ie intelligence per token, and then tokens per second

My current feel is that if Sonnet 4.6 was 5x faster than Opus 4.6, I'd be primarily using Sonnet 4.6. But that wasn't true for me with prior model generations, in those generations the Sonnet class models didn't feel good enough compared to the Opus class models. And it might shift again when I'm doing things that feel more intelligence bottlenecked.

But fast responses have an advantage of their own, they give you faster iteration. Kind of like how I used to like OpenAI Deep Research, but then switched to o3-thinking with web search enabled after that came out because it was 80% of the thoroughness with 20% of the time, which tended to be better overall.

There has been quite some progress unifying DDPM & SGM as SDE

> DDPM and Score-Based Models: The objective function of DDPMs (maximizing the ELBO) is equivalent to the score matching objectives used to train SGMs.

> SDE-based Formulation: Both DDPMs and SGMs can be unified under a single SDE framework, where the forward diffusion is an Ito SDE and the reverse process uses score functions to recover data.

> Flow Matching (Continuous-Time): Flow matching is equivalent to diffusion models when the source distribution corresponds to a Gaussian. Flow matching offers "straight" trajectories compared to the often curved paths of diffusion, but they share similar training objectives and weightings.

Is there a similar connection between modern transformers and diffusion?

Suppose we look at each layer or residual connection between layers, the context window of tokens (typically a power of 2), what is incrementally added to the embedding vectors is a function of the previous layer outputs, and if we have L layers, what is then the connection between those L "steps" of a transformer and similarly performing L denoising refinements of a diffusion model?

Does this allow fitting a diffusion model to a transformer and vice versa?

Me: What are some of Maradona's most notable achievements in football?

Mercury 2 (first sentence only): Dieadona’s most notable football achievements include:

Notice the spelling of "Dieadona" instead of "Maradona". Even any local 3B model can answer this question perfectly fine and instantly... Mercury 2 was so incredibly slow and full of these kinds of unforgivable mistakes.

Other labs like Google have them but they have simply trailed the Pareto frontier for the vast majority of use cases

Here's more detail on how price/performance stacks up

https://artificialanalysis.ai/models/mercury-2

The open question for me is whether the quality ceiling is high enough for cases where the bottleneck is actually reasoning, not iteration speed. volodia's framing of it as a "fast agent" model (comparable tier to Haiku 4.5) is honest -- for the tasks that fit that tier, the 5x speed advantage is genuinely interesting.

Assuming that's what is causing this. They might show some kind of feedback when it actually makes it out of the queue.

i think instead of postiioning as a general purpuse reasoning model, they'd have more success focusing on a specific use case (eg coding agent) and benchmark against the sota open models for the use case (eg qwen3-coder-next)

Mercury 2

Thought for a minute

Well?

Mercury 2 Today at 3:06 PM

Yes – Unicode includes a seahorse emoji. It is U+1F9A0 and renders as:

(seahorse)

(If your device or browser doesn’t show the graphic, you may see a placeholder box.)

Ask

Explain

'''

And a pop-up error of: "The string did not match the expected pattern."

That happened three times, then the interface stopped working.

I was hoping to see how this stacked up against Taalas demo, which worked well and was so fast every time I've hit it this past week.

https://arxiv.org/pdf/2506.17298

Um, no it isn’t. Presumably this is the answer to any question about a company it doesn’t know? That’s some hardcore bias baking.

The future looks crazy