Computing Power is Productivity: The GPU Cost Dilemma and Token Industrialization Breakthrough in AI Manhua Drama Production

I. From Trend to Track: Market Explosion and Industry Landscape of AI Manhua Dramas

The year 2025 can be called the "first year" of AI manhua dramas in China. During this year, manhua dramas evolved from a niche category on short-video platforms into an independent strategic sector, and the deep penetration of AI technology brought about a generational leap in content production. Data from iiMedia Research shows that the market size of animated micro-dramas in China reached 18.98 billion yuan in 2025, a year-on-year surge of 276.3%, and is expected to exceed 85 billion yuan by 2030. Statistics from DataEye Research confirm this trend: the manhua drama market was approximately 16.8 billion yuan in 2025 and is projected to surpass 24 billion yuan in 2026, a year-on-year growth of over 40%.

Behind this market explosion is a dual resonance of AI technology and the creative ecosystem. Platforms such as Douyin, Kuaishou, Hongguo, Tencent Animation, and iQiyi have entered the arena aggressively, providing increasing levels of traffic support and capital incentives. On the supply side, expansion has been exponential: Douyin hosted 37,583 native manhua dramas in 2025, with over 30,000 launched in the second half of the year alone. On the technology front, the iteration pace of major players' models has been unprecedentedly intense — ByteDance's Seedance 2.0 achieved "one-click generation of 15-second videos," Kuaishou's Kling AI reached an ARR of $500 million, and models from Alibaba Wan2.6, Zhipu, and others continued to upgrade.

The capital side has also accelerated its entry. In the first half of 2026, Zhipu AI and MiniMax went public on the Hong Kong Stock Exchange; Shengshu Technology (Vidu) and Aishi Technology (PixVerse), backed by Alibaba, are rumored to have IPO plans within the year; and Wondershare is pushing for a dual "A+H" listing. The capitalization process of AI video companies has clearly accelerated. It can be foreseen that in the next two to three years, AI manhua dramas will move rapidly from "wild growth" into an era of precision cultivation.

II. The Full Workflow of AI Manhua Drama Production: How AIGC Reconstructs the Creative Chain

Traditional manhua drama production — from script to final video — typically requires a team of dozens of people across multiple roles such as writers, illustrators, animators, and voice actors, and producing three to five episodes a month is already considered efficient. AI technology is reshaping all of this.

The current automated production process for AI manhua dramas can be roughly divided into the following core steps: script generation, character asset generation, storyboard breakdown, image generation and animation synthesis, and dubbing/editing with multi-format output. In this chain, large language models (LLMs) transform novels or original story outlines into structured, episode-by-episode scripts incorporating scene descriptions and character dialogues; image generation models (e.g., Stable Diffusion, Flux) rely on basic assets like character orthographic views to maintain character consistency across storyboards using feature-locking techniques; video generation models (e.g., Seedance, Kling, Sora) convert keyframes into dynamic video clips; and finally, intelligent editing systems combine audio and video and produce multiple output formats.

Compared to the traditional 3D animation cost of 2,000–5,000 yuan per minute, AI manhua drama production can now drop to 400–1,000 yuan per minute — just a fraction to a few hundredths of the traditional cost. Platforms like Chengxing Dream Factory, through multi-agent collaboration and multimodal large model integration, have compressed the production cycle of a single episode from one month to three days, reducing costs by 60%–90%. Wondershare's Wanxing Theater has achieved a closed loop of AI-powered full-process production, covering original scripts, novel adaptations, character generation, intelligent storyboarding, and post-editing, attracting nearly ten thousand dramas to the platform, with some creators producing "one drama per person per day." 360's industrial-grade AI manhua platform, "Nanomanhua Assembly Line," has further compressed single-episode production time to 30 minutes to one hour, with a material generation success rate exceeding 90%.

An interesting trend is that manhua dramas are transforming from traditional "content creation" into a "computing-intensive industry." As industry observers have pointed out, AI video generation typically accounts for only 10%–20% of the total material in a manhua drama production process, but the real drivers of computing consumption are the non-model steps — image generation, frame expansion, video compositing, dubbing, editing, and batch output. If these high-frequency, small-batch tasks are all handled through cloud APIs, costs will quickly spiral out of control.

III. The Hard Constraint of Computing Power: The Ceiling Effect of GPU Costs

If AI manhua drama production has already achieved initial "cost reduction and efficiency improvement," then computing power cost is becoming a new threshold lying across the industry.

From the perspective of token-based pricing, the cost of calling AI video generation models follows a clear formula. Take Huawei Cloud's Wan2.2 video generation model: the token consumption for video is roughly equal to (width × height × frame rate × duration) / 256. A 720p, 16 fps, 5-second video consumes about 288,000 tokens, costing about 1.44 yuan. ByteDance's Seedance 2.0 adopts a dual-track pricing model: the editing mode (with video input) costs 28 yuan per million tokens, while the pure generation mode (without video input) costs 46 yuan per million tokens. Generating a 15-second video consumes about 308,880 tokens, costing about 14.21 yuan, or nearly 1 yuan per second.

These are just the costs for the video generation phase. For a manhua drama episode longer than one minute, dozens or even hundreds of image generations, multiple segments of speech synthesis, and automated editing are required. A mature manhua drama account may produce 5–20 pieces of content per day, requiring 20–50 image generations and multiple video generations. If all these processes rely entirely on cloud APIs, the cost will increase exponentially.

An even bigger challenge is the continued rise in computing power prices. In April 2026, major domestic cloud providers collectively raised prices for AI computing services, with some core model price increases exceeding fourfold — Tencent's Hunyuan model saw an input price increase of 463%, Alibaba Cloud adjusted prices three times within a month, Baidu Smart Cloud raised prices by 5%–30%, and Zhipu AI raised prices for the third time in a year by 10%. These computing cost increases directly affect downstream creative teams: Yang Hao, founder of Heya Manhua Drama, revealed that the computing cost for his team's 1-minute AI manhua drama jumped from under 100 yuan to nearly 200 yuan, forcing the team to incorporate token usage into performance reviews and even shift work hours to 3 a.m. to avoid peak times. It is understood that the weekly compound growth rate of AI point consumption at some leading AI manhua drama platforms has reached as high as 63%. This not only reflects the rapid growth of market demand but also signals that computing cost is becoming a core bottleneck limiting large-scale expansion.

IV. Capital Influx and Future Opportunities: The Next Growth Pole for AI Manhua Dramas

Against the backdrop of continuously rising computing power costs, the industry landscape is rapidly reshaping. On the one hand, capital is pouring into the AI video sector at an unprecedented pace, and the revenue performance of leading model providers is eye-catching — Seedance 2.0, just three months after its release, generated over 1 billion yuan in revenue in May, corresponding to an ARR of over $\mathrm{<span\; style="color:\#FFFFFF;">1.7</span>}\mathrm{<span\; style="color:\#FFFFFF;">b</span>}\mathrm{<span\; style="color:\#FFFFFF;">i</span>}\mathrm{<span\; style="color:\#FFFFFF;">l</span>}\mathrm{<span\; style="color:\#FFFFFF;">l</span>}\mathrm{<span\; style="color:\#FFFFFF;">i</span>}\mathrm{<span\; style="color:\#FFFFFF;">o</span>}\mathrm{<span\; style="color:\#FFFFFF;">n</span>}\mathrm{<span\; style="color:\#FFFFFF;">.</span>}\mathrm{<span\; style="color:\#FFFFFF;">K</span>}\mathrm{<span\; style="color:\#FFFFFF;">l</span>}\mathrm{<span\; style="color:\#FFFFFF;">i</span>}\mathrm{<span\; style="color:\#FFFFFF;">n</span>}{\mathrm{<span\; style="color:\#FFFFFF;">g</span>}}^{\mathrm{<span\; style="color:\#FFFFFF;">\prime </span>}}\mathrm{<span\; style="color:\#FFFFFF;">s</span>}\mathrm{<span\; style="color:\#FFFFFF;">M</span>}\mathrm{<span\; style="color:\#FFFFFF;">a</span>}\mathrm{<span\; style="color:\#FFFFFF;">r</span>}\mathrm{<span\; style="color:\#FFFFFF;">c</span>}\mathrm{<span\; style="color:\#FFFFFF;">h</span>}\mathrm{<span\; style="color:\#FFFFFF;">A</span>}\mathrm{<span\; style="color:\#FFFFFF;">R</span>}\mathrm{<span\; style="color:\#FFFFFF;">R</span>}\mathrm{<span\; style="color:\#FFFFFF;">r</span>}\mathrm{<span\; style="color:\#FFFFFF;">e</span>}\mathrm{<span\; style="color:\#FFFFFF;">a</span>}\mathrm{<span\; style="color:\#FFFFFF;">c</span>}\mathrm{<span\; style="color:\#FFFFFF;">h</span>}\mathrm{<span\; style="color:\#FFFFFF;">e</span>}\mathrm{<span\; style="color:\#FFFFFF;">d</span>}$500 million, doubling from $240 million in December last year.

On the other hand, due to high computing consumption, Sora's strategic pace has been adjusted because of difficulties in commercial implementation, and star companies like Runway and Pika are also grappling with the tension between growth and costs. Small and medium-sized creative teams are bearing the brunt of rising computing power costs — a nine-person manhua drama team failed to fulfill contracts due to a broken funding chain. Industry divergence is inevitable. The heavy pressure of computing costs is accelerating industry consolidation and driving AI applications toward higher-value-added areas.

Looking ahead, the industrial-scale production of AI manhua dramas will not remain at the level of "gacha-style" generation; it requires mature computing infrastructure as a foundation. More and more content teams are exploring a "hybrid deployment" model: using cloud-based large models for critical shots, while handling high-frequency tasks such as image generation, video processing, automated editing, and multi-format output with local GPU computing power. From the perspective of industry evolution, the future infrastructure architecture for AI manhua dramas may take a three-tier structure of "cloud large model center — city computing nodes — content studios." Whoever possesses stable, low-cost computing supply will seize the advantage in this competition.

V. Guardian of Computing Power: omniyq.com Provides a Solid Foundation for AI Manhua Drama Production

In this computing power race for AI manhua dramas, producers' anxiety is far from resolved. Whether it is the rising token consumption or the frequent price adjustments by cloud providers, both repeatedly remind us of the same truth: computing power is not a nice-to-have bonus but the essential fuel for content production. As more and more creators rush into the track, what really needs to be built is not an eye-catching "gacha magic" but a low-cost, high-efficiency, queue-free computing foundation. This is exactly where omniyq.com focuses — with its leading GPU computing resources and optimized token scheduling strategy, it provides a solid foundation for the full-chain production of AI manhua dramas. From high-concurrency script generation and character asset drawing to batch video compositing and multi-format output, it significantly reduces teams' computing waiting costs and API call expenses, allowing creators to truly overcome "computing anxiety" and devote all their energy back to the stories and visuals.