"Bigger isn't always better. The smartest teams pick the smallest model that solves the problem."
The AI industry has a size obsession. Every headline trumpets the latest trillion-parameter behemoth. But here's what those headlines miss: for most real-world applications, smaller, faster models outperform the giants.
Yes, GPT-4 is more capable than GPT-3.5. But for 80% of real-world use cases—answering questions, summarizing text, generating structured outputs—the difference is marginal. What's not marginal is the 5-10x latency difference.
Smart engineering is about picking the right tool for the job. A 7B parameter model responding in 50ms is better than a 70B model responding in 500ms—if both solve your problem equally well.
Always use the biggest model. Accept the latency tax.
Use the smallest model that works. Optimize for speed.
Large models still win for:
But for everything else—chatbots, classification, extraction, simple Q&A—smaller models running faster create better user experiences.
Our flagship model is purpose-selected for the speed/quality frontier. Fast enough to feel instant, smart enough to be useful. The sweet spot for real-time applications.
The next wave of AI won't be about making models bigger. It'll be about making them more efficient. Smaller models, better training, smarter inference. The winners will be those who deliver the most value per millisecond.
Stop defaulting to the biggest model. Start optimizing for the experience you want to deliver.