kling-4-prompting v1.0.0A

Immutable. This exact content never changes and is served at /api/v1/blob/41bbf474322769de.

---
name: kling-4-prompting
description: >-
  How to prompt Kling 4 (Kling 4.0, kling_v4) for cinematic AI video that does not look like AI
  slop: let a strong image model compose every shot, then give Kling 4 two to four key frames and a
  short director's prompt. Covers reference-to-video inputs (@Image, @Element, @Video, @Voice),
  multi-shot prompts with seconds, 30-second takes, dialogue in quotes, "no slow motion", lens and
  film-look lines, elements that need two photos, the failure fixes (slow motion by default, six
  fingers, AI-smooth framing, face drift in close-ups) and the exact varg call with the reference
  route pinned. Use when someone asks how to prompt Kling 4, why Kling 4 looks worse than Seedance
  2.5, how to use Kling 4 reference-to-video, elements, multi_prompt or keyframes, or wants a
  Kling 4 prompt written. For a whole short film use kling-4-keyframe-film; for a 30-second action
  montage use kling-4-action-montage.
license: MIT
metadata:
  author: vargHQ
  version: "1.0.0"
  openclaw:
    requires:
      anyBins:
        - curl
    homepage: https://varg.ai/how-to
compatibility: >-
  Needs a VARG_API_KEY with curl (see the varg-ai skill) or the varg MCP connection
  (https://mcp.varg.ai/mcp). Key frames are made with gpt_image_2_5 or nano_banana_2_1 on the same key.
allowed-tools: Bash(curl:*) Bash(jq:*) Bash(ffmpeg:*) Bash(ffprobe:*) Read Write Edit
---

# Kling 4 prompting: compose in pixels, animate with Kling

Kling 4 (`kling_v4`, Oct 2026) renders up to 30 s at 1080p in one take, cuts between shots by itself, speaks dialogue with native audio and takes up to 10 images, 5 videos, 7 elements and 3 voices as references. Its motion and sharpness are a step up from Kling 3. Its weak spot is the shots it invents: given only a character sheet, a location and a long prompt, it picks flat, "AI-smooth" framing.

The method below comes from fal's Kling 4 cinematic guide and Halloween music video ([YouTube, 2026-10-30](https://www.youtube.com/watch?v=WzXKggH-0Io)). It was checked on varg with our own test (see Tested).

## 1. The mental model

| Seedance 2.5 habit | What Kling 4 wants instead |
|---|---|
| Two character sheets and a location render, a long prompt, then hope | Every important shot already made as a still: composition, lens, light, grade |
| The video model decides the framing | The image model decides the framing, Kling 4 only moves between the frames |
| Long prompt does the work | Short prompt (1–4 sentences), the pictures do the work |

In the author's words: "don't let Kling 4 have to think that much. Give it the still frames, let GPT 2.5 think." With the same pre-made composition, lighting and references, Kling 4 and Seedance 2.5 come out "very comparable", and Kling 4 looks sharper ("cinematic sharpness", "characters feel crisp and alive").

## 2. Inputs and limits (reference-to-video)

| Input | Prompt name | Limit | Notes |
|---|---|---|---|
| `image_urls` | `@Image1`, `@Image2`… | 10 (frame images count toward it) | Key frames and style references |
| `video_urls` | `@Video1`… | 5, each 3–30 s, 30 s total with element videos | Motion reference (a real BMX trick, a dance) |
| `elements` | `@Element1`… | 7 (3 with a voice, 3 as video) | A character: `frontal_image_url` + 1–3 `reference_image_urls`, or a 3–8 s `video_url`, optional `voice_id` |
| `voice_ids` | `@Voice1`… | 3 including element voices | Create on fal's Kling create-voice model |
| `multi_prompt` | — | list of `{prompt, duration}` | Total length = sum of shot durations |
| `duration` | — | 3–30 s (integer) | Flash: up to 20 s, 720p |
| `aspect_ratio` | — | 16:9, 9:16, 1:1, 21:9, auto | No 4:3 |
| `resolution` | — | 720p, 1080p | 4K and 10-bit HDR are announced, not live |

Audio-only references are not accepted. To pass a song or a voice line, put the audio on a black video track and send it as a video.

An element needs at least two images: a frontal image plus one or more reference images. A single photo fails before the job starts. If you only have one photo, make two more angles with the image model ("same person, three-quarter view, same outfit and light"); duplicating the photo also passes, but locks the likeness less well.

## 3. The prompt patterns that worked

**Short director's prompt + key frames.** One line of what happens, one line of look, the length:

```
the pumpkin boy break dances in NYC while everyone bobs their heads to the 90s hip hop beats.
shaky handheld footage. 30 seconds.
```
(3 key frames of the dancer in the crowd ring were attached; the agent expanded it to "Shaky handheld 16mm street documentary footage with heavy film grain, low fisheye angle, cinemascope frame. Washington Square Park, New York City, overcast winter…")

**Dialogue scene from two frames.** Give the wide shot and the over-the-shoulder, write the line in quotes, end with the camera event and the look:

```
21:9 the man walks by the woman as she places a white pumpkin on the table. the man says
"what does the white one do?" she smiles and smashes the white pumpkin on the top of the man's
head with both hands. The camera shakes and falls to the ground and fades to black. handheld
shaky 16mm documentary style footage. 15 seconds.
```
Kling made its own hard cut to her, gave her a natural voice without a voice sample, and rolled the camera onto its side as it fell. A canted or rolled camera is rare in earlier models: ask for the camera event ("the camera falls to the ground") rather than the angle.

**Story beats in one 30-second take.** Name the tricks, the turn and the line:

```
montage of the punkin boy doing BMX tricks on the Brooklyn Banks. tail whip. bar spin. Then he
messes up the trick mid air and crashes to the ground smashing the punkin head and returning to a
normal man. the blond girl helps him up. she says "Sick run, want to go again?" 30 seconds.
```
(4 key frames: two tricks, the take-off, the crash with the girl helping him up.)

**Shot list with seconds (`multi_prompt`).** When the order and timing matter, split the take. For the take above, fal's agent planned "Crash: he lands on just his back wheel, loops out and falls backwards. The pumpkin head hits the concrete and explodes, leaving the real man lying there" and "Ending: I split the last 7 seconds into two shots. First she pulls him up (3s), then a close-up on her face as she says 'Sick run, want to go again?' (4s)". Written as `multi_prompt` (our version):

```json
"multi_prompt": [
  {"prompt": "Montage of the rider from @Image1 doing a tail whip and a bar spin on the brick banks, fisheye, real speed.", "duration": 15},
  {"prompt": "He misses the next trick mid-air, lands on the back wheel, loops out and falls; the pumpkin head hits the concrete and explodes, leaving the real man lying there, as in @Image4.", "duration": 8},
  {"prompt": "The blonde woman from @Image4 pulls him up.", "duration": 3},
  {"prompt": "Close-up on her face, she says \"Sick run, want to go again?\" and laughs.", "duration": 4}
]
```

**Rules that showed up in every good take**
- Say **"No slow motion, everything moves at real speed"** for action. Kling 4 drifts into slow motion by default.
- Name the lens and the grade in the prompt as well as in the frames ("ultra-wide fisheye", "16mm film, coarse grain, motion blur"). Without it Kling picks its own lens: in one close-up it chose a telephoto while the whole film was wide.
- Put spoken lines in double quotes after "says". One or two short lines per take.
- Ask for motion blur in the stills and the prompt: it hides AI smoothness.
- Give a start and an end state (the crash frame, the closing smile). Kling 4 is good at "temporally making sense" between frames you set.

## 4. The key frames (where the quality comes from)

Make each still with `gpt_image_2_5` (best at following long, structured prompts and at real places) or `nano_banana_2_1` (cheaper drafts). Write it like a shot spec. The prompt behind the film's crash frame:

```
Cinematic 21:9 film still, high overhead shot tilted at an angle, ultra-wide fisheye lens with
heavy barrel distortion bending the brick ramps and street edges. Use the four reference images:
Image 1 is the man to match exactly: same face, black cap with small white ring logo, round black
glasses, black hoodie with CODEX chest text and white sleeve graphics, black pants, black sneakers
(he has his normal human head). Image 2 is the woman to match exactly: platinum blonde blunt bob
with bangs, silver hoop earrings, small neck tattoo, blue denim overalls/jacket. Image 3 is a
reference only for the black BMX bike (do NOT show the pumpkin character). Image 4 is a reference
only for color grade, 16mm film tone, grain and motion blur; do not include its people.

Scene: the man in black sprawled on his side on the ground, propped on one elbow, dazed. His black
BMX bike lies flat on the ground beside him, rear wheel spinning in a motion blur. A white pumpkin
is smashed on the concrete right next to his head, white shell fragments and seeds scattered. The
woman in denim crouches beside him, both hands under his arm, helping him up, mid-motion with blur
on her arms and hair. A few blurred skaters and onlookers at the curved frame edges. Overcast
daylight. 16mm film look like Image 4: milky yellow-green highlights, teal shadows, deep blacks,
coarse grain, motion blur. Avoid text overlays, clean digital look, extra pumpkins, night.
```

The parts to copy: aspect + angle + lens first; "Image N is X, match exactly" with the details listed; "reference only for…" with what to leave out; the scene in one present-tense paragraph; the grade in concrete colors; an "Avoid…" line.

Fix broken anatomy in the still before animating. In the film, a warped BMX (no seat, a missing peg) in a middle frame made that trick the sloppiest part of the take while the take-off from a clean frame was "really good".

## 5. Run it on varg

`kling_v4` routes by input: text only → text-to-video; files → **image-to-video, where image 1 is the start frame and image 2 the end frame** (a third image is not used). For key frames as references, pin the reference route by its key:

```bash
curl -s -X POST https://api.varg.ai/v2/tools/video/call \
  -H "Authorization: Bearer $VARG_API_KEY" -H "Content-Type: application/json" -d '{
  "model": "fal:fal-ai/kling-video/v4/reference-to-video",
  "files": [{"url": "FRAME_1"}, {"url": "FRAME_2"}, {"url": "FRAME_3"}],
  "aspect_ratio": "21:9", "resolution": "1080p", "duration": 30, "audio": true,
  "provider_options": {"fal": {"multi_prompt": [
    {"prompt": "…shot 1, uses @Image1…", "duration": 10},
    {"prompt": "…shot 2, uses @Image2…", "duration": 10},
    {"prompt": "…shot 3, uses @Image3…", "duration": 10}
  ]}}
}'
```

- `files` become `@Image1…` in order. Send `prompt` instead of `multi_prompt` for a one-paragraph take.
- Elements and voices: `provider_options.fal.elements` = `[{"frontal_image_url": "…", "reference_image_urls": ["…"], "voice_id": "…"}]`, then write `@Element1` in the prompt.
- Exact frames at exact times: pin `fal:fal-ai/kling-video/v4/keyframes-to-video` with `provider_options.fal.keyframes` = `[{"image_url": "…", "time": 0}, …]` (2–10 frames, prompt ≤ 2,500 characters).
- In the MCP: `call_tool({tool: "video", input: <the body above>})`. `estimate_cost` does not know route keys: price with `"model": "kling_v4"` and the same duration and resolution (same per-second price). 15 s at 720p ≈ $2.36 (236 credits); 30 s at 1080p ≈ $7.05 (705 credits).
- Draft at 720p or on `kling_v4_flash`, then rerun the winning take at 1080p.

## 6. Failure → fix

| What you see | Fix |
|---|---|
| Flat framing, smooth "AI" skin and light | You prompted it like Seedance (sheet + location + text). Make the shots as stills first (section 4). |
| Everything in slow motion | "No slow motion, everything moves at real speed" + "high speed, motion blur". |
| The face changes in a close-up | Add a clear photo of that person as another reference (or an element) and say which shot uses it. |
| Element job fails before starting | The element has one image. Add one to three more angles. |
| Six fingers, a warped bike or board | Fix the still that caused it; keep the hands or object large and clean in the frames. |
| Weird things in the background crowd | Expected in 30 s takes. Cut around it, or keep crowds blurred and at the frame edges. |
| A shot Kling invented looks more "AI" than your stills | Normal: its own multi-shots are a little softer. Use them as glue, or make that shot as a still too. |
| Wrong lens on an invented shot | Name the lens in the prompt for that shot. |

## 7. Editing mindset

Treat a 30-second generation like a live-action take: you get several cuts, repeated attempts at the same move and a ramp-up in one file, and you keep the 2–5 s that work. Generate 2–4 takes per beat, cut the best moments, and expect some takes to give nothing usable.

## Tested

2026-10-30 on varg, our own scene (not from the video): three 16:9 stills of a skateboarder on a Lisbon hill made with `gpt_image_2_5` in the section 4 format (the first from text, the other two with the first as reference; 68 credits for all three), then the pinned reference route with a three-shot `multi_prompt`, 15 s, 720p, audio on: 236 credits, about 4 minutes. The three shots came back in order at the set durations, with the same face, outfit, fisheye and grade, and she says "Again?" at the end. Weak spots: the ollie floats for about two seconds despite "no slow motion", and the board vanishes from her hand in the last shot. https://s3.varg.ai/files/72c55813-c0fa-4cf2-9f64-ec4f7ed0e7ae/file_9483ac202e90.mp4. Prompts and notes: `references/test-run.md`.

## Related skills

`kling-4-keyframe-film` (a short film or music video with this method), `kling-4-action-montage` (dance, sport tricks, chases in 30 s), `varg-character-design` (character sheets), `varg-consistent-characters`, `varg-ai` (models and prices). Prompts shown in the source video: `references/prompts-from-the-video.md`.