Kling 2.1 Master — premium text-to-video and image-to-video, 5–10s clips, 1080p, 24fps.
Kling/kling-v2-1-master is a specific model version from Kling designed for image-to-video generation. It takes a source image and optionally a text prompt to produce a short video that continues the…
The model's core capability is generating a short video from a static image, maintaining the scene's aesthetics and adding plausible motion such as camera pans, object movement, or atmospheric effects. It can handle a variety of image types, including photographs, digital art, and rendered frames. The output video typically lasts a few seconds and loops seamlessly. The model also attempts to respect any text prompt provided, so users can influence the style of motion or additional elements. It does not support text-to-video generation from scratch; it requires an initial image as the seed.
Like most public models, kling-v2-1-master likely includes safety filters to prevent generation of harmful or illegal content. Specific details on prohibited categories are not provided in the available facts, but typical restrictions include nudity, violence, and copyrighted material. The model provider (Kling) and platform (OrcaRouter) may enforce usage policies. Users should review terms of service and ensure their inputs comply. If a request is blocked, the API returns a standard error response. For sensitive applications, consider testing with allowed content first.
While kling-v2-1-master offers high benchmark scores, it may be overkill for simple or low-resolution outputs. If your use case only requires fast generation without high fidelity, a lighter model (e.g., Kling's earlier versions or other providers on OrcaRouter) might suffice at lower cost and latency. This model is best for projects where quality is the primary factor. Also, if you need real-time performance (e.g., for interactive apps), the inference time of this advanced model may not be suitable. Always profile the model's latency with representative inputs before integrating into production.
Based on the model's design for image-to-video, it can produce various motion types including camera movement (zoom, pan, tilt), object animation (e.g., a person walking, water flowing), and subtle atmospheric changes (clouds moving, lighting shifts). The exact range depends on the training data. Users should experiment with different prompts to modulate the motion. The model struggles with highly complex physics or rapid scene changes. It performs best with images that have clear foreground/background separation and moderate detail.
The AA I2V Arena (Image-to-Video Arena) is a benchmark that ranks models based on human evaluations of generated video quality. A score of 1203.0 indicates that kling-v2-1-master outperforms the baseline by a significant margin. The exact evaluation methodology involves pairwise comparisons: raters choose which of two videos better matches the input image and exhibits natural motion. A score above 1000 indicates better-than-average performance. This suggests that kling-v2-1-master produces videos that are convincing and faithful to the source.
The AA I2V Arena leaderboard includes models from various providers like Runway, Pika, and Stability AI. With a score of 1203.0, kling-v2-1-master sits near the top. Specific rank and comparisons are not provided in the available facts, but this score implies it is competitive with leading commercial models. Users looking for the highest quality image-to-video generation should consider this model. However, benchmark results may not reflect performance on all image types; testing on domain-specific content is recommended.
No formal limitations are documented in the provided facts. However, as a neural network model, kling-v2-1-master may exhibit common weaknesses: difficulty generating coherent motion for highly abstract or cluttered images, occasional artifacts like flickering or warping, and limited video duration (typically a few seconds). It may also struggle with consistent character identities if multiple similar objects are present. The model's performance on non-photorealistic styles (cartoons, paintings) may vary. Users should be aware that high-quality results often require careful prompt engineering and multiple attempts.
Inference speed is not specified in the available facts. For advanced image-to-video models, generation typically takes tens of seconds to a few minutes, depending on compute resources, image resolution, and desired video length. When using OrcaRouter's API, exact latency will depend on backend load and model version. For production planning, it is advisable to measure latency with typical inputs. Faster models exist, but they may sacrifice quality. If speed is critical, consider models with lower benchmark scores but quicker inference.
No specific pricing information is provided in the available facts. OrcaRouter's pricing model typically charges per API call based on input and output tokens or generation units. For video models, costs may be higher than text models due to resource intensity. To get current pricing, consult OrcaRouter's official documentation or contact their sales team. It is also possible that Kling applies its own usage fees through the API. Always verify costs before scaling usage.
The available facts do not mention caching or batching options for this model. However, OrcaRouter may offer prompt caching or repeated use discounts for high-volume customers. For image-to-video generation, batching is unlikely because each request has different image inputs. The most effective cost-saving strategy is to reduce output quality parameters (if supported) or to use a cheaper model for less critical tasks. Check OrcaRouter's documentation for any available optimization features.
Without specific pricing data, a direct comparison cannot be made. Generally, higher-performing models like kling-v2-1-master tend to cost more per generation due to larger model size and increased compute requirement. Alternative models may offer lower cost at the expense of quality or motion realism. To evaluate cost-effectiveness, run a test with representative inputs and compare total cost vs. output quality against other available models. OrcaRouter's model catalog page likely lists price per generation for each provider.
Common cost factors include: input image resolution and file size, output video length and resolution, model version (v2.1-master vs. older versions), and any optional parameters like number of frames or steps. Since exact pricing is not disclosed, users should assume that larger or longer outputs increase cost. Additionally, OrcaRouter may charge for token usage of the text prompt and any system messages. Always test with the exact settings you plan to use to estimate cost.
Call the API with an HTTP POST to https://api.orcarouter.ai/v1/chat/completions (or the appropriate endpoint as documented). Set the model parameter to "kling/kling-v2-1-master". Include your API key in the Authorization header. The request body should follow OpenAI's chat format: a messages array containing a system message (optional) and a user message. For image input, include a content part of type "image_url" with the image as a base64 string or URL. Optionally provide a text prompt as another content part. The response will include a message with the generated video (likely as a URL or base64).
Available parameters beyond the required image may include: prompt (text describing desired motion), negative_prompt (to exclude certain effects), duration (in seconds), and resolution (width x height). However, exact supported parameters are not all documented in the provided facts. Refer to Kling's official API documentation for the complete parameter list. Standard OpenAI parameters like temperature, top_p, max_tokens may not apply; video generation uses special options. OrcaRouter may also support a metadata field for user-defined IDs.
Streaming of intermediate results is not mentioned in the available facts. Video generation models typically do not support true streaming because the full output must be generated before playback. The API likely returns a synchronous response after generation completes. If real-time feedback is needed, consider asynchronous polling or webhooks if supported. Check OrcaRouter's API documentation for any streaming capabilities specific to this model.
Migration requires changing the base URL to https://api.orcarouter.ai/v1, updating authentication to use an OrcaRouter API key, and adjusting the model identifier to "kling/kling-v2-1-master". The request format is OpenAI-compatible, so if your previous API also followed that pattern, code changes are minimal. If your original API used different parameter names, map them accordingly. Test with a simple request first. Be aware that OrcaRouter may have different rate limits or pricing; adjust your usage quotas.
The AA I2V Arena score for kling-v2-1-master (1203.0) suggests it surpasses many alternatives in quality. Runway Gen-3 Alpha is a competing video generation model that also supports image-to-video. Without a direct benchmark comparison, general observations: both produce high-quality outputs, but kling-v2-1-master may excel in preserving input image details, while Runway might offer faster inference or longer video length. Users should evaluate both on their specific use case. OrcaRouter may offer both models, allowing side-by-side testing.
Pika 2.0 is another popular image-to-video model. The AA I2V Arena score of 1203.0 for kling-v2-1-master indicates it is highly rated in human evaluations. Pika's score, if lower, would suggest kling has an edge in motion coherence and visual fidelity. However, Pika may offer more creative control or specific editing features. Without official comparisons, the best approach is to test both models with identical images and prompts on OrcaRouter's platform to see which meets your quality and cost requirements.
Stable Video Diffusion (SVD) is an open-source model with known strengths in generating consistent video from images. Kling's v2.1-master outperforms SVD on the AA I2V Arena benchmark (SVD's score not provided here). If benchmark quality is your priority, choose the Kling model. However, SVD may be run locally without API costs, making it suitable for high-volume projects where budget outweighs quality. OrcaRouter's API provides easy access to kling-v2-1-master without local infrastructure.
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-v2-1-master",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)| Per request | $0.2800 |
| Currency | USD |
| Flat fee per API call (image generation models) | |
GET /api/public/models/kling/kling-v2-1-masterOpen @misc{orcarouter_kling_v2_1_master,
title = {kling/kling-v2-1-master API},
author = {kling},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/kling/kling-v2-1-master}
}kling. (n.d.). kling/kling-v2-1-master API. OrcaRouter. https://www.orcarouter.ai/models/kling/kling-v2-1-master