AI video with readable on-screen text: Kling 3.0 or Seedance 2.5

If the words on screen must be exactly right, do not rely on any video model to draw them. Kling says Kling 3.0 renders text natively and keeps logos legible, while Dreamina positions Seedance 2.5 as producing clean frames "ready for text and music editing". Both are claims about tendencies. The reliable route on Sume is to generate the clip without text and burn your own copy afterwards.

Here is what each vendor page says, how to prompt when you still want in-frame text such as a sign, and the captions call that puts exact wording on a finished clip.

What do Kling and Dreamina claim about text?

The Kling Video 3.0 page lists "native text rendering" that preserves brand clarity in commercial content. The Omni guide says the model keeps signage, captions and branded elements legible across camera movements and preserves logo clarity during dynamic shots.

The Dreamina Seedance 2.5 page takes the opposite approach: it describes output as clean-based 4K videos ready for text and music editing and says the model removes random subtitles and unwanted background music. In other words, Kling sells drawing the text, Seedance sells not drawing stray text so you can add your own.

How should you prompt for text inside the frame?

Keep it short and literal. One to three words on a single surface, in quotation marks, with the surface named: a neon sign above a door reading "OPEN", a coffee cup with the word "MORNING" on the sleeve. Long sentences, small print and several text areas are where models fail first.

Keep the camera gentle while the text is visible. A slow push-in keeps lettering stable; a whip pan or heavy rotation tends to smear it. Render a 4 or 5 second draft at 480p, freeze a frame, and read the letters before paying for a longer, higher-resolution take. Treat misspellings as a normal outcome, not a bug in your request.

How do you put exact words on a finished clip with Sume?

Use video captions. The video captions API takes a public HTTPS video_url and returns a captioned video. For text that has to match your copy exactly, pass cues (or segments) with text, start and end; the docs say authored overlay copy is burned in without speech-to-text. This also suits silent clips, which fail speech captioning with caption_no_speech.

Pick a style or leave it out and let the wording decide: the docs say an omitted style resolves to slam for Latin text and black-outline for Korean, and language is only a speech-to-text hint that never selects the style.

curl -X POST https://api.sume.com/v1/video-captions \
  -H "Authorization: Bearer $SUME_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: promo-0042-overlay" \
  -d '{
    "video_url": "https://example.com/clean-clip.mp4",
    "style": "slam",
    "cues": [
      {"text": "New roast. Same price.", "start": 0.5, "end": 3.0}
    ]
  }'

Which model should you pick for an ad with text?

For a logo or a one-word sign that has to appear inside the scene, test Kling first, via the kling-3 id on Sume. For anything with legal copy, prices or a call to action, generate on seedance-2.5 or kling-3 with no text in the prompt and add the words with captions. That keeps the generation cheap to redo and the typography in your hands.

Two limits to remember: Sume's video request has no text-overlay field, and the docs do not promise that any catalog model spells words correctly. Check the live catalog row for generate_audio and resolution, then draft at 480p or 720p. A failed job is refunded; a completed job with a misspelled sign is not, so inspect drafts before rendering at full size.