How Much Does AI Video Production Cost? Pricing Logic and Budget Evaluation for 2026 Projects
"How much does it cost to make an AI video?"
This is a question we have encountered with increasing frequency over the past year.
What is interesting, however, is that different brands often have completely different interpretations of "AI video cost." We have even had clients ask whether a project could be billed based on API tokens. While this is certainly not the standard procurement method for mature brands, it reflects an emerging perception: since AI models charge based on credits, API calls, or generation time, shouldn't AI video simply equal "tool cost + a little manual execution fee"?
That is precisely where the misconception lies.
AI video generation costs can be calculated by model usage, but commercial video value cannot be measured by token consumption.
Today, the price spectrum for AI video production in the open market is vast. On professional video service platforms, listing prices range from 20 RMB to 100, 200 RMB per second or higher. Meanwhile, public pricing disclosed by commercial AI video creator teams reaches 100–500 RMB per second. Once projects enter corporate or institutional procurement, actual contract amounts scale to tens of thousands or even hundreds of thousands of RMB. While all of these are labeled "AI video production," they represent fundamentally different services.
Searching for "how much AI video costs" without context yields a market of prices that are impossible to compare directly.
To make sense of it, brands must first deconstruct what that money actually buys.
Models Are Cheap—So Why Can AI Video Still Be Expensive?
Let's first look at the underlying generation cost.
According to Vidu's current public API pricing, video generation fees across different models, resolutions, and modes can be as low as ~$0.02 per generated second, while high-spec modes reach over $0.10 per generated second. Runway's Gen-4.5 bills at 12 credits per generated second, which translates to roughly $0.12 per generated second when purchasing additional credits at $0.01/credit.
From this perspective, it is understandable why brands assume AI video should be extremely inexpensive.
However, these numbers reflect a single model generation pass, not a final, market-ready commercial video.
A 10-second shot might hit the target on the first try, or it might require dozens of iterations to refine character consistency, action, product details, camera motion, or overall composition. That is to say nothing of the preceding creative concept, scriptwriting, and storyboarding, or the subsequent editing, color grading, sound design, motion graphics, formatting, and client review cycles.
Thus, model fees function much like raw equipment rentals in traditional film production.
Camera rental is not the price of a commercial, nor is studio hourly rent the price of a TVC. Similarly, the cost of tokens, credits, or generation seconds should not be directly equated with the price of a commercial AI video project.
What Brands Are Actually Buying: Guaranteed Results
This distinction has become increasingly clear through our own commercial practice at FansAI.
When FansAI created the "Activate the Classic" series for Yili Satine—achieving zero live-action shooting with a 10-day turnaround—our core insight was clear: what brand-grade AI video truly requires is not purchasing a given count of generations, but organizing creativity, brand standards, and AI's inherent unpredictability into a final result that the brand can confidently use within a strict deadline.
Whether a video is fully generated by AI does not automatically determine whether it should be cheap or expensive.
If a brand arrives with a complete script, storyboard, and visual reference, needing a vendor to execute specific shots, the work leans heavily toward "execution." Conversely, if a project starts from a blank brief requiring creative development, visual concept design, and character/product asset creation, the cost structure is entirely different.
Add IP licensing, real-person likenesses, multi-stakeholder approvals, global localization, hybrid live-action shooting, or tight marketing windows into the mix, and the brand is no longer buying "video generation"—it is purchasing guaranteed project delivery.
This explains why two vendors quoting on the exact same 60-second video brief can offer wildly different prices without either being inherently inaccurate.
What Does 20 to 200 RMB per Second Actually Buy?
Low-cost AI video services exist in the market, and low cost does not inherently imply low quality.
On open freelance platforms like VJshi, listing prices for AI video generation range from 20, 30, to 100 or 200 RMB per second. Certain services at the 100–200 RMB/s mark explicitly include basic script adaptation, generation, post-production, and limited revisions. Meanwhile, the AI film team behind Huo Qubing publicly cited commercial video rates of 100–500 RMB/s, narrative film rates of 100–300 RMB/s, and CG/3D-heavy production at 300–1,000 RMB/s.
These numbers do not define a single "fair market price." Rather, they illustrate how AI is driving clear product tiering across video production.
When requirements are highly standardized, low unit pricing is completely viable. For instance: when the script and visual direction are pre-set; when character or product consistency demands are minimal; when shot lengths are short and suited to automated model output; when revision cycles are strictly capped; and when the brand internalizes creative management and cross-departmental coordination.
In such cases, vendors are selling standardized AI production capacity.
As foundational models evolve, prices at this execution tier will likely drop further. Similar dynamics are visible in the AI short drama sector, where small teams of 3–5 creators produce 80–100 episodes per month at compute costs averaging ~100 RMB per minute. There too, unit pricing reflects high-volume automated throughput rather than corporate brand delivery environments.
When seeing low per-second rates, brands should ask:
What is actually included in that baseline rate?
Three Pricing Models for AI Video in Today's Market
Rather than offering an arbitrary "industry average," we categorize current market pricing into three distinct operational tiers:
| Project Tier | Typical Scope & Demand | Public Pricing References | Primary Cost Drivers |
|---|---|---|---|
| Lightweight Execution | Existing script/direction; short clips, batch social assets, basic generation & editing | Public freelance platforms list reference rates from tens to hundreds of RMB per second | Generation volume, shot count, revision caps, post-production complexity |
| Custom Brand Production | Creative conceptualization, script, visual design, asset consistency, complete end-to-end video | Institutional procurement records show project packages ranging from tens of thousands to ~100,000 RMB | Creative strategy, staffing, character/product consistency, fine finishing, revision workflows |
| Complex Full-Service | End-to-end strategy and concept through live-action hybrid, IP integration, multi-market & multi-platform delivery | Custom project pricing starting at 100,000+ RMB, where per-second metrics become irrelevant | Strategic positioning, creative direction, pipeline management, IP rights, risk mitigation, delivery guarantees |
Note: This table reflects observed market structures rather than a standardized rate card.
Institutional procurement records highlight this shift toward project-based pricing. For example, Shanghai International Studies University awarded a "Digital Host + AIGC Video Production" contract at 49,000 RMB; Harbin Institute of Technology commissioned an AIGC video project for ~89,600 RMB; and Beihang University procured an AIGC short video production package for 100,000 RMB. While each project scope varied, these transactions demonstrate that institutional buyers procure AI video on a holistic project basis rather than per-second rates.
Similarly, a public tender published by Nanfang Daily Media Group budgeted 160,000 RMB for an AIGC promotional video series (at least 5 episodes, 3–5 minutes each), encompassing scripting, storyboarding, live-action shooting, AI animation, editing, bilingual voiceovers, project management, and copyright indemnification. At this level, evaluating costs per minute fails to capture the scope of delivery.
Lumping compute units, execution services, and full commercial projects under the single label of "AIGC video production" is the root cause of pricing confusion.
What Really Drives the Price Difference in AI Video?
Price divergence in commercial projects rarely stems from Vendor A paying more for API credits than Vendor B. It stems from the scope of responsibility assigned to each partner:
- Creative Origin: Executing an existing concept vs. building creative strategy, consumer positioning, and storytelling from scratch requires fundamentally different team structures and cost foundations.
- Control Rigor: Atmospheric, abstract video clips allow high flexibility, whereas commercial work requiring strict adherence to real actors, IP characters, packaging specs, logo placements, and narrative continuity demands complex workflow design.
- Tolerance for Variance: AI excels at generating random possibilities, but commercial branding demands deterministic outcomes. Minimizing variance requires intensive manual retouching, frame-by-frame cleanup, and strict quality control.
- Approval Complexity: Projects involving internal legal teams, IP licensors, event organizers, or cross-border stakeholders require robust account management beyond mere production.
- Deliverable Scope: Producing a single 16:9 master export involves far less operational overhead than delivering vertical cuts, short social edits, textless masters, digital signage formats, and localized multi-language packages.
- Accountability & Risk: Simple execution contracts sell labor hours or generation passes; full-service engagements contractually guarantee that a campaign launches on time without legal or brand safety issues.
AI Reduces Production Overhead, Not Content Value
AI undeniably unlocks significant cost efficiencies.
Visuals that previously required physical set builds, location shoots, large cast ensembles, and complex traditional visual effects can now be developed directly within generative workflows. Visual testing occurs faster, failure costs for complex scenes drop, and multi-version iteration becomes seamless.
However, AI does not eliminate strategy, creative judgment, directorial vision, brand governance, copyright clearance, or project management.
Instead, it reallocates where budget is spent across the production pipeline.
This is why certain AI tasks can be fulfilled at low price points, while high-stakes brand campaigns remain aligned with traditional commercial video budgets.
For example, in FansAI's Winter Olympics campaign for TCL, we combined local live-action shooting in Italy with domestic AIGC workflows. The entire project was delivered in 14 days, including on-location filming, AIGC integration, and multi-stakeholder approval rounds. Here, AI expanded visual scope and accelerated throughput rather than replacing the production ecosystem entirely.
This highlights the true financial benefit of AI for brands:
AI significantly lowers the marginal cost of realizing complex visuals, but commercial campaigns ultimately pay for creative judgment, brand control, and delivery certainty.
How Brands Should Compare AI Video Quotes
When reviewing AI video quotes, brands should temporarily set aside the total cost figure and evaluate scope alignment first.
If Quote A covers creative strategy, scripting, storyboarding, asset generation, fine post-production, sound design, multiple revision rounds, copyright clearance, and localized adaptations, while Quote B only covers rendering scenes based on an existing script, comparing their final prices directly is misleading.
Brands should systematically verify delivery boundaries:
- Who owns creative strategy and script writing?
- Who is responsible for maintaining character and product consistency?
- How many rounds of revisions are included?
- What is the backup workflow if AI models fail to generate a specific action?
- Who handles copyright clearances for music, fonts, and assets?
- How many aspect ratios and final versions are included?
- Who guarantees project delivery against fixed launch deadlines?
Once these boundaries are established, financial comparisons become meaningful.
Rather than asking "How much does a 60-second video cost?", buyers should ask "What level of complexity and risk does this quote resolve for our brand?"
Conclusion: No Universal Price, But Clearer Pricing Logic
The AI video landscape in 2026 continues to evolve rapidly. As foundational models become cheaper and tools become more accessible, low-cost execution services will expand. Concurrently, enterprise brands will increasingly deploy AI across major TVCs, IP campaigns, and global commercial initiatives.
These two market segments coexist naturally—much as smartphone cameras did not eliminate cinema production, and accessible design software did not flatten global brand identity budgets.
AI transforms how video is produced, but brands ultimately purchase tiered capabilities based on strategic need. Defining budget is less about calculating video duration and more about identifying how much risk your brand can afford.