Discussion summary
Muse Spark 1.1 is not open-weight and is trained on proprietary data, with some skepticism about benchmark selection and transparency. Meta's AI efforts are viewed as improving, though concerns about benchmarks leaking and gaming persist.
What the discussion says
- Some users question the transparency and openness of the model.
- Concerns about benchmarks being manipulated or leaked.
- Optimism about Meta's recent AI progress.
- Skepticism regarding the selection of benchmarks.
“This is not open-weights, right?”
“Meta is back in the game, albeit not at the top.”
Comments
Hacker News
This not being available on Openrouter really makes it hard to test. I was going to compare vs Grok 4.5 and GPT-5.6 Luna, but I don't want to deal with signing up for Meta for it unless it checks out. Please Meta make this available.
by mchusma
How is every company able to show itself at the top of every benchmark?
by paxys
I personally do not like Meta, but I'll say this. The more competition, the better for regular consumers. (Enterprise too)- Google
- Chinese models
- Grok
- Meta
- OpenAI
- Anthropic
I think this is a win. I'm building like crazy to take advantage of all these subsidized tokens while I can.
by kilroy123
Interesting how the prevalent opinion until yesterday seems to have been that OpenAI & Anthropic are irreversibly ahead and now with xAI and Meta at least delivered something that's competitive with useful models and cheap too. Granted, the narrative that the two leading labs are ahead still holds with Fable (and perhaps an upcoming GPT6), but it's not as over as common knowledge by the opinion leaders would have us believe.
by Sol-
The pricing is insane: $1.25/$4.5 for 1M tokens, and $0.15 for cached input!
https://dev.meta.ai/docs/getting-started/pricing-rate-limits
by Tiberium
Maybe Zuck should double down on his "spoiler" role with models rather than compete head-to-head.
He doesn't have to match Anthropic or OpenAI model revenue if he can deflate theirs by 99%.
All he has to do is keep spending a few billion dollars developing frontier models, release them as open weights, and turn coding models into a commodity. He also needs a good OSS reference harness to match. Very few people are in a position to do this and for it to make business sense.
That's quite likely where things are headed regardless, and he could speed it up significantly.
We should all hope models move from proprietary products to commodities the way compilers did.
This may be one of the best things Zuck could do for the world.
by jacobgold
I had a few days of preview access, which was long enough to put together a plugin for LLM. You can try the model out in the terminal like this:
uv tool install llm
llm install llm-meta-ai
llm keys set meta-ai
# paste API key here
llm -m meta-ai/muse-spark-1.1 "Generate an SVG of a pelican riding a bicycle"
For comparison, here's the pelican I got from Muse Spark 1: https://simonwillison.net/2026/Apr/8/muse-spark/
by simonw
Lot more details in the linked report https://ai.meta.com/static-resource/muse-spark-1-1-evaluatio...
From Terminal-bench-2.1 details,
> We use a bash-tool-only agent harness to evaluate 89 Terminal-Bench 2.1 tasks from the official repository, where resources are capped at 6 CPU cores and 8GB RAM.
This disqualifies the results. Each terminal bench task has a cpu upper limit and RAM upper limit. Overriding either is disqualification.
For reference, in tbench-2.1,
1. 0 out of 89 task allow 6 cpu cores (highest is 4, and i think only 1 task)
2. 8 out of 89 tasks allow 8GB RAM
This kind of shady benchmarking (I was talking about it just yesterday in a different context https://news.ycombinator.com/item?id=48838212) takes all joy out of building a harness to improve benchmark performance of a model because no matter what you do, you won't beat the headline (cheating) number. This is presumably why this model is not in the official benchmark leaderboard https://www.tbench.ai/leaderboard/terminal-bench/2.1
As an ex Meta employee, this is a little sad but not massively surprising. 'Number go up' is the core performance evaluation metric until PSC is done and you move on.
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- Hacker News
- This not being available on Openrouter really makes it hard to test. I was going to compare vs Grok 4.5 and GPT-5.6 Luna, but I don't want to deal with signing up for Meta for it unless it checks out. Please Meta make this available.by mchusma
- How is every company able to show itself at the top of every benchmark?by paxys
- I personally do not like Meta, but I'll say this. The more competition, the better for regular consumers. (Enterprise too)
- Chinese models
- Grok
- Meta
- Google
- OpenAI
- Anthropic
I think this is a win. I'm building like crazy to take advantage of all these subsidized tokens while I can.
by kilroy123 - Interesting how the prevalent opinion until yesterday seems to have been that OpenAI & Anthropic are irreversibly ahead and now with xAI and Meta at least delivered something that's competitive with useful models and cheap too. Granted, the narrative that the two leading labs are ahead still holds with Fable (and perhaps an upcoming GPT6), but it's not as over as common knowledge by the opinion leaders would have us believe.by Sol-
- The pricing is insane: $1.25/$4.5 for 1M tokens, and $0.15 for cached input!
https://dev.meta.ai/docs/getting-started/pricing-rate-limits
by Tiberium - Maybe Zuck should double down on his "spoiler" role with models rather than compete head-to-head.
He doesn't have to match Anthropic or OpenAI model revenue if he can deflate theirs by 99%.
All he has to do is keep spending a few billion dollars developing frontier models, release them as open weights, and turn coding models into a commodity. He also needs a good OSS reference harness to match. Very few people are in a position to do this and for it to make business sense.
That's quite likely where things are headed regardless, and he could speed it up significantly.
We should all hope models move from proprietary products to commodities the way compilers did.
This may be one of the best things Zuck could do for the world.
by jacobgold - I had a few days of preview access, which was long enough to put together a plugin for LLM. You can try the model out in the terminal like this:
Here's the result: https://tools.simonwillison.net/markdown-svg-renderer#url=ht...uv tool install llm llm install llm-meta-ai llm keys set meta-ai # paste API key here llm -m meta-ai/muse-spark-1.1 "Generate an SVG of a pelican riding a bicycle"For comparison, here's the pelican I got from Muse Spark 1: https://simonwillison.net/2026/Apr/8/muse-spark/
by simonw - Lot more details in the linked report https://ai.meta.com/static-resource/muse-spark-1-1-evaluatio...
From Terminal-bench-2.1 details,
> We use a bash-tool-only agent harness to evaluate 89 Terminal-Bench 2.1 tasks from the official repository, where resources are capped at 6 CPU cores and 8GB RAM.
This disqualifies the results. Each terminal bench task has a cpu upper limit and RAM upper limit. Overriding either is disqualification.
For reference, in tbench-2.1,
1. 0 out of 89 task allow 6 cpu cores (highest is 4, and i think only 1 task)
2. 8 out of 89 tasks allow 8GB RAM
This kind of shady benchmarking (I was talking about it just yesterday in a different context https://news.ycombinator.com/item?id=48838212) takes all joy out of building a harness to improve benchmark performance of a model because no matter what you do, you won't beat the headline (cheating) number. This is presumably why this model is not in the official benchmark leaderboard https://www.tbench.ai/leaderboard/terminal-bench/2.1
As an ex Meta employee, this is a little sad but not massively surprising. 'Number go up' is the core performance evaluation metric until PSC is done and you move on.