Two years ago the question for anyone making images or video with AI was simple: which tool is best? In 2026 the question has inverted. Google, OpenAI, ByteDance, Alibaba, Kuaishou, MiniMax and xAI each ship a new image or video model every few months, each one better at something specific, and each one behind its own account, its own credits and its own interface. A small studio that wants Nano Banana for product shots, Seedance for video and Kling for motion control is now managing five logins and five bills.
This fragmentation is quietly reshaping how creative work gets done, and it has produced a new category of product: the multi-model workspace.
What changed
The first generation of AI creative tools were single-model products: one company, one model, one interface. That worked while one model was clearly ahead. It stopped working in 2025, when the leaderboard started changing monthly. Seedance 2.5 renders 30-second clips with native audio; Veo 3.1 is still the reference for first-and-last-frame control; GPT Image 2.5 reads text and layout instructions that other models ignore; Qwen Image 3 handles Chinese typography that nothing else gets right. None of them is the best tool for every job, and a creator who commits to one is leaving quality on the table for the others.
The result is a wave of aggregator workspaces that put dozens of models behind one interface and one balance. Pollo, CreateVision and Orylia are three of the names in this space; between them they route to roughly forty image and video models, with new ones appearing weeks after the vendor’s own launch.
How the multi-model approach works
The pitch is less about the models themselves than about the plumbing around them. A single credit balance replaces four subscriptions. Per-second pricing is shown before a video renders instead of discovered on an invoice. Outputs from different models land in one library, which matters when a product shot from one model becomes the first frame of a video from another.
Some of these platforms have gone further and built an agent layer on top. Orylia describes its approach as a visual agent: the user states the job in plain language, and the system picks the model, writes the prompt, checks the result and retries if the output misses the brief. The company says its agent now remembers a user’s past preferences across sessions, so a brand’s house style carries from one project to the next without being re-specified each time.
The trade-offs
Aggregation is not free. Platforms pay the vendors’ API rates and add a margin, so a heavy user of one model may pay more than they would going direct. Vendor-specific features sometimes arrive late or not at all. And the convenience of one balance can hide how much a 30-second 4K clip actually costs until the credits are gone.
There is also a question of durability. Several single-model startups have folded or been acquired in the last year; aggregators that depend on those APIs inherit that risk.
What to watch
Three signals will show whether the multi-model workspace is a lasting category or a transitional one. First, whether the big vendors start bundling each other’s models themselves, which would remove the aggregators’ reason to exist. Second, whether the agent layer delivers measurable time savings rather than demos. Third, whether per-task pricing survives as models get cheaper, or whether the market drifts back toward flat subscriptions.
For now, the practical advice for anyone producing AI visuals at volume is the same advice that applied to cloud computing a decade ago: avoid locking into one provider until you know which one you actually need.
