Meta Open Sources Llama 4: This Time It's Not "Catching Up," It's "Redefining the Rules"

When I saw the news about Llama 4’s release, I suddenly realized something — Meta has been “competing” in open-source large models for 3 consecutive years.

From Llama 1 to Llama 4, a major update every year, each time pushing the performance ceiling of open-source models up a notch. This time, Llama 4 even directly claimed the title of “strongest open-source model,” and the benchmark data is indeed impressive.

But honestly, what I care more about is — why is Meta so persistent about open-source?

According to normal business logic, training large models costs tens of millions of dollars — compute costs, labor costs, data costs are all substantial. If you open-source all of this, aren’t you “spending money to raise competitors”?

But Meta did exactly that, and for 3 years straight.

My personal understanding is that Meta is playing a very big game — using open-source for ecosystem, using ecosystem for discourse power.

Think about it, if the Llama series becomes the “standard base” of the open-source community, what does that mean? It means thousands of developers, researchers, and enterprises will do secondary development, build applications, and do optimizations based on Llama. These people will become part of the Llama ecosystem, and their innovation will feed back into this ecosystem.

This is similar to how Google open-sourced Android back then — although Android itself didn’t make money, it gave Google discourse power in the mobile internet era. Now Meta might want to do the same thing: using open-source models to seize ecosystem dominance in the AI era.

Is this strategy brilliant? I think it’s quite clever.

It avoids a harsh reality — in the closed-source large model battlefield, Meta would struggle to beat OpenAI and Anthropic. OpenAI has first-mover advantages and technical accumulation; Anthropic has top teams and ample funding; Google has compute and data. If Meta competed head-to-head on closed-source, they might only be “third place.”

But if they switch to the “open-source large model” track, Meta is the undisputed leader. This track doesn’t directly make money, but it can influence the entire industry’s technical direction, standard-setting, and talent flow. In the long run, this influence is more valuable than short-term profits.

Of course, Meta’s strategy also has risks — the ecosystem gained through open-source may not translate into commercial returns.

If developers just “freeload” Llama models without buying into Meta, then Meta truly becomes the sucker “spending money to raise the entire industry.” But from the current perspective, Meta doesn’t seem to care about this risk — or rather, they think this risk is worth taking.

Honestly, I quite admire Meta’s “open-source spirit” (even though there are business considerations behind it).

In an era where “everyone wants to keep their technology close to their chest,” Meta chose to open-source their core large models. This itself is a kind of courage. Regardless of their motives, at least the result is good — the open-source community has better tools, research has a better foundation, and the entire industry’s technical threshold is lowered.

Final question: Do you think Meta’s strategy of “open-source for ecosystem” is “doing charity” or “playing a big game”? If you were Zuckerberg, would you choose to open-source core technology or keep it closed-source to protect commercial interests?

Anyway, my personal attitude is — whatever their motives, having open-source to use is great. As for whether Meta can make money from this strategy, that’s their problem.