Guides → Playground & Guide → Video Generation Cost - What You Actually Pay Per Second
Meet Priya Nair. Growth marketer producing short-form social video at scale. "Every model quotes a different per-second rate and some include audio, some don't. What's my real monthly bill for 1,000 clips?"
🔥 Need 1,000 eight-second clips a month for paid social. Finance wants the number before we commit to a model.
Video bills by the second, and the rate spread is about 15x. The same 8-second 1080p clip is roughly $0.40 on Veo Lite-class models and $3.20 on Veo 3.1 standard. Two structural factors move the rate: resolution (4K runs 2-3x the 1080p price where offered) and audio - Veo and Sora bundle synchronized sound, while Runway, Kling base, Seedance, Wan, Luma and Pika do not, so adding audio costs roughly $0.03/sec on top.
Priya's 1,000 eight-second clips/month = 8,000 seconds. On Veo 3.1 (1080p, audio): ~$3,200/mo. On Sora 2 (720p): ~$800/mo. On Runway Gen-4 Turbo + audio add-on: ~$640/mo. On Veo 3.1 Lite: ~$400/mo. For paid-social hooks the quality gap is far smaller than the 8x price gap, so the model choice dominates the budget.
One availability caveat. OpenAI has announced the Sora API will be discontinued (around late 2026). Sora is priced attractively, but for a long-running pipeline plan a migration path to Veo, Kling, or Runway. The calculator flags Sora with a sunset badge so the risk is visible while you compare.
Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.
Estimate the monthly cost of generating video — by the second, across Veo, Sora, Runway, Kling and more. Prices live from the LiteLLM catalog.
💡Video bills per second, so two levers dominate: clip length × volume (total seconds) and the model/resolution rate (a ≈15× spread). Audio adds ~$0.03/sec on models that don’t bundle it.
Same calculator, three team sizes. Click a tab to see how the numbers shift.
Short hooks for paid social. Quality gap vs premium is small at this length, so a budget model wins. 1,000 x 8s = 8,000s, ~$400/mo on Veo Lite.
Healthy range: <$600/mo for 1,000 clips
Story-driven clips with bundled audio and strong physics. Veo 3.1 standard. Lower volume, higher per-clip value. 200 x 10s = 2,000s, ~$800/mo.
Healthy range: $600-700/mo (premium justified)
Runway Gen-4 Turbo at $0.05/sec + $0.03/sec audio = $0.08/sec all-in. 8,000s = ~$640/mo. Check whether bundled-audio Veo is cheaper once sound is required.
Healthy range: Compare all-in vs a bundled-audio model
Same calculator, different applications. Sizes above, workloads here. Pick the one that looks like yours.
Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers, then hit "Try this scenario" to load it into the calculator above.
Per-SKU demo clips with character/subject consistency. Kling 3.0 (bundled audio). 2,000 x 10s = 20,000s, ~$2K/mo.
Healthy range: $1.5K-2.5K/mo
Massive ad variant volume. Seedance 2.0 Fast at $0.09/sec is the budget production king; add audio for voiceover. 10,000 x 6s = 60,000s.
Healthy range: Cheapest production-quality per second
Longer scenes where clip length dominates. Veo 3.1 Fast balances cost and quality with bundled audio. 300 x 30s = 9,000s, ~$1.35K/mo.
Healthy range: $1.3K-1.4K/mo
Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.
🚀 Open the full calculator →Video-gen bills per second with a ~15x spread. Real monthly math across Veo 3.1, Sora 2, Runway, Kling, Seedance - resolution, clip length, and audio add-ons included.
Each input shapes the cost. Click an input on the calculator to set it. The explanations below match the live calculator field by field.
| Input | Default | Typical ballparks |
|---|---|---|
modelKey
|
gemini/veo-3.1-generate-001 | — |
resolution
|
1080p | — |
secondsPerClip
moves the needle
|
8 | — |
clipsPerMonth
moves the needle
|
1,000 | — |
audioAddOn
|
false | — |
Ballparks are broad industry starting points (sourced ranges; * = rough estimate) — your result gets more accurate as you replace them with measured numbers.
Try them live in the calculator;
API & agent users get the same data from the MCP resource aicost://input-reference/video-generation-cost.
Cost per clip is the number to feel. Per-second rates are abstract; $3.20 vs $0.40 per finished clip is the decision. Multiply by your monthly volume to see the bill.
Audio is a real line item. On non-bundled models, adding sound is ~$0.03/sec - for an 8s clip that's $0.24, which can rival the video cost itself on the cheapest models. If you need audio, a bundled-audio model (Veo) may be cheaper all-in.
Resolution multiplies fast. 4K at 2-3x the 1080p rate compounds across thousands of clips. Only render 4K where the delivery surface needs it.
Honest limitations. Every model is wrong; some are useful. Where this one falls short:
For these, use: Image Generation Cost for stills. Multimodal RAG Stack for full pipelines.
Cost isn't the only dimension. Click any constraint to see how recommendations change.
Per-second rate is the dominant lever - up to 15x. Pick the cheapest model that clears your quality bar, and remember bundled-audio models can be cheaper all-in than a cheap model plus an audio add-on.
Most providers grant commercial use, but terms differ. Sora's announced API discontinuation makes it a poor base for long-running production despite attractive pricing.
If users supply reference media for image-to-video, treat it as sensitive and use a no-train tier.
Video generation is slow and bursty; design for asynchronous jobs with progress UI, not real-time interaction.
Given fast price moves and Sora's sunset, a routing layer that lets you swap models cheaply is worth building.
Instrument cost per usable clip and avoid over-rendering resolution; both compound across thousands of clips.
Tradeoff analysis is where most AI projects go sideways. Talk to a CFO-grade AI cost analyst →
The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.
Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →
You have seen the shape of it. Open the calculator with your model, your tokens, your volume.
🚀 Open the full calculator →Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.
Last-verified date is the most recent successful daily snapshot
(aicost_pricing_snapshots) or, when no snapshot exists yet,
the latest successful crawler run (aicost_crawler_runs).
10 of 10
vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.)
are not listed.
Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).
| Vendor / Model | Field | Why it’s inferred |
|---|---|---|
| Anthropic — Claude Sonnet 4.6 | cachedInput |
Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier. |
| Anthropic — Claude Sonnet 4.5 | cachedInput |
Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6. |
| Anthropic — Claude Sonnet 4.5 | batchInput |
Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount. |
| Anthropic — Claude Sonnet 4.5 | batchOutput |
Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount. |
| Anthropic — Claude Haiku 4.5 | cachedInput |
Derived at 10% of input rate — Anthropic 90% cache-hit discount convention. |
| OpenAI — GPT-5.4 Mini | cachedInput |
Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier. |
| OpenAI — GPT-5.4 Nano | cachedInput |
Derived at 10% of input — OpenAI 90% cache-hit convention. |
| OpenAI — GPT-5.4 Nano | batchInput |
Derived at 50% of input — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Nano | batchOutput |
Derived at 50% of output — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Pro | cachedInput |
Derived at 10% of input — OpenAI 90% cache-hit convention. |
| OpenAI — GPT-5.4 Pro | batchInput |
Derived at 50% of input — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Pro | batchOutput |
Derived at 50% of output — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.2 | cachedInput |
Derived at 10% of input; no residency uplift. |
| OpenAI — GPT-5.2 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.2 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 | cachedInput |
Derived at 10% of input. |
| OpenAI — GPT-5 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.5 Pro | cachedInput |
Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention. |
| OpenAI — GPT-5.5 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.5 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.2 Pro | cachedInput |
Derived at 10% of input — pro-tier convention. |
| OpenAI — GPT-5.2 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.2 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.1 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.1 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 Nano | cachedInput |
Derived at 10% of input. |
| OpenAI — GPT-5 Nano | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 Nano | batchOutput |
Derived at 50% of output. |
| Google — Gemini 3 Flash | cachedInput |
Derived at 10% of input — Google caching discount convention ~90%. |
| Google — Gemini 3.1 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 3.1 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 3.1 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.5 Pro | cachedInput |
Derived at 10% of input. |
| Google — Gemini 2.5 Flash | cachedInput |
Derived at 10% of input. |
| Google — Gemini 2.5 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 2.5 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.5 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash | cachedInput |
Derived at 25% of input per Google 2.0 family caching rates. |
| Google — Gemini 2.0 Flash | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 2.0 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| xAI — Grok 4 (legacy) | cachedInput |
Extrapolated at 25% of base. |
Pricing is cross-verified against the
LiteLLM community registry
when available. Daily snapshots are kept in aicost_pricing_snapshots;
every change is logged to aicost_price_changelog with old & new
values for full audit trail. Read the full methodology →