TikTok Symphony avatar pricing: no figure found, and what Sume lists

We found no Symphony avatar price in our sources. Here is what a Sume-side cost model can use from the docs: compose, trim, captions and speech-to-text.

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We have no Symphony avatar price to quote. The Stackmatix snippet we read on 2026-10-02 describes Voiceover Avatars and Product Avatars but gives no figure in the part we saw, so this post will not guess one. What we can price is the Sume side of an ad pipeline, using the rates printed in Sume's own docs.

What does Sume's documentation list?

These are the fixed public rates on the pages cited. Each page says to confirm live pricing in GET /v1/catalog, so check before you budget.

Fixed Sume rates in the docs (read 2026-10-02)
StepPublic ratePage
Timeline compose (still plus video)$0.02 per jobTimeline compose
Video trim$0.02 per jobVideo trim
Standalone video captions, up to 60 s$0.20 per jobVideo captions
Speech-to-text in video inspect$0.01 per audio minuteVideo inspect
Video probe and stillsUnbilledVideo inspect

Why is there no avatar line in the table?

Because the avatar and video generation prices on the pages we cite are not a single fixed figure we can quote here. Read GET /v1/catalog for the current rate on the model you plan to call, and use it in place of a number from memory.

How do you compare a bundled tool with an API?

Compare per finished ad, not per feature. List the steps each route needs: generate, trim, caption, label, export. Price each from its own page, and add your time for the steps a bundled tool does inside one screen.

Avoid comparing a quote with a list price. A list price excludes retries and review passes. Count how many versions you throw away before one ships, and multiply the per-clip cost by that number.

Does a cheap step make the whole ad cheap?

No. The fixed rates above are for edits and checks. They are small next to generation, and generation is where retries cost the most. Budget for the number of attempts it takes to get one usable clip, not for the best case.

Run a small batch first, count the attempts, and read the usage page in the dashboard to see what the batch actually cost before you scale it.

Sources

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