Kling AI Model Catalog

Overview

Kling AI offers multiple model versions across video generation, image generation, lip sync, virtual try-on, and effects. Each version trades off quality, speed, and cost. This skill is the reference for choosing the right model.

Video Generation Models

Model ID	Supports	Max Duration	Resolution	Speed	Quality
kling-v1	T2V, I2V	10s	720p	Fast	Good
kling-v1-5	I2V only	10s	1080p	Fast	Better
kling-v1-6	T2V, I2V	10s	1080p	Medium	Better+
kling-v2-master	T2V, I2V	10s	1080p	Medium	High
kling-v2-1	I2V only	10s	1080p	Medium	High
kling-v2-1-master	T2V, I2V	10s	1080p	Medium	High
kling-v2-5-turbo	T2V, I2V	10s	1080p 30fps	Fast	High
kling-v2-6	T2V, I2V	10s	1080p 30-48fps	Medium	Highest

T2V = text-to-video, I2V = image-to-video

Kling v2.5 Turbo (Recommended for Speed)

• 40% faster than v2.0
• Up to 1080p at 30 FPS
• Best cost/quality ratio for production pipelines

Kling v2.6 (Recommended for Quality)

• Native audio generation (voice, SFX, ambient in one pass)
• 1080p at 30-48 FPS
• Set motion_has_audio: true for synchronized audio

Image Generation Models (Kolors)

Model ID	Purpose	Resolution
kolors-v1-5	Face/subject reference	Up to 2048x2048
kolors-v2-0	Image restyle	Up to 2048x2048
kolors-v2-1	Text-to-image	Up to 2048x2048

Specialty Models

Feature	Endpoint	Model Versions
Lip Sync	/v1/videos/lip-sync	v1.6+
Virtual Try-On	/v1/images/kolors-virtual-try-on	v1.5
Video Extension	/v1/videos/video-extend	All video models
Effects	/v1/videos/effects	v1.6+
Motion Control	T2V/I2V with camera_control	v1.6+

Mode Selection

Every video generation accepts a mode parameter:

Mode	Credits (5s)	Credits (10s)	Use Case
standard	10	20	Drafts, previews, iteration
professional	35	70	Final output, client delivery

Model Selection Decision Tree

Need fastest generation?
  → kling-v2-5-turbo + standard mode

Need highest quality?
  → kling-v2-6 + professional mode

Need audio in the video?
  → kling-v2-6 with motion_has_audio: true

Image-to-video only?
  → kling-v2-1 (optimized for I2V)

Budget-conscious production?
  → kling-v2-5-turbo + standard mode (10 credits/5s)

Legacy compatibility?
  → kling-v1-6 (stable, well-documented)

API Usage

# Specify model in any video generation request
response = requests.post(f"{BASE}/videos/text2video", headers=headers, json={
    "model_name": "kling-v2-6",       # model version
    "mode": "professional",            # standard or professional
    "prompt": "A futuristic city at sunset with flying cars",
    "duration": "5",
    "aspect_ratio": "16:9",
})

Aspect Ratios (All Models)

Ratio	Use Case
16:9	Landscape, YouTube, presentations
9:16	Vertical, TikTok, Reels, Stories
1:1	Square, Instagram, thumbnails
4:3	Classic TV, presentations
3:4	Portrait photos
3:2	Standard photography
2:3	Tall portrait
21:9	Ultra-wide, cinematic

Prerequisites

• A dated snapshot of the provider's current model and capability documentation, a selection owner, an approved credit budget, and an explicit fallback model.
• Define the intended use, aspect ratio, duration, audio needs, quality/latency thresholds, and destination. Test with synthetic prompts and rights-cleared reference media only; confirm content-policy and likeness/consent requirements before submission.
• Use a sandbox project and draft/watermarked canaries. Production promotion requires owner approval and a rollback/removal plan for outputs that fail policy, rights, quality, or cost checks.

Instructions

1. Translate the request into capability requirements, then verify each candidate's current support, limits, pricing mode, and policy constraints from the dated documentation snapshot.
2. Eliminate unsupported or unapproved candidates before generation. Run the smallest synthetic canary for the remaining candidates with publish=false, watermark/draft enabled, and an explicit credit ceiling.
3. Compare aggregate quality, latency, credit use, policy result, and rights review. Choose the model that satisfies the requirements and document why the fallback is acceptable.
4. Obtain approval before production use. Keep the selected model ID pinned, monitor the first staged release, and revert to the approved fallback if any threshold or policy check regresses.
5. Remove rejected, superseded, or unapproved canary media, revoke temporary access, and retain a redacted selection receipt rather than raw prompts or outputs.

Output

Return a model-selection record with requirements, documentation snapshot date, candidate IDs and exclusions, synthetic fixture ID, aggregate canary metrics, estimated credits, policy/rights outcomes, selected model, fallback, approval state, rollout scope, retention deadline, and rollback/removal reference. Do not include prompts, media, likenesses, audio, signed URLs, identities, or secrets.

Error Handling

• If documentation is stale, contradictory, or missing a capability, mark the candidate unknown and stop selection until verified; do not guess from a model name.
• If a candidate rejects content, lacks a required feature, exceeds budget, or fails quality/latency thresholds, quarantine and remove its canary output, then evaluate only an approved fallback.
• If the selected model becomes unavailable or changes behavior, pause promotion, restore the pinned fallback, reconcile in-flight tasks, and record the redacted rollback receipt.

Examples

For a synthetic vertical draft, set requirements=t2v,9:16,5s, candidates kling-v2-5-turbo,kling-v2-6, destination=sandbox-review, watermark=draft, publish=false, and credits_max=100. Select only after policy=pass, rights=pass, and owner approval; otherwise remove both canary outputs.

Resources

• Model Documentation
• Video Duration Reference
• Pricing