Muse Spark 1.3

frozenseven

This should probably be primary: https://news.ycombinator.com/item?id=49541149

simonw

llm -m meta-ai/muse-spark-1.3 "Generate an SVG of a pelican riding a bicycle" https://tools.simonwillison.net/markdown-svg-renderer?url=ht... 4.2266 cents, 38 seconds. For comparison here's Muse Spark 1.2, which animated it without me asking it to: https://tools.simonwillison.net/markdown-svg-renderer?url=ht... The 1.3 one is definitely better - better bicycle frame, better wing, better pelican hat. UPDATE: Here's another one with five pelicans for each of the five Muse Spark 1.3 reasoning levels: https://tools.simonwillison.net/markdown-svg-renderer?url=ht... The most expensive was reasoning level xhigh - 7.5 cents, 1m34s. And I ran five pelicans at all reasoning levels for 1.2 as well, here: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

Gecko4072

Used Muse Spark 1.2 and was not impressed at all. Fast and cheap but even GPT 5.6 Terra felt much more capable. Also not really looking to support a company that was just forced to pay $18B for mental health damages.

scotty79

Is the fact that everybody almost catches up with the frontier a sign that we are entering a new region of sigmoid curve?

wxw

“contributor” pricing at $0.10/$0.20 is crazy cheap if it’s measuring up to Sol. Definitely shows how important a user data flywheel is for RL and model improvement.

finnjohnsen2

So one model is "Not used to improve our products" and is 10-20 times more expensive to the "Used to improve our products"-model. Given this is Meta, my immediate assumptions that one is cheap because it lets me "be the product". I know I'm rushing to conclusions but there is zero trust here. The brain will do its thing. And the wording here is giving the brains a lot of wiggle room.

majerep

The previous version was, in my experience, the best free model available on OpenCode. It's been very good at simple/moderate tasks where I am precise in my ask and it doesn't need to make a ton of undefined assumptions. Hopefully this new version is also available on opencode for free.

mgaunard

They could have just called the article "struggling to remain relevant"

geooff_

Could this be best intelligence / $ if you're willing to let zuck digest your data?

meerita

I declined the use of cookies and everything went black. No content at all. Dissapointed.

7734128

Practically free for "contributors" at 0.2 usd/mtok. That's going to be hard to say no to for hobbyists.

superfrank

I started using Spark 1.2 for development because if you're willing to let Meta train on your data it was dirt cheap and was actually really pleasantly surprised with it. It's not a frontier model by any means, but for work that didn't require a top of the line model, I really enjoyed using it. I'm anthropomorphizing it a bit, but it felt like it knew its weaknesses and didn't try to impose it's opinions on me. What I mean by that is that it did what I told it and if there was something unexpected in the code that it put out it was often because I gave it ambiguous or conflicting instructions. It didn't try to go above and beyond and just acted like a tool, which is what I want from a coding agent 90%+ of the time. I also felt that it did a much better job of following established patterns in my code than many of the other current models do. I'm a huge fan of OpenAI's models and Spark 1.2 is what I expected 5.6 Luna to be. I'm curious and a little excited to use 1.3, but honestly a little worried that as Meta pushes for better benchmarks that Spark will start to fall into the trap of trying to be "helpful" in ways I don't want it to be. Tangential, but when I first started using Spark 1.2, it made me realize how much I miss 5.3 Codex. That model was the peak of coding models, IMO, in that it knew how to write good code, but didn't try to overstep or be "helpful" in unexpected ways. That got me thinking about how the major labs seem to be stepping away from coding focused models toward more general purpose ones and how I can't help but feel like that's a mistake.

tinyhouse

I had no idea Meta has a coding agent harness. Does anyone have experience with it and can comment? The 1.3 contributor prices look very attractive. I'll probably start using their API if performance is good and the API is reliable with decent rate limits.

bertili

DeepSWE scores 75.4 - that's the best score so far. And it's crazy cheap! Google held the top a few hours today with Gemini 3.8 Flash, but now second to Spark 1.3. All this competition will drive prices down!

Lucasoato

A model that (at least in benchmarks) is getting closer to SOTA. A clear separation between what’s used to improve their products and what’s not (at least this is what they claim). Good job Meta! Seriously. This is almost making me forget about the 18B$ lawsuit for children social media addiction.

ChrisArchitect

Blog post: https://research.meta.ai/blog/introducing-muse-spark-1-3 ( https://news.ycombinator.com/item?id=49541149 )

mromanuk

I didn't like 1.2, It make some mistakes in a web app, so I quickly went back to Claude, Kimi K3 or Deepseek V4. Hope this one can clear agentic development, because Muse Spark models are fast and cheap.

tyre

Meta is one of those companies where, if there is anything remotely comparable, I'm happy to pay more to not use them. They've had a profoundly negative impact on society and Zuckerberg is not who I want controlling the future at the top of AI. I feel the same about Grok w/ Elon. I will pay extra to use someone else. I'm not an Amodei stan, but of all of these people he seems to have the most ethical focus. Again, not everything done perfectly and I have my gripes, but of the leaders of frontier labs, I'll vote with my money. And, yeah, I wouldn't trust sama to watch my bag while I went to the bathroom.

souvlakee

Why they didn't use LLM to create html table instead of https://lookaside.fbsbx.com/elementpath/media/?media_id=1048... ?

jumploops

The "contributor" pricing is the standout here at a ~20x discount, if you allow training on your data. The model seems on par with Sol and Opus 5 on paper (admittedly on some older/saturated benchmarks, but very competitive for $). Stats: 1M context, $0.10 input/$0.002 cached, $0.20 output (Mtok)