video.

  • Creator reviews of the latest Kling releases praise multi-shot consistency, native audio and genuine 4K output.
  • Comparisons with Sora and Runway focus on prompt adherence and motion stability rather than raw resolution.
  • The common workflow advice: generate many short takes cheaply, then upscale or extend the ones that land.

SOURCES

Frequently Asked Questions

  • When should I choose Veo 3 Image-to-Video?

    You can tolerate longer generation times than chat — especially when you specifically need Veo 3 Image-to-Video.

  • What are limitations of Veo 3 Image-to-Video?

    Like other API models, Veo 3 Image-to-Video can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • How long do Veo 3 Image-to-Video jobs take?

    Video generation typically takes longer than chat; latency scales with duration and resolution and may vary by queue.

  • What is Veo 3 Image-to-Video?

    Video generation model by Muapi. On LLM.API it is addressed as `veo3-i2v`.

  • Which providers serve Veo 3 Image-to-Video?

    LLM.API currently lists: muapi. Availability can vary by region and account.

  • How is Veo 3 Image-to-Video priced on LLM.API?

    Listed pricing metadata shows: $0.312/sec. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • Does Veo 3 Image-to-Video support streaming?

    Streaming depends on the active provider; check the providers table on this page for flags.

  • How do I call Veo 3 Image-to-Video via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "veo3-i2v" and your LLM.API key. See the code snippet on this page.

  • What is the context length for Veo 3 Image-to-Video?

    Reported context for Veo 3 Image-to-Video is See provider specs. Always verify the active provider row if multiple providers are listed.

  • Can I use tools or structured outputs with Veo 3 Image-to-Video?

    Tooling support varies; for pure video models, prefer the modalities listed rather than assuming chat tools.

  • Where is the canonical page for Veo 3 Image-to-Video?

    https://llmapi.ai/models/openai-veo3-i2v/

  • Is Veo 3 Image-to-Video a chat model?

    No—Veo 3 Image-to-Video is categorized as a video model. Use the matching API surface rather than assuming chat completions.

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