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Muse Code and Muse Spark 1.2

research.meta.ai

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Re: Muse Code and Muse Spark 1.2

#91

Meta is offering a 10x discount on input ($0.10 vs. $1.25/Mtok) and 20x discount on output ($0.20 vs. $4.25/Mtok) if you opt in to let them train on your data. https://developer.meta.com/ai/models/muse-spark/

This makes it a very interesting alternative to Deepseek for personal work where I don't care about the training - judging by the AA benchmarks it seems like overall cost per task is similar to the new Deepseek Flash but with better benchmarks (and inbuilt vision capabilities).

Except with one you know they'll release the weights and architecture back to the community, with the other, it leans towards they won't do that.

Re: Muse Code and Muse Spark 1.2

#93

Meta is offering a 10x discount on input ($0.10 vs. $1.25/Mtok) and 20x discount on output ($0.20 vs. $4.25/Mtok) if you opt in to let them train on your data. https://developer.meta.com/ai/models/muse-spark/

I've been surprised by the reception to this, as OpenAI, for a while now, has had free API usage when data sharing is enabled (https://help.openai.com/en/articles/10306912-sharing-feedbac...)

Re: Muse Code and Muse Spark 1.2

#94
post #56
post #50

Earlier quoted context omitted.

We can throw benchmarks in the bin by now. Each one I've seen is heavily biased and skewed. It holds very little reliable data points (unfortunately)

My conclusion is the opposite. If benchmarks were meaningless, surely Meta would be able to find some benchmark that shows they are better than Sol and Fable. The fact that they can't do that tells me that benchmarks still do mean something.

Muse 1.1 performed relatively well according to benchmarks, putting it within spitting distance of the premier models. However, based on the results I got from it and the review videos I watched, it wasn’t even close.

Opus 5 is incredible at making games. Almost like a generation better than other models from my experience. You won't see that if you just look at the popular benchmarks..

You have to test each model on your actual use case to see how well it really performs.

Re: Muse Code and Muse Spark 1.2

#95
post #26

Last I heard, everyone at Meta was using Claude Code. Any insiders know how Muse Code is doing internally?

Meta lets engineers use the best tools for the job. I doubt anyone internally is going to be rushing to switch from Claude Code or Codex.

Re: Muse Code and Muse Spark 1.2

#96
post #86
post #76

Earlier quoted context omitted.

I hate to say this and this is because I fucking despise meta. But between DeepSeek and Meta, and trust they handle the training data correctly, I trust meta.

What do you mean by "correctly"?

For example, OpenCode says they have a ZDR with DeepSeek. Some of us are skeptical that's going to be properly honored. There's no way to know.

Re: Muse Code and Muse Spark 1.2

#98
post #74

Earlier quoted context omitted.

I actually really like that pricing strategy. It's very transparent

I love the idea of this pricing strategy but there is no way meta is not training on your data regardless of your monthly invoice

So you think the only difference between the $1.25/million token plan and the $0.10/million token plan is that you pay them more to both lie to you and breach their contractual obligation to you?

Re: Muse Code and Muse Spark 1.2

#99
post #93

Meta is offering a 10x discount on input ($0.10 vs. $1.25/Mtok) and 20x discount on output ($0.20 vs. $4.25/Mtok) if you opt in to let them train on your data. https://developer.meta.com/ai/models/muse-spark/

I've been surprised by the reception to this, as OpenAI, for a while now, has had free API usage when data sharing is enabled ( https://help.openai.com/en/articles/10306912-sharing-feedbac... )

I tried following this page, and it's certainly a lot more complex than what Meta is offering. Different price tiers, opt-in configurations, usage based availability.. I'll take the 10x discount for flipping a param switch over this all day long.

Re: Muse Code and Muse Spark 1.2

#100
The most interesting thing here is the kernel optimization graph.

It look like all models were still improving, when they cut off the experiment.

It reminds me of a genetic algorithm. The graph is the same: long plateaus and then massive leaps.

The only difference between the models seems to be how quickly they arrive.

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