Damnit, I've been waiting on an open weights model like this one, still no dice :(

I've been trialing this for the last few hours. It was quite slow at first, but now I'm getting around 300-400tps. Still nowhere near 1000tps, but it's still incredibly fast compared to what I'm used to with other models. It's quite capable as well. I asked it to present strategies for training an AI to play fighting games and it gave quite well-reasoned output. It searched the existing my existing knowledge base for details about the game's internals, including record/playback, and it's own general knowledge about the game is played. It wasn't sycophantic about my opinions either - it challenged me appropriately.

I think they might have a really decent model if they can deliver their advertised speeds at scale at reasonable prices. A tiered approach could work well.

Hmm at least in Openrouter, deepseek v4.1 flash via together/baseten/fireworks still beat mercury 3 on latency and intelligence

Ttft is quite slow with mercury

I created a pelican worked wonderfully, better than old haiku model or even sonnet models, maybe slightly worse than new gen models.

But overall it's a pretty ok model, the issue with speed seems to be that ttft is still high but still not sure how high thinking burden would be, I hope it scales well will be using it a lot if it works well.

This might actually crush most other LLMs if it's price and performance actually scales well. It was as cheap as deepseek for a few tasks I tried.

Anyone know if there is a subscription for it? I might want to test it out properly without burning a few hundred dollars on it.

I have liked diffusion models for quite a while, ever since i used the original Mercury to play AI Roguelite. I only have one question regarding Mercury 3: how tf did they get it to be so cheap?

If these benchmarks hold up it is a no-brainer against Haiku 5.5 and GPT-6 Luna, especially considering that Haiku 5.5 becomes 5x more expensive past 100k context, and this model goes up to 1M for the same price. (Note: the price increases 2x after a period post launch but is still way lower than Haiku is priced for >100k requests)

Are these models harder to reason about given they produce tokens non-linearly? Especially since we rely on non-latent reasoning to know what the model is "thinking".

its definitely good at admitting when it doesnt understand stuff. which is a rare treat with these models.

Having a chart with a linear scale on one axis and a log scale on the other axis is a really neat trick.

The resulting charts have a high probability of looking fantastic :-D

I wonder given its speed how competitive it would be with the current batch of decision models like Jev et al. Seems to me for decision models you really want maximum throughput.

I'm going to start using this instead of Haiku/GPT-Luna for tasks that require speed.

I'm wondering if it'll still be useful once the discount is lifted?