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o1 API: Endpoint & Pricing

Deprecated 23 Oct 2026

Video O1 by Kling is a unified multimodal AI video model that can generate and edit cinematic clips from text, image, and video inputs within a single system.

Compare Video O1 alternatives (29) →
Start Using API

What is Video O1?

Video O1 is Kling’s unified multimodal video generation and editing model that treats text, images, and videos as joint inputs in a single creative engine. It is mainly used for text-to-video and image-to-video creation of short cinematic clips, and for advanced editing tasks like style transfer, shot extension, and start/end frame control while preserving character and scene consistency. It also underpins reference-based workflows where users upload example images or clips and then direct the final video with natural language prompts, enabling professional-grade content production. Video O1 belongs to the Kling O1 family from Kuaishou/Kling AI, succeeding earlier Kling video models such as Kling 1.x and extending them into a unified multimodal foundation model.

Can you self-host Video O1?

No. We found no public weights for Video O1 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.

Get API keySee API code ↓

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

List price by provider

InputOutput
openai$15 in
$60 out

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

Provider Input Output Cache read /M Latency Throughput Uptime
openaiin $15; out $60 per 1M tokens200K tokensvision, streaming, reasoning, JSON, structured

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

Try this model

Test Video O1 right here — free to start.

Video O1
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

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 o1 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": "o1",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ],
  "reasoning_effort": "none"
  }'
from openai import OpenAI

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

resp = client.chat.completions.create(
    model="o1",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
    reasoning_effort="none",
)
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: "o1",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);
stream = client.chat.completions.create(
    model="o1",
    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": "o1",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ],
  "reasoning_effort": "none"
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID o1 · 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 modeSupportedresponse_format: {"type": "json_object"}
Structured outputsSupportedresponse_format: {"type": "json_schema", …}
StreamingSupportedstream: true
Vision (image input)Supportedimage_url content parts
Web searchNot supportedweb_search
Reasoning effortnone · low · medium · highreasoning_effort: "none"

ERRORS

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.

o1 in production

What the model is good at.

  • Multimodal Inputs

    Accepts mixed text, image, and video inputs in a single request for unified generation and editing workflows.

  • Video Generation

    Generates short, high-fidelity clips from prompts, including text-to-video, image-to-video, and reference-to-video creation.

  • Video Editing

    Edits existing footage with operations like transformation, restyling, inpainting, start–end frame interpolation, and extension.

  • Semantic Understanding

    Uses deep semantic understanding to perform context-aware edits, subject replacement, and consistent narrative-level changes.

  • Identity Consistency

    Maintains consistent characters, props, and scenes across shots using multi-image or element references for continuity.

What you get when you call o1 through LLM.API.

  • Unified AI Routing

    Automatically route each request to the best model across providers based on latency, price, and quality—no code changes when vendors, versions, or specs change.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with built-in price awareness, per-project budgets, and smart model selection so you ship fast without surprise overages or manual cost tuning.

    Optimize for price, safely.

  • Automatic Fallbacks

    Define fallback chains once and let LLM.API recover from provider outages, timeouts, or quota errors while preserving SLAs and user experience.

    Resilient by default.

  • Deep Observability

    Get full visibility into every request—latency, tokens, errors, and model choices—plus searchable traces to debug prompts and regressions in production.

    See every token, trace every call.

  • Task-Level Abstractions

    Describe tasks like chat, generation, tools, or RAG once and run them on any compatible model, decoupling your app logic from vendor-specific APIs.

    Code to tasks, not models.

  • High-Throughput Batch

    Run massive offline or background workloads via a single batch API with automatic chunking, retries, and progress tracking across providers.

    Scale batch without glue code.

When to Use — When NOT to Use

Use it if...

  • You need to generate short, visually rich marketing videos from scripts or storyboards.
  • You need AI-produced demo clips to showcase product features or UI flows.
  • Your use case involves social media content creation that benefits from cinematic visuals.
  • Your use case involves turning static images or posters into engaging motion graphics videos.
  • You need rapid video prototyping to pitch concepts before investing in full production.
  • Your use case involves creative experimentation with AI video styles, transitions, and compositions.

Avoid if...

  • You need strict control over every frame like traditional video editing or compositing.
  • Your workload requires deterministic outputs with stable, reproducible frames across multiple generations.
  • You need guaranteed license terms suitable for sensitive broadcast or major studio releases.
  • Your workload requires low-latency, real-time video generation or live interactive rendering.
  • You need precise, frame-accurate compliance with brand guidelines and regulatory visual standards.
  • Your workload requires robust on-premise deployment instead of cloud-based video generation services.

LLM API rating for o1

★ 3.2/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis
4.2Production adoptionWidely used reasoning model
2.4Benchmark qualityArtificial Analysis Intelligence Index 15.2 · LMArena text rank #183, Elo 1366
1.9Developer ecosystemContext window, tool and JSON support, price against the category median
Reasoning & chat 3.2Coding 3.2Vision & multimodal 3.5Long context 3.6
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.

COMMUNITY

What developers say about Video O1

LMArena#183 · Elo 1366Arena coding#202 · Elo 1367Speed—Sources: LMArena, Artificial Analysis

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

  • Developers on LinkedIn report that Video O1 shows strong potential for reusing characters across productions, achieving approximately 90% satisfaction in early testing for character consistency and filmmaking workflows.
  • A developer on Hacker News noted that while the model handles multi-reference composition well, visual artifacts like distorted hands and faces remain visible in complex scenes.
  • Early testers on community forums praise the model's 'temporal consistency,' stating it is a significant step up from older models that produced disjointed motion or static imagery.

SOURCES

Frequently Asked Questions

  • What is Video O1?

    Video O1 is a Kling video generation model accessible through LLM.API for turning text prompts into high-quality video clips.

  • What modalities does Video O1 support?

    Video O1 supports text-to-video generation, producing short video clips from natural language prompts via the LLM.API interface.

  • How is Video O1 priced on LLM.API?

    Video O1 usage on LLM.API is billed per generated video according to LLM.API’s Kling-specific pricing tier shown in your dashboard.

  • What is the maximum video length or context for Video O1?

    Video O1 supports short-form clips with a fixed maximum duration defined by LLM.API’s Kling integration documentation, not a token-based context window.

  • How fast is Video O1 in terms of latency?

    Video O1 typically has higher latency than text models, with generation time depending on clip length, resolution, and current Kling backend load.

  • How do I call Video O1 through LLM.API?

    You call Video O1 by specifying the Kling provider and Video O1 model name in the LLM.API video generation endpoint with your text prompt.

  • How does Video O1 compare to other video models on LLM.API?

    Compared to other video models, Video O1 focuses on high-fidelity, prompt-aligned clips, while capabilities and performance vary by alternative providers.

  • Does Video O1 support audio generation with the video?

    Video O1’s audio support, if available, is defined by Kling and documented in LLM.API’s capabilities matrix for this model.

  • What are the main limitations of Video O1?

    Video O1 may struggle with very long narratives, precise text rendering, or complex multi-shot storytelling within a single generated clip.

  • Are there safety or content restrictions when using Video O1?

    Yes, Video O1 requests are filtered by Kling and LLM.API safety policies, which restrict disallowed or sensitive video content.

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