Kling 1.5
Strongest physics simulation in the space — and cheaper than Sora.
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Kling 1.5 Overview at a Glance
Kling 1.5 is a text-to-video model from Kuaishou, first released on 19 September 2024. It is proprietary (closed-weights) and sits in the text to video, video, and consumer categories of our catalog. Strongest physics simulation in the space — and cheaper than Sora. This page covers Kling 1.5 pricing, benchmarks, API limits, speed, modalities, best use cases, and how it compares with similar models — so you can decide whether it belongs in your stack in 2026.
Kling 1.5 is a text-to-video generator priced around $0.05 per second of output (about $0.50 for a 10-second clip). Buyers care about motion realism, physics coherence, camera control, max clip length, audio inclusion, watermarking, and commercial licensing. Kling 1.5 is proprietary (closed-weights) from Kuaishou.
Marketing, film previsualization, and social content teams evaluate Kling 1.5 when they need short generated clips without a full production shoot. Typical jobs include action shots and physical-product demos. Notable strengths: best at physical realism, cheap, and up to 2min clips. Watch-outs: english prompt adherence behind us peers. This page summarizes pricing, feature limits, performance expectations, and how Kling 1.5 stacks up against Sora, Runway, and Kling-class alternatives.
Before committing budget, generate the same prompt across two or three video models, score motion artifacts and brand safety, and multiply per-second price by your average clip length. Queue times and concurrency caps often matter as much as list price for campaign deadlines. Use the comparison table and FAQ for “Kling 1.5 pricing”, limits, and alternatives research.
- Price per second
- $0.05
- Time to first token
- 45s
- Input modalities
- text, image
- Output modalities
- video
- License
- Proprietary
- Provider
- Kuaishou
- Best at physical realism
- Cheap
- Up to 2min clips
- English prompt adherence behind US peers
- Action shots
- Physical-product demos
Kling 1.5 Pricing
Kling 1.5 video pricing is typically metered per second of generated footage (about $0.05/sec in our catalog snapshot). Longer clips, higher resolution, and extended motion controls increase spend quickly, so prototype at short durations before scaling production renders. A useful planning habit: price a hero 8–12s shot and a full weekly content pack separately.
Failed or moderated renders may still consume credits depending on the vendor — read the fine print. Enterprise contracts sometimes unlock watermark-free exports and higher concurrency that change the real cost per usable second of Kling 1.5 output.
- Price per second
- $0.05
- 10-second clip
- $0.50
- 60-second clip
- $3.00
Kling 1.5 Benchmarks
Kling 1.5 is a text-to-video model, so classic LLM suites (MMLU, GPQA, HumanEval) do not apply. Instead, judge quality with side-by-side generations, human preference tests, and modality-specific metrics (FID/CLIP for images, FVD/motion coherence for video, MOS/WER-adjacent listening tests for speech). We highlight qualitative strengths and peer comparisons further down this page.
Kling 1.5 API Pricing
Video APIs for Kling 1.5 usually debit credits or dollars per rendered second. Check concurrency limits, max clip length, and whether audio is included. Enterprise contracts may unlock higher resolution or watermark-free exports.
Design for long-running jobs: poll or webhook on completion, retry transient failures with idempotency keys, and keep prompt/style presets versioned so creative iterations stay comparable when Kling 1.5 model versions change.
Always verify live rates on the official docs — our figures are refreshed periodically (last catalog update: 2026-06) and providers change list prices. Official reference: https://kling.kuaishou.com/en.
Kling 1.5 Context Window
Context window is an LLM concept and does not map 1:1 onto Kling 1.5. For text-to-video models, the practical limits are prompt length caps, max resolution/duration, and concurrent job quotas set by Kuaishou. Check the official docs for the latest hard limits on prompt characters and output size.
Think of “context” for Kling 1.5 as the creative brief you can pack into one job: style references, negative prompts, camera notes, and brand constraints. If the product truncates long prompts, move durable instructions into saved presets or project settings instead of repeating them every call.
Kling 1.5 Input / Output Modalities
Kling 1.5 accepts text and image as input and produces video as output. Knowing the modality matrix matters when you design pipelines — for example, vision-capable language models can take screenshots or PDFs as images, while pure text models need an OCR or captioning step first.
If you need bidirectional voice, native video understanding, or tool-use with multimodal arguments, confirm support in Kuaishou’s API schema rather than assuming parity with the consumer chat app. Modality support also affects pricing: image or audio inputs may be tokenized differently than plain text.
For Kling 1.5, prompts may combine text with optional image/video references depending on the product. Outputs are short video files — budget bandwidth, transcoding, and review tooling alongside generation cost.
- Inputs
- text and image
- Outputs
- video
Kling 1.5 Token Limits
Kling 1.5 is not metered in LLM tokens. Limits show up as max prompt length, max output duration/resolution, and account rate limits. Treat the pricing rows above as the cost unit, and consult Kuaishou for concurrency and fair-use caps.
Operationally, set guardrails in your app: maximum jobs per user, maximum output duration/resolution, and backoff when Kuaishou returns 429s. Those application-level limits prevent surprise bills even when the model API itself is flexible.
Kling 1.5 Speed
Generation latency for Kling 1.5 depends on resolution, duration, and queue depth at Kuaishou. Our snapshot lists a typical turnaround near 45 seconds under default settings. Production apps should implement async jobs, webhooks, and retries rather than blocking user requests on cold starts.
- Typical generation time
- 45s
Kling 1.5 Performance Charts
Because Kling 1.5 is a text-to-video model, we emphasize qualitative and pricing comparisons rather than LLM benchmark bars. The similar-models section below is the primary performance chart substitute — scan price-per-unit and feature notes to position Kling 1.5 in the market.
Intelligence index vs similar models
Comparison with Similar Models
Choosing an AI model is rarely absolute — it is relative to the next-best option. Kling 1.5 is most often weighed against Sora, Runway Gen-3 Alpha, and Gemini 2.0 Flash. Compare intelligence (or generation quality), latency, price, license, and modality support. A slightly weaker but much cheaper model can win for high-volume workloads; a pricier frontier model wins when a single mistake is expensive.
Use the links and table below for structured Kling 1.5 vs alternatives research. We also maintain dedicated head-to-head pages for popular matchups when available. If you are standardizing on Kuaishou, check sibling models from the same lab before leaving the ecosystem.
For text-to-video models, run the same creative brief through Kling 1.5 and two peers, blind-rank outputs with stakeholders, and only then look at price. Quality gaps are often obvious in a side-by-side grid even when benchmarks are unavailable.
Also compare licensing and brand-safety defaults — a model that is slightly prettier but blocks commercial use (or watermarks exports) can be a non-starter for client work. Factor those constraints into the Kling 1.5 decision, not just aesthetics.
| Model | Provider | Intelligence | Speed | Price |
|---|---|---|---|---|
| Kling 1.5 | Kuaishou | — | — | $0.05/sec |
| Sora | OpenAI | — | — | $0.50/sec |
| Runway Gen-3 Alpha | Runway | — | — | $0.10/sec |
| Gemini 2.0 Flash | 64 | 220 t/s | $0.18/1M |
Kling 1.5 vs popular alternatives
Kling 1.5 Best Use Cases
Best use cases for Kling 1.5 follow from its strengths, price point, and modality support. Match the model to the job: frontier reasoning for hard planning, fast/cheap tiers for classification, image/video/speech specialists for media pipelines.
Based on catalog notes, Kling 1.5 is a particularly strong fit for action shots and physical-product demos. Validate with a short bake-off on your real prompts before a full cutover.
Strong fits include social teasers, storyboard animatics, and idea pitches. Weaker fits include long-form narrative with consistent characters across minutes of footage — most text-to-video systems still struggle with identity lock over long timelines.
- Action shots
- Physical-product demos
Kling 1.5 Pros & Cons
Every model trades quality, speed, cost, and openness. Here is a concise pros and cons list for Kling 1.5 drawn from our catalog strengths and weaknesses — pair it with your own evals before committing.
Read pros as “reasons to shortlist” and cons as “risks to mitigate,” not as deal-breakers in isolation. A listed weakness (for example higher price or smaller context) may be irrelevant if your workload is bursty, short-context, or already standardized on Kuaishou.
After scanning this list, jump to the comparison table and FAQ for decision support, then lock a trial window with success metrics before replacing a production model with Kling 1.5.
- Best at physical realism
- Cheap
- Up to 2min clips
- English prompt adherence behind US peers
Kling 1.5 — frequently asked questions
Need help choosing between models?
Compare every option in one sortable table — intelligence, speed and price on a single page.