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Kling V3 Standard Image-to-Video API: Endpoint & Pricing

Create short generative video with Kling V3 Standard Image-to-Video on LLM.API, with resolution-aware pricing and a unified developer workflow.

What is Kling V3 Standard Image-to-Video?

Kling V3 Standard Image-to-Video targets motion content rather than chat. Video generation model by Muapi. On LLM.API it sits alongside other media models under a single developer account.


Can you self-host Kling V3 Standard Image-to-Video?

No. We found no public weights for Kling V3 Standard Image-to-Video on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.

Providers

LLM.API routes Kling V3 Standard Image-to-Video to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput
muapi$0 in
$0.0001 out

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

ProviderPricingContextCapabilities
muapiin —; out $0.0001 per second of video——

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test Kling V3 Standard Image-to-Video right here — free to start.

Kling V3 Standard Image-to-Video
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Kling V3 Standard Image-to-Video through the OpenAI-compatible API — Video generation via LLM.API (see docs for media endpoints).

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to kling-v3-std-i2v and the call runs unchanged.

bash
curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kling-v3-std-i2v",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'
from openai import OpenAI

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

resp = client.chat.completions.create(
    model="kling-v3-std-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)
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "kling-v3-std-i2v",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);
stream = client.chat.completions.create(
    model="kling-v3-std-i2v",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)
{
  "model": "kling-v3-std-i2v",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID kling-v3-std-i2v · OpenAI-compatible.

  • POST /v1/chat/completionsChat Completions — supported
FeatureStatusHow to set it
Function calling (tools)Not supportedtools, tool_choice
Parallel tool callsNot supportedparallel_tool_calls
JSON modeNot supportedresponse_format: {"type": "json_object"}
Structured outputsNot supportedresponse_format: {"type": "json_schema", …}
StreamingNot supportedstream: true
Vision (image input)Not supportedimage_url content parts
Web searchNot supportedweb_search

Errors & fallback routing

CodeWhat it meansWhat to do
401Missing or invalid API key.Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active.
429Rate limited, or the upstream provider is throttling the request.Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
5xxUpstream provider error or timeout.Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Kling V3 Standard Image-to-Video in production

What the model is good at.

  • Resolution options

    Supports multiple output resolutions depending on provider pricing tiers. Relevant for `kling-v3-std-i2v` workloads on LLM.API.

  • Product visualization

    Helps visualize products and environments before shoot or 3D work. Reflects video generation positioning for this endpoint.

  • Cinematic prompting

    Responds to camera, lighting, and mood language in creative briefs. Reflects video generation positioning for this endpoint.

  • Short-form content

    Fits vertical and horizontal short-form marketing formats. Relevant for `kling-v3-std-i2v` workloads on LLM.API.

  • Iterative creatives

    Enables prompt-based A/B exploration of motion concepts. Grounded in the model's video role rather than generic chat claims.

What you get when you call Kling V3 Standard Image-to-Video through LLM.API.

  • Unified AI Routing

    Reach Kling V3 Standard Image-to-Video and sibling models through one OpenAI-compatible endpoint.

    One endpoint, every model.
  • Cost Control

    Compare provider price points and keep spend visible as you scale Kling V3 Standard Image-to-Video.

    More performance, less spend.
  • Reliability Layer

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

    Never go dark on users.
  • Observability

    Practical: Trace prompts, tokens, and errors for Kling V3 Standard Image-to-Video alongside the rest of your stack.

    See every token, everywhere.
  • Drop-in SDKs

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

    Think tasks, not models.
  • Model Breadth

    Production: Swap Kling V3 Standard Image-to-Video for chat, media, or embedding alternatives without rewriting auth.

    Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

  • You are building short-form generative video features (Kling V3 Standard Image-to-Video)
  • You can tolerate longer generation times than chat (Kling V3 Standard Image-to-Video)
  • You need resolution-tiered pricing for motion content (Kling V3 Standard Image-to-Video)

Avoid if...

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

LLM API rating for Kling V3 Standard Image-to-Video

★ 2.6/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value
Estimated from published specs
2.5Production adoptionNot yet tracked — no public usage data for this release
3.1Benchmark qualityScored on multimodal input support and context
2.0Developer ecosystemContext window, tool and JSON support, price against the category median
Vision & multimodal 2.6
How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

  1. Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
  2. Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
  3. Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

What creators say about AI video models

LMArena—Speed—Sources: LMArena, Artificial Analysis

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 Kling V3 Standard 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.

What developers say about Kling V3 Standard Image-to-Video

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

  • Developers on Eachlabs and fal.ai note that Kling V3 Standard is a cost-efficient tier suitable for high-volume workflows, rapid prototyping, and character-driven animations compared to the V3 Pro version.
  • Developers on VeniceStats report that the model is best suited for animating still images into short clips where natural human motion and a cinematic look are prioritized over maximum resolution.
  • Technical documentation from various platforms highlights the model's ability to maintain temporal consistency and storyboard control, including support for native audio generation and complex camera movements.

Frequently Asked Questions

  • What modalities does Kling V3 Standard Image-to-Video support?

    Kling V3 Standard Image-to-Video accepts text, image and produces video according to its architecture metadata on LLM.API.

  • What is Kling V3 Standard Image-to-Video?

    Video generation model by Muapi. On LLM.API it is addressed as `kling-v3-std-i2v`.

  • How long do Kling V3 Standard Image-to-Video jobs take?

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

  • What are limitations of Kling V3 Standard Image-to-Video?

    Like other API models, Kling V3 Standard 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.

  • Is Kling V3 Standard Image-to-Video a chat model?

    No—Kling V3 Standard Image-to-Video is categorized as a video model. Use the matching API surface rather than assuming chat completions.

  • Which providers serve Kling V3 Standard Image-to-Video?

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

  • What is the context length for Kling V3 Standard Image-to-Video?

    Reported context for Kling V3 Standard Image-to-Video is See provider specs. Always verify the active provider row if multiple providers are listed.

  • Does Kling V3 Standard Image-to-Video support streaming?

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

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