Introducing SubQ Snap, the first long-context decision model for real enterprise work. Snap is the only decision model or LLM where compute per token stays constant as context grows. • 384K tokens of multimodal context, 12x larger than Jev. • 61.7 on Decision Index v0.2.1 vs Jev’s 57.9, outperforming... on 13 of 16 enterprise benchmarks. •Processes 100K tokens and returns a decision in under one second. •$0.05/MM input tokens. Output is free. Available today to anyone. The first 10,000 sign-ups get 100M free input tokens. We’ll likely hit capacity fast and will add users as we add compute. Compute offers welcome 🙂. Latency will be variable as we tune the platform to this new model and category of users. Model speeds are 80ms for minimal context, 250ms for 32k, 660ms for 100k, and 2.5 seconds for 384k. Visit:show more

big fan of your guys co-founder btw

jian yang was ahead of his time.

Using a frontier LLM for every decision is like driving a Ferrari to Walmart because you needed milk.

What happened to your first model?

It is in private beta with some of the world's largest enteprises, and we have released three generations since May. If you want to be considered for access, you can fill out the form here! Broader access will come, and this model release is a test run for us.

I’d love to see a demo where this runs something very big, and finding very small pieces and at very high accuracy at speed. The launch video looks impressive though

If you look around X, you will see quite a few! We gave a few hundred people access over the weekend, and dozens of folks built cool demos or thorough evaluations. One person even wrote a 70-page report. I'll try to highlight them as they start to show up on X.

I’m looking forward, let me know if you have anything particular that really blows your mind, send it over, DM or just ping me, DM is way easier for me to spot than a ping tho 🙏🏼🫡

Let's not stop at 384K 🚀

You know we won't!

On it, boss!

I just signed up!

Thank you! We would love to see what you build.

Yep, that looks super impressive! Good job! Love to see

Thank you! We would love you to go build something awesome on top of it! Early access folks built a few dozen cool ideas, and we want to see more!

Congrats on the launch! We ran subq-snap-preview through ~25k calls / 20 tasks in the closed beta (all zero-shot): • MMLU 86.8% · ARC-C 97.6% • 90–95% accuracy up to ~195k tokens • 9 languages + images • calibrated probs, ~0.55s p50 Full report 🧵👇

Thanks for the thorough evaluation! There are so many nuggets here, I need more time to dig into this. I already flagged it in the discord!

Welcome to the decision race 🔥 We just integrated and timed 8 hosts (24 models) - happy to add Snap as the 9th

Thanks! Feel free to test us any way you want, the more evaluation the better!

Big projects that felt like too much just got a lot more doable

We can't wait to see your next project!

Testing it right now

Excited to see enterprises adopt specialized models like this. Congrats!

Thanks!

Most agent calls are structured decisions, not creative work.. I know this very well, first hand experience! Congrats on the launch. - Elon (p)

this looks amazing a fresh take on decision models

I don't trust you guys

Still routing every decision through a full frontier model in 2026 is pure waste

Agreed! This is why we will co-design our LLM and decision model to hand off work to each other.

The interesting part is how this changes the way we build AI agents. Let smaller models handle structured decisions and save the powerful LLMs for tasks that actually need them. Are we overcomplicating AI development by using one model for everything? 👀

Exactly how things should be - powerful models solely focusing on the real challenges. Isolating problems like this is literally the most proactive way people will use Ai in the future, well done👏👏

This is an exciting unlock for for decision model uses!

what an insane context capability

Thank you! More coming!

Congrats on this launch. It's really key that decision models stay useful and efficient in longer contexts. Very cool stuff.

Thank you!

Most agents still choke on real context. Snap looks like it doesn’t

It does not! We would love to see how you use Snap in your best agents.

@Av1dlive This look solid

Congratulations!

Thanks, Sarah! We are excited to have you as part of our journey.

Need access to SUBQ PREVIEW Early access ASAP Snap is beating all our internal benchmarks, but it's not enough

feels like a new category

It is!

Bye bye Jev, hello SubQ Snap 👋

We would love to see what you build!

Can’t wait to take it for a spin, do you have a GitHub repo with sample / inspirational implementations we could look at Alexander?

Another software moat getting smaller

awesome processing 100K tokens and returning a decision in under a second could be valuable for enterprise workflows that need fast answers from large datasets.

This is the one. 384k in a single call with no chunking is exactly what long docs needed.

All that context

What baselines were used for the 13 benchmark wins? Would be good to see the evaluation setup on long-horizon compliance tasks.

This was a comparison against Jev, on 16 benchmarks from the Decision Index. You can see the methodology here. We evaluated more than 50 benchmarks in total and created some of our own too.

Thanks for sharing.. let me have a look .

I let the model classify and an if/then makes the actual decision, that way I can explain every single one to the client

This is a remarkable advancement in AI capabilities, particularly the consistent compute cost across extended contexts.