Teodora P L

This month we launched HeyGen Video. Then HeyGen Voice went straight to #1 on Artificial Analysis. At the same time, more and more of the model layer is going open source. So I keep getting two questions. How will video and voice models play out from here? And if anyone can get a great model, where is the real competitive advantage? Here's how I see it. First, where I think this is going. Open models will win in video and voice, the same way they did in LLMs. In LLMs, closed models led the frontier first. Then open models caught up. Today, open models serve most of the tokens. I believe video and voice will follow the same path. It's simply a better world to live in. Open models mean more people building models, more apps, and more value for the end user. And it's just getting started. A year ago, the best open video models were second-tier. MiniMax H3 (Aug 2026) changed that. For the first time, an open video model was released at the frontier. That was the tipping point for the industry. Voice is moving too. Fish Audio released their open model this week. I haven't tested it yet, but the demos look great. So if the model is no longer the moat, what is? I think LLMs and coding are the best reference we have. The model is shared. The advantage comes from what you build around it. Here's what that looks like in video. 1. Private video data is untouched. Enterprises sit on huge amounts of private video and voice data. In the AI world, this is the least developed context of all. Open models finally let companies build on it. 2. Cost matters much more in video than in LLMs. In LLMs, frontier models cost more than open models, but the value is so big that cost almost doesn't matter. Video is different. A frontier video model can cost close to $1, sometimes up to $10, for a few seconds of video. The demand is there. Price is blocking it. Open models give companies the chance to build very efficient inference, push the cost down, and unlock that demand. We're already seeing it. We released HeyGen Video as the fastest and cheapest model on the market, and it has been #1 on OpenRouter for more than a week in a row. 3. Speed without losing quality is the real skill. Most inference companies know how to make models faster. That's low-level work: CUDA, GPU optimization, GPU scheduling. Video is a different game. The hard part is the trade-off between quality and speed, and that is decided in post-training. Post-training is a completely different game from pre-training. It's full of nuance. You curate the data. You steer the model in a specific direction. You make it faster while losing as little quality as possible. We've been doing this at HeyGen for years. Cheap and best are no longer opposites. Open video and voice models are just getting started. This is the most exciting time to be building in video.