ChatGPT
Perplexity
Claude
Gemini
On March 24, OpenAI announced it was shutting down Sora. The app goes dark on April 26. The API follows in September. The billion-dollar Disney partnership signed back in December dissolved along with it, and according to reporting, Disney found out less than an hour before the rest of the world did.
If your creative workflow lived inside Sora, you have a few weeks to export your work and figure out what's next. That's the kind of timeline that turns an abstract debate about AI strategy into a very concrete problem.
It also points at a question worth thinking through carefully: in a field this volatile, what's the right way to build a creative practice on top of AI tools? It's a question OpenArt has been building a particular answer to from the start. Before getting to that answer, though, it's worth looking at why the question keeps getting harder to avoid.
El panorama no va a estabilizarse
There's a natural instinct, when something is moving fast, to wait for it to slow down. To pick the eventual winner once the dust clears. It's a reasonable instinct, but it doesn't really match what's happening in AI image and video generation right now.
The pace isn't slowing. If anything, it's picking up. In just the last few months, Midjourney shipped V8 with a rewritten engine and native 2K output. Black Forest Labs released Flux 2. Google's Nano Banana 2 became one of the fastest image models available. Imagen 4 raised the bar for photorealism again. Recraft V4 took over as the model people reach for when they need vectors. Ideogram keeps leading on text rendering. Kling and Seedance are pushing video forward on a near-monthly cadence. And Sora, which a year ago looked like the future of AI video, is being sunset.
The pattern isn't a market consolidating around a few stable winners. It's a research frontier where the leader changes regularly, capabilities leapfrog each other constantly, and the economics of running these models are demanding enough that even well-funded labs are making hard tradeoffs. Sora wasn't shut down because it was bad. By most accounts the underlying model is impressive. It was shut down because video generation is enormously expensive to run, the user numbers didn't justify the compute, and OpenAI needed those resources elsewhere. That same math applies, in some form, to almost every model in the space.
No single model is best at everything
Once you're working with this landscape regularly, the question of "which model is best" starts to feel like the wrong question. Best for what?
A rough sketch of where things stand right now:
Midjourney sigue marcando el listón en calidad estética y coherencia estilística. Si quieres una imagen que se vea preciosa nada más generarse, muy poco se le acerca. La contrapartida es un ecosistema cerrado, sin una API real, con integración limitada en flujos de trabajo más amplios y un modelo basado en Discord y suscripción que no encaja en todos los casos de uso.
Flux 2 is where a lot of the open-source workflow community has landed. It's flexible, controllable, and plays well with the broader ecosystem of fine-tuning, LoRAs, and pipeline tools. If you need to chain things together or build something custom, it's hard to beat.
GPT Image 1.5 is built around iteration. The conversational interface means you can refine an image the way you'd talk to a collaborator. Make this part warmer, move that element, try it again with a different background. The model holds context across the whole conversation, and that iteration loop is the actual product.
Google Imagen 4 y Nano Banana 2 están haciendo un trabajo extraordinario en fotorrealismo y velocidad. Nano Banana 2, en concreto, se ha convertido en el modelo de referencia para editar y mantener la coherencia de personajes, ese tipo de problema de infraestructura poco vistoso que determina si puedes terminar un proyecto o solo generar impresionantes piezas sueltas.
Recraft V4 es el modelo al que recurrir cuando necesitas vectores, logos o diseños que deban escalar de forma limpia.
Ideogram still leads on text rendering, which matters more than people expect once you start trying to put words inside images.
Kling and Seedance lideran ahora mismo la conversación sobre vídeo, con nuevas versiones apareciendo a un ritmo que hace que cualquier afirmación de "mejor modelo de vídeo" parezca temporal por definición.
The takeaway isn't that one of these is the right answer. It's that a photographer, a concept artist, a small business owner making product mockups, and a filmmaker storyboarding a scene all need different things, and the model that's best for one of them is rarely the best for the others.
The cost of locking in
When you commit your creative process to a single model, you take on more than you might realize. You inherit its limitations, its pricing changes, its quality regressions when a new version ships, and its risks if the company behind it changes direction. You lose flexibility to take advantage of whatever launches next month. And you take on the kind of tail risk that, until very recently, a lot of people were treating as theoretical.
El cierre de Sora es el ejemplo más sonado hasta ahora, pero no es el único. Los modelos quedan obsoletos. Los precios cambian. Las API desaparecen. A veces la calidad varía de formas que rompen flujos de trabajo que funcionaban perfectamente el día anterior. Nada de esto significa que un modelo concreto sea mala elección. Solo significa que tratar un único modelo como la base de tu trabajo es más frágil de lo que parece.
The bet OpenArt made
La alternativa a encasillarse no tiene nada de dramático. Consiste simplemente en tratar los modelos de IA como los fotógrafos tratan los objetivos, o como los músicos tratan los instrumentos. Aprendes para qué sirve cada uno, echas mano del adecuado cuando el trabajo lo pide y no te encariñas demasiado con ninguno.
That's the premise OpenArt was built on. Not "we have more models than the other guys" as a feature checklist, but a position about where the power in a creative practice should sit: with the person making the work, not with whichever lab happens to be in the lead this quarter. The Suite is designed so that the right model for the job is one click away, rather than another subscription, another interface, and another file format to wrangle. When a new model launches, it shows up in the workflow you're already using. When a model shuts down, and more will, the work moves with you instead of getting stranded.
En OpenArt creemos que los creadores no deberían tener que cambiar de plataforma cada vez que el panorama se transforma, y que el trabajo de una herramienta creativa es proteger tu obra de ese tipo de vaivenes, no traspasártelos. Por supuesto, hay otras formas de mantener la flexibilidad. Puedes montar tu propio flujo de trabajo multiherramienta repartido entre varias plataformas, y mucha gente lo hace. Lo importante, sea cual sea el camino que elijas, es que alguna versión de la flexibilidad es cada vez más el único enfoque sensato.
A few practical principles
Si intentas averiguar cómo enfocar esto en tu propio trabajo, aquí van algunas cosas que hasta ahora han funcionado bien:
Ajusta el modelo al resultado, no al revés. Si notas que estás retocando tu idea para que encaje con lo que un modelo puede hacer, es una señal que merece atención.
Keep your work portable. Prompts, reference libraries, decisions, and exports are all easier to move if you build them to be moved from the start.
Stay curious about new models, especially the ones outside whatever you're already using. The model that's best for your work today probably isn't the one that'll be best six months from now, and that's worth being excited about, not anxious about.
And don't confuse familiarity with the right fit. Sticking with a model because you know it well is different from sticking with it because it's the best one for the job.
Where this leaves us
A estas alturas, la era multimodelo no es realmente una predicción, sino simplemente una descripción del presente. Sora's shutdown is a sharp reminder of why flexibility matters, but it isn't the first reminder and it won't be the last. The creators and teams who'll do well in this period are probably the ones who treat AI models as a toolkit rather than a commitment, who pay attention to what each model is actually good at, and who let their workflows evolve as the landscape does.
That's the bet OpenArt is built around, and it's the bet we'll keep making every time another model rises, falls, or rewrites the rules. None of this requires abandoning the models you love, it just means holding them a little more loosely, and leaving room for whatever comes next.