Kling O1 — next-gen video generation with text-to-video, image-to-video, subject control and video-reference editing, 5–10s clips, std/pro modes.
kling/kling-video-o1 is a text-to-video generation model created by Kling, a division of Kuaishou Technology. It takes a natural language prompt as input and outputs a video file. The model is…
kling/kling-video-o1 generates short video clips from text prompts. The exact maximum duration and resolution are not specified in the provided facts; typical text-to-video models from Kling produce clips ranging from a few seconds to around 10 seconds, often in 720p or 1080p. The model can depict a wide variety of scenes, from realistic environments to stylized animations, depending on the prompt. It aims for high aesthetic alignment, meaning the output should look visually pleasing and consistent with the description. However, generation quality may vary based on prompt clarity and complexity.
The AA T2V Arena (Aesthetic Alignment Text-to-Video Arena) measures human preference for video quality and alignment with the prompt. kling/kling-video-o1 scored 1209.0, indicating strong performance in producing videos that raters found aesthetically appealing and semantically accurate. This suggests the model is well-suited for creative projects where visual quality is prioritized. Users can expect outputs that generally match the described content and look cohesive. The score also reflects competitiveness against other video models in the same benchmark.
If your use case does not require high aesthetic quality or if you are generating videos for internal testing where visual polish is not critical, a cheaper or faster video model might be more cost-effective. Additionally, if you need very long videos or specific resolutions not supported by kling/kling-video-o1, alternative models may offer those features at lower cost. On OrcaRouter, you can compare pricing across different video models to find the best fit. Always evaluate the trade-off between quality and price for your specific volume and requirements.
As with most text-to-video models, performance degrades with extremely abstract, highly detailed, or contradictory prompts. kling/kling-video-o1 is optimized for clear, descriptive natural language. For prompts that specify multiple characters, actions, and scene changes, the model may simplify or drop elements. It works best when the prompt describes a single coherent scene or action. For sequential storytelling, multiple generations may be needed. No specific prompt engineering guidelines are provided, but users should use concise, concrete language for best results.
AA T2V Arena stands for Aesthetic Alignment Text-to-Video Arena. It is a crowd-sourced benchmark where human raters compare two generated videos from the same prompt and choose the one they prefer based on overall quality and alignment with the text. The score of 1209.0 for kling/kling-video-o1 is an Elo-like rating derived from these pairwise comparisons. This score indicates the model's relative standing; higher is better for human preference. The benchmark focuses on visual appeal and prompt fidelity, not on metrics like FID or CLIP score.
The score 1209.0 is the result of submitting kling/kling-video-o1's outputs to the AA T2V Arena, where it competed against other text-to-video models in blind comparisons. Each generated video was evaluated by human raters for aesthetic quality and how well it matched the prompt. The Elo rating system was used to calculate the final score. This means the model performed well in head-to-head battles, winning more often than losing against a set of competitor models. For context, scores typically range from 1000 to 1500; 1209 places it above average.
Only one benchmark score is provided: 1209.0 on AA T2V Arena. There is no information about inference speed, latency, video length limits, resolution, or batch processing capabilities. Users should note that benchmark scores do not guarantee performance on all prompts or production workloads. The model may have biases, artifacts, or failure modes common to text-to-video models (e.g., flickering, unrealistic motion, object morphing). Without additional facts, specific limitations cannot be enumerated. Always test with your own prompts before deploying at scale.
No specific inference speed or latency figures are provided for kling/kling-video-o1. Text-to-video generation typically takes longer than image generation due to the temporal dimension. Actual performance depends on video length, resolution, complexity of the scene, and the underlying hardware used by Kling. On OrcaRouter, the API may have additional network latency. To understand typical response times, users are advised to run benchmark tests with representative prompts under expected load conditions. Contact OrcaRouter support for service-level guidance if needed.
Exact pricing per video generation is not provided in the given facts. OrcaRouter charges based on the provider's rate, which can be per second of video, per generation request, or per compute time. Typically, higher-quality models cost more. Users should visit OrcaRouter's pricing page or API documentation for the current cost of the "kling/kling-video-o1" model. Because pricing may change, always verify before building cost-sensitive applications. There is no indication of free usage or credits.
No volume discount information is provided. OrcaRouter may offer tiered pricing or prepaid packages for API access, but this is not specific to kling/kling-video-o1. For high-frequency users, it is advisable to contact OrcaRouter directly to discuss custom pricing. Additionally, if your use case allows lower-quality outputs, consider using a cheaper model on OrcaRouter to reduce costs. The provider (Kling) may also have its own pricing agreements that affect what OrcaRouter charges.
Caching is not mentioned in the provided facts for kling/kling-video-o1. Typically, text-to-video models generate unique outputs per prompt due to stochastic sampling, making cache hits unlikely unless the exact same prompt and seed are reused. OrcaRouter may cache frequently requested generations, but no information is available. For cost management, assume each generation incurs a separate charge. To avoid redundant costs, design your application to reuse generated videos when possible, rather than regenerating identical prompts.
Use the OpenAI-compatible chat completions or dedicated video generation endpoint. The base URL is https://api.orcarouter.ai/v1. Set the model parameter to "kling/kling-video-o1". Authentication is required, typically via an API key in the Authorization header. For a text-to-video generation, you likely send a JSON payload with a prompt field. Exact request format may vary; consult OrcaRouter's documentation for the specific schema. A minimal curl example: curl https://api.orcarouter.ai/v1/video/generations \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"kling/kling-video-o1","prompt":"A cat walking on a beach at sunset"}'
Available parameters are not fully documented in the provided facts. Common parameters for text-to-video models include prompt (required), negative_prompt, num_frames, width, height, guidance_scale, seed, and num_inference_steps. kling/kling-video-o1 likely supports a subset of these. Since OrcaRouter abstracts the provider, some parameters may be transformed or limited. Start with the documented Kling parameters and test for compatibility. Always check for error messages if a parameter is unsupported. Default values will be applied for missing parameters.
If you are already using OrcaRouter's OpenAI-compatible API with a different video model, migration is straightforward: change the model field from the old ID to "kling/kling-video-o1". Ensure your prompt format is compatible. If you were using a different API (e.g., direct Kling API), rewrite requests to match OrcaRouter's schema. No inference code changes are necessary on the client side if you stick to OpenAI's standard format. Test with single requests before switching production traffic. Note that output quality and latency may differ.
OrcaRouter uses API key authentication. You must include your API key in the Authorization header as "Bearer YOUR_API_KEY". API keys are typically generated from the OrcaRouter dashboard. Keep them secret. For security, avoid hardcoding keys in source code; use environment variables. If you need to rotate keys, update your configuration accordingly. OrcaRouter may also support other authentication methods like OAuth, but the provided facts do not specify. Follow best practices for secure API access.
Kling has released multiple versions, including Kling 1.0, 1.5, and various variants like Kling-video-o1. The AA T2V Arena score of 1209.0 suggests this model performs well in aesthetic alignment. Without scores for other Kling models, direct comparison is not possible. Generally, newer models improve on quality and fidelity. The "o1" designation might indicate improved reasoning or planning for complex prompts, but this is speculative. Users should compare outputs on their own prompts to decide which Kling model best fits their needs.
OrcaRouter offers access to multiple video generation models from different providers. kling/kling-video-o1's benchmark score of 1209.0 on AA T2V Arena places it in a competitive range. Other models like Stable Video Diffusion, Pika, or RunwayML may have different strengths (e.g., speed, control, realism). Since no direct cross-model benchmark is provided, users should evaluate based on their specific requirements: prompt adherence, visual style, generation time, and cost. OrcaRouter's unified API makes it easy to test multiple models by simply changing the model ID.
Cheaper video models may have lower aesthetic quality, shorter video lengths, or more artifacts. The AA T2V Arena score suggests kling/kling-video-o1 delivers high-quality results, which justifies a higher price for users who prioritize visual appeal. If your project tolerates lower quality or is for internal prototyping, a cheaper model can reduce costs. On OrcaRouter, you can compare generated samples side-by-side to quantify the difference. Also, consider inference speed: more expensive models may also take longer to generate. Decision should balance budget, quality, and throughput.
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="kling/kling-video-o1",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)| Per request | $0.0840 |
| Currency | USD |
| Flat fee per API call (image generation models) | |
GET /api/public/models/kling/kling-video-o1Open @misc{orcarouter_kling_video_o1,
title = {kling/kling-video-o1 API},
author = {kling},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/kling/kling-video-o1}
}kling. (n.d.). kling/kling-video-o1 API. OrcaRouter. https://www.orcarouter.ai/models/kling/kling-video-o1