Kling AI
Kling v2.1 Master — cinematic AI video generation competing with Google Veo 3
Kling AI (by Kuaishou) is a leading video generation platform offering text-to-video and image-to-video models. Kling v2.1 Master produces cinematic-quality 5-second and 10-second video clips with realistic motion and high visual fidelity. The 2026 platform has expanded with Kling v2.1 Standard (faster, lower cost), a Video Effects API, and improved image-to-video consistency. Kling is available via API and through Replicate and fal.ai.
Kling v2.1 Master produces some of the most cinematic AI video available — competing directly with Google Veo 3 on quality benchmarks — with a Video Effects API and strong image-to-video consistency.
- Kling v2.1 Master: cinematic-quality video with realistic motion
- Image-to-video with strong subject consistency
- Video Effects API for motion effects and transitions
- Available on Replicate and fal.ai for easy integration
- Competitive pricing vs Google Veo 3 and Sora
- 5-second and 10-second clip generation
- Longer generation times than image models (30–120 seconds)
- Primarily targets Chinese market — international latency may vary
- No open-weight models
Kling AI Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
kling-v2.1-master Kling | Video | frontier | $0.56 per second EST Jul 2026 | — | 60.0s | 0 t/s | 4K | 0 | ||
kling-v2.1-standard Kling | Video | advanced | $0.28 per second EST Jul 2026 | — | 40.0s | 0 t/s | 4K | 0 |
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Full comparison tableKling AI LLM pricing overview
Kling AI is a proprietary AI lab that develops and hosts its own large language models. Kling AI (by Kuaishou) is a leading video generation platform offering text-to-video and image-to-video models. Kling v2.1 Master produces cinematic-quality 5-second and 10-second video clips with realistic motion and high visual fidelity. The 2026 platform has expanded with Kling v2.1 Standard (faster, lower cost), a Video Effects API, and improved image-to-video consistency. Kling is available via API and through Replicate and fal.ai. Common use cases include Video generation, Marketing video, Image animation, Creative content. Headquartered in Beijing, China, founded 2024. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Kling AI vs other LLM providers
Kling AI competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Kling v2.1 Master produces some of the most cinematic AI video available — competing directly with Google Veo 3 on quality benchmarks — with a Video Effects API and strong image-to-video consistency. Use the LLM provider comparison tool to see Kling AI token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Kling AI?
Kling AI stands out because: Kling v2.1 Master produces some of the most cinematic AI video available — competing directly with Google Veo 3 on quality benchmarks — with a Video Effects API and strong image-to-video consistency.. The API currently offers 2 models, spanning 1 frontier, 1 advanced tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Kling AI token pricing
Kling AI charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from Kling AI's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Kling AI context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Kling AI's largest context window is 4K tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Kling AI makes sense
Managed inference APIs like Kling AI eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Kling AI API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Kling AI token price data is available in the LLM price history charts.
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