# Kling v2.5 Turbo Text-to-Video

Kling v2.5 Turbo Text-to-Video is the fastest way to go from a written description to a video clip in the v2.5 generation. It converts natural language prompts into physics-aware video without a reference image, at turbo speed optimized for high-iteration creative workflows.

- **Model ID:** `klingai/kling-v2.5-turbo-t2v`
- **Type:** video
- **Providers:** klingai
- **Context window:** Not applicable
- **Maximum output tokens:** 0
- **Pricing:** See the provider details below
- **Canonical page:** https://vercel.com/ai-gateway/models/kling-v2.5-turbo-t2v

## Supported parameters

- **Parameters:** `aspectRatio`, `resolution`, `duration`, `fps`
- **Operations:** `text-to-video`
- **Input modalities:** `text`
- **Output modalities:** `video`
- **Aspect ratios:** `16:9`, `9:16`, `1:1`
- **Resolutions:** `1080p`
- **Durations:** `5s`, `10s`
- **Frame rates:** `30 fps`
- **Maximum outputs per request:** Not reported

## Example

```ts
import { experimental_generateVideo as generateVideo } from 'ai';

const result = await generateVideo({
  model: 'klingai/kling-v2.5-turbo-t2v',
  prompt: 'A serene mountain lake at sunrise.'
});
```

## About

Kling v2.5 Turbo Text-to-Video starts from a text description and generates a video clip entirely from that prompt. No image input is needed. The turbo path prioritizes speed: it applies fewer refinement passes than standard or Pro modes so you get a result faster at lower per-second cost. When rapid prompt-to-clip iteration matters more than maximum refinement per frame, that tradeoff is the point.

V2.5 tightened prompt adherence, physical simulation (cloth, liquid, crowds), and style consistency across frames. Portrait-style prompts that describe expressions also track a bit better than older Kling text-to-video tiers.

Because the model generates visual content entirely from text, prompt construction is the main lever. Clear scene descriptions, motion cues, and atmosphere details yield more predictable results. Lock prompt templates that match your brand before you scale volume.

## What to consider

Video generation is in beta for Pro and Enterprise plans and paid AI Gateway users. Confirm your plan before you ship.

## When to use

### Best For
- High-volume prompt variation testing, exploring many textual interpretations of a creative concept before selecting finalists for higher-quality rendering
- Short-form social media content generation from written creative briefs, where turbo speed keeps the pipeline responsive
- Concept visualization and storyboarding where fast turnaround matters more than final-quality rendering
- Prototype video assets for app or web UI mockups before production filming

### Consider Alternatives When
- A reference image must anchor the visual subject or style. Use the i2v variant
- You need multi-shot narrative video with distinct scene changes. V2.6 t2v supports multi-shot storytelling
- You require integrated audio narration or sound effects. V2.6 t2v includes native audio generation

## Best for

- **High-volume prompt testing:** Exploring many textual interpretations of a creative concept before selecting finalists for higher-quality rendering
- **Social short-form content:** Generating social media content from written creative briefs where turbo speed keeps the pipeline responsive
- **Concept visualization:** Storyboarding and exploration where fast turnaround matters more than final-quality rendering
- **UI prototype assets:** Prototype video assets for app or web UI mockups before production filming

## Consider alternatives

- **Image-anchored visuals:** A reference image must anchor the visual subject or style, so use the i2v variant
- **Multi-shot narratives:** You need multi-shot narrative video with distinct scene changes, which V2.6 t2v supports
- **Integrated audio:** You require audio narration or sound effects, which V2.6 t2v includes natively

## Frequently asked questions

### How does the turbo tier affect video quality versus speed?

Turbo applies fewer refinement passes than standard or Pro paths, so runs finish sooner and cost less per second of video. You don't get every refinement pass from higher tiers. For iteration-heavy or social workloads, that speed usually matters more.

### What prompt elements most improve output quality for t2v generation?

Specify subject, environment, motion, and visual atmosphere in your scene descriptions. Including motion cues (panning, close-up, slow motion) and atmosphere descriptors (lighting, color palette, mood) helps the model render your intent rather than making compositional choices by default.

### What output durations are available?

Outputs are available at five or 10 seconds. Supported aspect ratios include 9:16 vertical, 16:9 widescreen, and 1:1 square.

### Does v2.5 Turbo t2v support structured camera control?

V2.5 reads motion language in prompts more reliably than older Kling text-to-video tiers. Explicit camera parameters (pan, push, pull) show up in later Kling versions; lean on prompt wording here.

### Why use v2.5 Turbo t2v rather than v2.6 t2v?

V2.5 Turbo is faster and cheaper when you don't need audio output or multi-shot storytelling. V2.6 t2v adds native audio generation and multi-shot narrative capability. Choose v2.6 when those features are part of your required output.

### What access tier is required to use this model through AI Gateway?

Video generation is in beta for Pro and Enterprise plans and paid AI Gateway users.

## Links

- [Model page](https://vercel.com/ai-gateway/models/kling-v2.5-turbo-t2v)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://app.klingai.com/global/release-notes/2025-09-19)
- [Provider pricing](https://klingai.com/global/dev/pricing)