How many AI tabs do you have open right now?
If you're like most developers, probably more than one.
A year ago, most people were debating which AI model was best.
Today, many developers use several.
One model for coding. Another for writing. A third for research. Maybe a different one for analyzing documents or reviewing code.
That's because AI workflows have changed.
The conversation is no longer about finding the perfect AI model. It's about building an effective workflow around multiple AI models and knowing when to use each one.
The challenge is that every new model adds more complexity. More tabs. More subscriptions. More chat histories. More context switching.
We've seen this pattern across developers, marketers, founders, researchers, and teams.
As AI tools become part of everyday work, the real question becomes:
How do you efficiently access, compare, and collaborate across multiple AI models?
That's the question that led us to build Aymo AI, and it's also why we're excited to join the DEV Community and share what we're learning about multi-model AI workflows, AI productivity, and team collaboration.
The AI workflow nobody talks about
Most people think the AI revolution is about better models.
From what we've seen, it's increasingly about better workflows.
A typical workflow might look something like this:
- Ask one model a question.
- Check another model for a second opinion.
- Open a third tool for research.
- Copy everything into Slack, Notion, or your documentation.
- Repeat tomorrow.
The AI models are impressive.
The workflow isn't.
Information gets scattered across tools. Context gets lost. Teams duplicate work. Valuable conversations disappear into personal accounts and browser tabs.
We've experienced this firsthand, and chances are you have too.
The best AI model doesn't exist
After working with dozens of frontier AI models, we've learned something important:
There is no single best AI model.
Different models excel at different tasks.
- Some are exceptional at coding and debugging.
- Some are stronger at long-form writing.
- Some handle research and reasoning better.
- Others shine when working with images, documents, or web search.
The question isn't:
Which AI model is best?
The better question is:
Which AI model is best for this task?
or
Which is the best AI model for research?
That's a very different conversation.
And it's one that more teams are starting to ask.
Why Comparing AI Responses Matters
One of the most valuable habits we've seen among experienced AI users is simple:
They compare.
Ask the same question to multiple models and you'll often get different perspectives, tradeoffs, and solutions.
A coding prompt might produce:
- One response focused on performance.
- Another focused on readability.
- Another focused on security and edge cases.
None are necessarily wrong.
But together, they help you make a better decision.
That's why we believe comparison is becoming a core part of modern AI workflows.
Not just generation.
Comparison.
AI is becoming a team sport
Most AI tools were designed for individuals.
But AI is increasingly being used by teams.
Developers collaborate on code.
Marketers collaborate on campaigns.
Researchers collaborate on findings.
Product teams collaborate on decisions.
Yet many AI conversations still live inside personal accounts.
When conversations aren't shared, knowledge becomes siloed. Teams repeat work. Context disappears.
We think AI should be collaborative by default.
The same way documents, repositories, and project management tools became collaborative, AI workflows need to evolve in the same direction.
Why we built Aymo AI
Eventually, we realized the problem wasn't the AI models themselves.
It was the workflow around them.
We built Aymo AI because we wanted a simpler way to work with multiple AI models without constantly switching tools, subscriptions, and workflows.
More importantly, we wanted a workspace where teams could:
- Access leading AI models from one place
- Compare responses side by side
- Analyze files and documents
- Search the web
- Share conversations
- Collaborate in real time
Today, Aymo AI brings together 45+ AI models from leading providers, including OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, Meta, Mistral, Kimi, MiniMax, MiMo, Z.ai, and Perplexity.
Not because having more AI models is interesting.
Because better workflows lead to better outcomes.
If you're curious about the models currently available, you can explore our complete AI Model Directory.
What's next
We're still early in the multi-model AI era.
New models launch every month. Capabilities evolve quickly. Workflows continue to change.
That's why we're building in the open and sharing what we learn along the way.
You can also explore our public Product Roadmap to see what we're currently working on, including model comparison improvements, collaboration enhancements, voice interaction, and upcoming integrations.
Why we're here
We're joining the DEV Community because some of the most interesting conversations about AI are happening among developers, builders, founders, and early adopters.
Over the coming months, we'll share what we're learning about:
- Multi-model AI workflows
- AI model comparisons
- Prompt engineering
- Developer productivity
- Building AI products
- AI research workflows
- Team collaboration with AI
- Lessons learned from working with frontier models
Some posts will be technical.
Some will be practical.
Some will challenge assumptions.
Hopefully all of them will be useful.
We'd love your perspective
How many AI tools do you actively use each week?
Do you rely on one model, or do you switch between multiple AI models depending on the task?
Let us know in the comments.
We're looking forward to learning from the community, sharing our experiences, and contributing to the conversation.
Useful links
- Website: https://aymo.ai/
- AI Models: https://aymo.ai/docs/ai-models
- Roadmap: https://aymo.ai/roadmap
- Pricing: https://aymo.ai/pricing
