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...

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.

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!

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.

muse-spark-1.3-contributor. Say what you want and Meta, changing the pricing to explicitly say 'we train on this and value it this much' is what every model provider should do. As a side note, it is now completely obvious how much stealing my tokens for training is worth to model providers. I avoid/pay extra/try my best to make sure I am not getting trained on but it seems like it keeps popping up that I missed a setting somewhere. This is the first quantifiable number I have seen out there from a model provider. Maybe it can help in lawsuits to quantify the damages for copyright/other things?

I like the approach of providing a discounted version of the API that is used to train vs. the full price version. Seems reasonable and transparent.

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.

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

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)

“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.

Still waiting on them to release weights for Muse Spark 1.2, like they promised to. Wonder if they plan on doing the same for 1.3 which would be crazy

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.

artificial analysis results: https://x.com/ArtificialAnlys/status/2095247787277553929

Im a caveman writing c/cpp. Last time ms1.2 was even worth than DeepSeek v4f preview on internal benchmark. It just feels like extremely over fitting on certain paths.

I have not tried Muse Spark for code, but I've been using it for a while to write Latin. I find it's one of the best at it, alongside Gemini. For example, I've recently been using it to translate the subtitles of the show I'm watching into Latin, to provide me with a bit more input. (I'm learning Latin, for context)

I am very impressed by this model so far. It's faaast and it seems to be just intelligent enough to do really well. It's UI work (simple python UI) is very clean and functional. The UX was 'there'.

For folks who are impressed with costs, why does it matter to you? Is subscriptions not a thing? I may be missing something but only companies should really care about this I would think?

Very keen to try this after using Claude Code over the last few months. Should I just point Claude Code to Muse Spark endpoint (because I'm familiar with Code)? What do people think of Muse Code or other coding agent harnesses?

Is everyone rushing to launch something before Astra tomorrow?

For all of the comments about training: I thought that subscription plans for other models allow the same. Am I mistaken?

Does anyone know what the license for this model is? Specifically any word on restrictions about what it can be used for?

As a product, would developers switch to a meta model/harness? I don’t think so.

Only way I see is if it becomes the new SOTA / frontier, does anyone think Meta will surpass Anthropic or OpenAI?

I still can’t get my head around why language models are an existential threat to Meta - they own the platforms people watch adds on?

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

it seems like gemini 3.8 flash is more capable and cheaper. The only reason i would use this is if i was willing to share my data with meta, and allow them to train on my data. In that case it becomes dirt cheap.

Ha, even with monitoring engineers keystrokes and mouse movements not SotA on OSWorld.

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

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

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.

How do people actually use this? Do they use it through some sort of subscription plan, or via OpenRouter?

It's true that there hasn't been any meta news about LLM for a while now

Funny how quickly Meta caught up after Lecun left.

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.

> /taste: an anti-slop filter: a flat checklist of visual defaults not to use, so generated UI stops looking machine-made.

This is interesting

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

$META has everything it needs, great team, great models coming out, great infrastructure (GPUs), great userbase and distribution channels. $META is underrated.

Any idea what size this is?

Not mentioned in pricing: Surveillance costs of using Muse Spark

What a day. OpenAI is behind basically all major competitors - at least for a some amount of time.

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.

This should probably be primary:

https://news.ycombinator.com/item?id=49541149

Lol "not used to improve our models" is AI's enterprise SSO.

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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.

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

All people here care about is hating Meta. Just look at the top voted comment. No one cares about the merits of the model, etc. HN has become nothing but an echo chamber.

If it's from meta, pit h in the bin.

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