Inception generalist model matches Claude Haiku quality at 5-10x speed
Stefano Ermon · ⚡️Mercury: Ultra-Fast Diffusion LLMs — Estefano Ermon, CEO Inception Labs · Aug 4, 2025 · at 16:55
Stefano Ermon discusses independent benchmark results from Artificial Analysis for Inception Labs' diffusion-based generalist foundation model compared to speed-optimized closed-source models.
0:00 / 0:23exact quote · 23.2s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“We had our generalist model evaluated by artificial analysis and the intelligence score from AA artificial analysis around 40. So it's comparable to GPT, 4.1 nano, cloud haiku, kind of like Close source speed optimized models. It's roughly comparable in terms of intelligence, but significantly faster, five, 10 X faster compared to this kind of speed optimized autoregressive models.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Stefano Ermon
Ermon: Diffusion models could become the dominant architecture over autoregressive models
“I'm pretty optimistic about a future where diffusion models
Can become the dominant solution. I've seen it happen before with GANs a few years ago, so I wouldn't be surprised if that's the case also here.”
Ermon: Diffusion LLMs Pareto-dominate autoregressive models on inference efficiency
“On the inference side, what we're seeing is that diffusion models are much more efficient. We're actually able to Pareto dominate autoregressive models. If you think about the typical trade-off between throughput versus latency, which you kind of like cannot, …”
Ermon: Power constraints will drive diffusion models to replace frontier LLMs
“If it happens, it's gonna be driven by efficiency. Like we're all constrained by essentially power. And if you have, I mean, at the end of the day, it's all an inference game, right? Okay. Training is expensive, but then the thing that matters is being able to…”
Ermon: Inception Labs trained the first commercial-scale diffusion LLMs
“We've been successful in training the first commercial scale diffusion language models. We call this model Mercury.”
Ermon: Google's Gemini Diffusion benchmark numbers match early Mercury Coder results
“They've released some benchmark numbers. They seem to be pretty close to the numbers that we were getting with the Mercury Coder back in some, you know, back in early this year.”
Ermon: Diffusion models naturally self-correct errors during generation unlike autoregressive LLMs
“The fact that you have error correction that is built in. So you think about an autoregressive model. Once you output something, you can never take it back. And so if you want to do, you know, if you want to fix mistakes, maybe you can do a reasoning chain. Ma…”