Delphin Resource
AI Video API Pricing Comparison: Per-Second vs Credits vs Plans
Compare AI video API pricing across per-second tiers, credits, and plan-style packaging. Learn how to normalize real clip costs before you compare vendors.
Last reviewed: April 28, 2026
Pricing note: Vendor pricing changes often. Use these examples to normalize comparison logic first, then confirm the current official pricing page before you budget production volume.
AI video pricing becomes confusing when one vendor sells seconds, another sells credits, and a third wraps the same cost logic inside a creator plan. The right comparison starts by translating every pricing page back into one concrete task, such as a 10-second clip at 720p or 1080p.
Why AI video pricing is harder to compare than text pricing
Text models usually expose a small number of billing units. Video models compound several variables at the same time: clip length, resolution, model tier, and sometimes audio or render mode.
That is why two pricing pages can both look simple and still be incomparable at a glance. One vendor may expose dollars per second, another may expose credits per second, and another may push you toward a bundled creator plan.
- Longer clips multiply cost immediately.
- Higher resolution can move you into a different tier, not just a slightly higher bill.
- Credits are not cheaper by default. They are just another pricing language that still has to be translated back into dollars or output volume.
Normalize one real task before you compare vendors
The safest method is boring on purpose: pick one deliverable, then translate every vendor back into that same job. A 10-second 720p clip, a 10-second 1080p clip, or a clip with native audio are common baselines.
Once you lock the task, you stop comparing marketing language and start comparing the real cost of finishing the same shot.
- Define the task first: length, resolution, and whether audio is included.
- Translate credits back into cash before deciding whether a vendor is cheaper.
- If a plan page hides too many variables, compare by likely monthly output instead of inventing fake precision.
Official pricing examples from public AI video API docs
These examples come from official vendor pricing pages that expose enough detail to normalize clip-level cost. They are useful reference points, not a promise that every workflow or quality mode will match exactly.
Public AI video API pricing examples
All examples below were checked against official pricing pages on April 28, 2026.
| Vendor | Official pricing language | 10-second reference point | How to read it | Source |
|---|---|---|---|---|
| OpenAI | Sora 2: 720p $0.10/sec, 1080p $0.40/sec. Sora 2 Pro: 720p $0.30/sec, 1080p $1.00/sec. | 10 seconds of Sora 2 at 720p is about $1.00. 10 seconds of Sora 2 Pro at 720p is about $3.00. | OpenAI separates model tier and resolution clearly, so normalize both before comparing other vendors. | OpenAI pricing |
| Veo 3.1 Fast: 720p $0.08/sec, 1080p $0.10/sec. Veo 3.1 Quality: 720p $0.15/sec, 1080p $0.20/sec. | 10 seconds of Veo 3.1 Fast at 720p is about $0.80. 10 seconds at 1080p is about $1.00. | Google exposes both quality tier and resolution, which makes side-by-side budgeting straightforward. | Gemini API pricing | |
| Runway | Gen-4 Turbo is billed at 5 credits/sec, and API credits are sold at $0.01 each. | 10 seconds of Gen-4 Turbo is about 50 credits, or about $0.50 before any other workflow costs. | This is the clearest example of why credits still need to be translated back into cash before you compare them to per-second APIs. | Runway API pricing |
If a vendor adds audio, premium motion, or enterprise-only options on top of the base tier, treat those as separate comparisons rather than hiding them inside one blended average.
How to normalize per-second pricing, credits, and plans
A pricing page becomes much easier to read once you classify the pricing language before you judge the number. The table below is a practical interpretation guide.
How to read the three most common AI video pricing languages
| Pricing language | Best way to normalize it | When to stay cautious | Reference source |
|---|---|---|---|
| Public per-second tiers | Multiply one fixed task, such as 10 seconds at 720p or 1080p, and keep the exact quality tier constant. | Do not mix a premium tier from one vendor with a fast tier from another and call it a fair comparison. | OpenAI or Google pricing docs |
| Credits per second or per render | Convert credits back to cash first, then compare the same deliverable cost against public per-second APIs. | If the vendor hides resolution, audio, or mode differences behind credits, compare by output fit rather than fake precision. | Runway API pricing |
| Plan or bundle packaging | Estimate how many clips you can realistically produce in one month, then divide the plan price by usable output volume. | Do not force a dollar-per-second number if the plan page does not expose enough technical detail to support it. | Use the vendor's current public plan page |
The goal is not to flatten every vendor into one fake universal metric. The goal is to compare the same job with the least distortion possible.
FAQ
Why is AI video API pricing so hard to compare?
Because vendors expose different billing languages for the same underlying workload. One page may show dollars per second, another credits per second, and another a plan with no clean clip-level breakdown.
How do I compare per-second pricing with credits pricing?
Pick one real task first, such as a 10-second 720p clip. Then convert the credits-based offer back into cash cost for that exact job before comparing it to a vendor that lists dollars per second directly.
Should I compare plan-based creator pricing to API pricing?
Only when the plan page exposes enough detail to estimate usable monthly output. If it does not, compare by likely clips per month or by workflow fit instead of forcing a fake dollar-per-second number.
Which official pricing pages are easiest to normalize today?
OpenAI, Google, and Runway currently expose some of the clearest public AI video pricing signals because they publish enough information to translate cost back into a single clip-level task.