Benchmark of Domestic AI Video Companies: Which 6 Technical Capabilities Should Brand Procurement Truly Evaluate?
When brand procurement enters the written technical qualification evaluation stage for an AI content vendor, they often encounter a new problem:
Almost all vendors' technical capabilities look virtually identical on paper.
In client slide decks, you will see Kling, Seedance, Midjourney, ComfyUI, LoRA, and Runway. Some stress self-developed workflows, others highlight multi-model orchestration; some claim to have built autonomous Agents, while others simply display a full page of model logos.
Two years ago, these items could indeed quickly differentiate whether a team understood AIGC.
By September 2026, however, relying on these labels alone makes it increasingly difficult to judge real technical capabilities.
This is because the infrastructure of AI video itself has undergone massive transformations. Seedance has reached version 2.5; Kling has evolved to 3.0 supporting MCP and CLI; Wan has updated to 3.0; MiniMax has launched H3; and Vidu has introduced its Q3 series tailored for advertising and narrative scenes. AI video tools are advancing from "single-shot generation" into multimodal referencing, native audio-visual output, editing, long narrative control, and automated workflows.
Consequently, the real core question for procurement has changed:
If every vendor can call these underlying models, why are their final deliverables still worlds apart?
The answer usually does not lie within the models themselves.
By 2026, "Which Models You Use" Has Become the Easiest Capability to Replicate
Let's first look at the current technological baseline.
Seedance 2.5, released by ByteDance in late July, supports up to 30-second single-shot audio-video generation, multi-round extension, multimodal references, and video editing, while introducing claymation control, green-screen editing, and other professional production controls.
Kling 3.0 unifies text, image, audio, and video into a multimodal architecture, covering text-to-video, image-to-video, reference-to-video, and video editing. It subsequently introduced native 4K and 3.0 Turbo, while officially launching MCP and CLI integration to enable AI Agents to batch-schedule Kling for content production.
As of September, Alibaba Cloud's Wan 3.0 handles text, image, video, and audio multimodal references within a single model for up to 30 seconds, covering first-frame, first-and-last frame, reference generation, video editing, and extension. Its documentation supports feeding multiple reference assets into a single generation process simultaneously.
MiniMax H3 understands unified contexts across text, image, video, and audio, generating up to 15 seconds at 2K resolution with native stereo audio. Its public feature list specifically highlights text and brand element rendering alongside V2V motion transfer.
Vidu Q3 introduced viduq3-ad specifically tailored for advertising scenarios, baking reference consistency, audio-visual sync, intelligent storyboarding, and "one-click ad creation" directly into product capabilities.
This reveals a very concrete reality:
A vendor writing "We use Seedance, Kling, Wan, and Vidu" in their pitch deck no longer proves they possess the engineering capabilities behind those models.
Calling an API and organizing models into a commercial production system are two completely different things.
This is precisely where brand procurement teams get confused most easily when evaluating AI video suppliers.
Our Own Projects Also Went Through This Shift
FansAI encountered this challenge early on across past brand projects.
FansAI produced an all-AIGC brand TVC for Yili Satine—zero live shooting, completed in 10 days. The production workflow at the time included scene generation, LoRA character control, multi-angle assets for product packaging, dynamic generation, frame-by-frame QC, and DaVinci color grading. To be clear: this was the actual tech stack used at the time of execution, and it does not mean we would naively copy the exact same set of models today.
What truly endured was not a specific model name, but a core engineering decision:
Identify which elements cannot be random first, and then decide what technology to deploy for each stage.
Character identities cannot drift, product packaging cannot distort, and brand tone cannot fluctuate due to model randomness. Therefore, these elements must be assetized, strictly controlled, and subjected to human quality assurance before final delivery.
In the YOYO Naked-Eye 3D billboard project, the same logic translated into character asset precision, multi-character temporal consistency, and industrial-grade screen post-production. The pipeline utilized high-precision static assets, Seedance 2.0 dynamic generation, and pixel-level post-refinement.
Thus, a much more effective question for procurement than "Which models are you proficient in?" is:
What is the item most prone to losing control in this project, and how do you plan to control it?
Only when a vendor answers this question clearly does real technical due diligence begin.
Capability #1: Presence of Model Routing, Not Just a Model Checklist
By 2026, AI video models exhibit an increasingly distinct division of labor.
Seedance 2.5 emphasizes long narrative, reference, and editing; Kling 3.0 reinforces 4K, all-modality, and Agent scheduling; Wan 3.0 unifies multimodal asset referencing, generation, editing, and extension in a single model; MiniMax H3 excels in brand text rendering, audio, and V2V; while Vidu builds dedicated models specifically for commercial ads and vertical storylines.
This means mature AI video production no longer sounds like:
"Our company primarily uses Kling."
Instead, it sounds like:
"Why this shot uses Kling, why this character relies on reference-to-video, why this product shot requires asset pre-building, and why this complex motion switches to a different model system entirely."
The true first layer of technical competence is Model Routing Capability.
Procurement should assess three things during technical audits: whether the vendor can articulate the boundary conditions of different models; whether fallback alternatives exist; and whether existing production methods can seamlessly migrate when underlying models update.
Because the best model available today may well be superseded in six months.
If a company's core barrier relies solely on being "exceptionally good at prompting a specific model," its technical moat is extremely fragile.
Capability #2: Brand Asset Controllability vs. Re-generating Every Shot
For casual AI video, a slight shift in character appearance or a changing cup shape might not ruin the viewing experience.
Brand content is different.
Logos, packaging, IP characters, product structures, subject appearances, and specific brand brand colors are non-negotiable assets that cannot fluctuate randomly.
Therefore, procurement must evaluate whether the vendor has the capability to transform these elements from simple "reference pictures" into reusable brand assets.
These capabilities are increasingly built into tools. Wan 3.0 supports multimodal references; Vidu Q3 emphasizes multi-camera subject consistency; Filmora.TV publicly advocates establishing three-view orthographic and multi-angle references for characters and products, linking them via workflow nodes to maintain consistency across shots.
However, procurement must distinguish:
Tool-supported consistency does not guarantee brand-level consistency from the vendor.
The real test is not asking "Can you maintain character consistency?" but asking them to demonstrate:
The exact same product rendered under different shot scales and lighting conditions; the stability of a character across multiple cutaway shots; and the exact mechanism used to rectify errors when Logos, packaging, or IP characters deform.
This is why we have consistently pre-built characters, products, and IPs as independent assets in past brand projects rather than relying entirely on unconstrained generation.
Capability #3: Complex Motion & Temporal Control: Deterministic Control vs. Random Rerolling
In the single-image era, AI's weakest link was hands.
In the video era, the problem expands to the entire physical world.
Multi-person interactions, character-product contact, complex body dynamics, continuous performances, and spatial relationships across cuts are all prone to artifacting.
2026 models are rapidly bridging this gap. Seedance 2.5 reinforces professional camera movement and performance orchestration; Kling 3.0 emphasizes narrative logic and precise camera control; PixVerse C1 focuses on complex physics, realism, and multi-shot scene staging for film production.
Yet despite model upgrades, procurement must press further:
What does the vendor do when the model fails to generate the correct movement on the first try?
If the only answer is "reroll and re-generate," that is still gacha-style random production.
A mature technical framework can diagnose at which layer the error occurred: Is the motion reference inaccurate? Are end-frame constraints insufficient? Has the character asset failed? Should the shot be split? Or should this specific element not be handled by a generative model at all?
From a commercial delivery perspective, "controllability" does not mean achieving 100% first-pass generation accuracy.
It means knowing exactly where and how to fix it when an error occurs.
Capability #4: Standardized Workflow and Fallback Mechanisms
This is perhaps the single most critical criterion for procurement teams today.
Model companies themselves are moving directly into workflow management.
Following Kling's MCP and CLI releases, Agents can orchestrate models for batch production; Alibaba Cloud provides video generation, reference generation, and video editing directly via CLI; Filmora.TV integrates Brief parsing, scripting, storyboarding, image generation, editing, team review, and delivery into a unified AI-native workflow.
This demonstrates that industry competition has transcended pure model capability.
Professional production vendors require their own structured pipeline.
A pipeline does not necessarily mean a glossy proprietary UI backend. It can consist of internal node-based workflows, ComfyUI setups, custom scripts, APIs, and traditional post-production software.
What procurement must verify is:
Is the production process reproducible?
Can another creator complete the project if the lead editor changes? Will the project collapse if a model updates mid-way? Which steps are automated and which require human QC? Does a clear fallback path exist if generation fails? Are assets and versions systematically managed?
If a vendor's output quality depends entirely on a single star creator manually rerolling prompts, they possess individual talent, not organizational technical capability.
This is the layer most frequently overlooked during technical due diligence.
Capability #5: Bridging AI Raw Clips to Industrial-Grade Final Delivery
Many vendor demos stop at raw model outputs.
For brand projects, that is precisely where actual production begins.
Color grading, compositing, retouching, sound design, subtitling, multi-aspect ratio adaptation, 4K upscaling, large-screen fitting, localization, and multi-version exports must all pass through an industrial production pipeline.
This is why leading AI platforms are aligning with professional production standards. Kling provides native 4K output; Filmora.TV combines AI canvases with multi-track editing; PixVerse C1 explicitly targets film production rather than short clip generation.
Our YOYO Naked-Eye 3D project serves as a clear illustration: after AI dynamic generation was completed, preparing the video for an outdoor naked-eye 3D screen required complex keying, pixel-level frame repair, and color optimization tailored for high-brightness outdoor displays.
The TCL Winter Olympics project ran a dual track combining live shooting in Italy with domestic AIGC, completing multinational production and multi-language delivery within 14 days.
Therefore, when evaluating an AI video vendor, procurement should look beyond raw generation demos.
Focus on whether they have:
Commercial spots that actually went live, and what media platforms or rigorous delivery conditions those spots met.
Capability #6: Embedding Copyright, Data Security, and Content Governance into Technical Systems
This topic used to be treated purely as a legal compliance matter.
Today, it is an integral technical component.
China's Measures for Marking Content Generated or Synthesized by Artificial Intelligence (effective September 1, 2025) imposes strict explicit and implicit marking requirements, covering file metadata and platform distribution tracking mechanisms.
At the same time, brands frequently entrust unreleased products, ambassador materials, IP assets, and new packaging to AI vendors.
Procurement must ask far more than just:
"Will you infringe on copyrights?"
They must ask:
Where are assets uploaded? Are third-party models training on client data? How are brand assets managed internally? Who holds access permissions? Are generated results and source reference assets fully traceable?
Filmora.TV explicitly discloses that its platform does not use customer-uploaded materials or generated results for model training, but calls to third-party models remain subject to those respective providers' terms and privacy policies. This detail highlights that different models within the exact same workflow may carry different data privacy boundaries.
Hence, technical governance is not a copyright disclaimer attached at contract signing.
It is part of the system architecture itself.
Comparing Key September 2026 Tech Platforms: Distinct Strategic Focuses
Based on currently verifiable public technological capabilities, major platforms have established distinct focus areas. The table below is not a ranking of company strength or commercial execution, but a framework to help procurement understand underlying tech trajectories.
| Company / Platform | Key Representative Capabilities (As of Sept 2026) | What It Best Demonstrates |
|---|---|---|
| ByteDance Seedance 2.5 | Up to 30s, joint audio-video generation, multimodal reference, editing, claymation & green-screen control | Long narrative, reference control, and professional video generation capabilities |
| Kuaishou Kling 3.0 / 3.0 Turbo | All-modality, native audio, 4K, MCP, CLI, Agent batch scheduling | High-fidelity generation and automated workflow infrastructure |
| Alibaba Wan 3.0 | Up to 30s, multimodal reference, audio-video generation, editing & video extension | All-in-One multimodal generation with API/CLI integration |
| MiniMax H3 | 2K, 15s, native stereo, brand text rendering, V2V motion transfer | Brand content, unified multimodal generation, and motion transfer |
| Shengshu Tech Vidu Q3 | Reference consistency, audio-visual sync, Q3-Ad ad model, ad workflow automation | Vertical ad generation and scalable content production |
| Aishi Tech PixVerse | V6 multi-shot audio-video, C1 film production model, R1 real-time world model | Exploration into film-grade generation and interactive video |
| Wondershare Filmora.TV | Multi-model switching, integrated Brief-Script-Storyboard-Generation-Edit-Review-Delivery workflow | Productized capability to organize foundation models into professional workflows |
Reviewing this comparison reveals a crucial reality:
By 2026, the AI video industry no longer lacks capable models.
It is completely normal for an AI content vendor to access several of these models.
Procurement should never treat "We integrated Seedance 2.5" or "We can use Kling 3.0" as the finish line of vendor technical competence.
The real question is:
Once these tools enter your project pipeline, who is responsible for selecting, controlling, repairing, reviewing, and delivering the final output?
What Should Technical Benchmarking Truly Evaluate?
If we were conducting written technical qualification audits for brand procurement teams today, we would not award automatic extra points for "self-developed foundation models" or "number of models used."
Instead, evaluation should focus on six core deliverables:
Clear model routing logic, asset controllability, handling mechanisms for complex dynamics, reproducible workflows, post-production readiness for commercial delivery, and robust data/copyright governance.
These six metrics answer one central question:
Does this company possess the capability to transform probabilistic AI outputs into deterministic commercial execution?
It is in this sense that "technical strength" must be redefined.
Building foundational models is unquestionably a deep technical feat. But when procuring a content service provider, training foundation models and delivering commercial brand videos are two distinct challenges.
A content company does not need to train its own foundation models, but it must know precisely when to use which model, when not to rely on AI generation, where human intervention is mandatory, and how disparate technologies converge into a broadcast-ready spot.
Conversely, possessing an advanced foundation model does not automatically make a platform capable of handling brand strategy, creative direction, IP compliance, and final commercial delivery.
Technical supply and technical delivery represent two completely different capabilities along the value chain.
What Should Brand Procurement Demand Vendors Prove?
When entering the written qualification phase, we advise procurement teams to look beyond tech buzzwords and require verifiable evidence.
For example: demand a complete real-world project workflow instead of a slide featuring model logos; request an actual case study proving cross-shot consistency for brand assets rather than a generic claim of "character consistency supported"; ask for a failure-recovery case study showing how errors were fixed; and inspect a finalized commercial TVC that successfully passed brand audits along with the exact technical scope handled by the vendor.
For complex projects, evaluate whether the vendor has simultaneously managed hybrid AI-live-action shoots, IP copyright audits, multi-language localization, multi-aspect ratio adaptations, or non-standard media formats.
Such evidence is exponentially harder to fake than claiming proficiency in the latest tools.
At FansAI, we rarely present technical strength as a passive list of models. The Yili Satine TVC validated control over characters, packaging, and visual tone in an all-AIGC spot; the Disney × F1 × MINISO campaign proved that IP boundaries, creative ideas, and production must collaborate within a single unified workflow; and the TCL Winter Olympics project proved dual-track live-action/AIGC execution under tight international timelines. The Disney × F1 project was executed within a strict 7-day window, passed three-party copyright compliance, and generated over 30 million monthly views on Instagram.
The value of these cases lies not in proving that "FansAI knows how to use AI," but in proving that technology operated successfully under commercial constraints and achieved concrete delivery.
Conclusion: The Next Threshold for AI Video Companies Is No Longer Tool Ownership
The iteration speed of AI video technology has reached a fascinating turning point.
Complex challenges that required vast experience from production teams months ago are rapidly becoming natively supported by base models.
30-second narratives, synchronized audio-visual output, reference consistency, native 4K, multi-shot orchestration, multimodal editing, and Agent batch scheduling are continuously becoming standardized infrastructure.
This does not diminish the value of specialized AI content companies.
On the contrary, it lays bare where true capabilities lie.
As models become universally accessible commodity tools, what brand procurement truly needs is not "the person who prompts AI best."
Brands need a strategic partner capable of understanding business requirements, routing tech stacks, controlling brand assets, managing failure fallbacks, executing industrial post-production, and taking full responsibility for commercial deliverables.
If procurement teams redesign their AI video vendor technical qualification forms for 2026, the single most important line item to remove is:
"Proficiency in how many AI tools."
And the most vital item to add is:
"Proof of a reproducible, repairable, auditable production system accountable for final commercial delivery."
Base models determine what can be created today.
System capabilities determine whether an AI content company can deliver excellence consistently.