On the manicured lawns outside Building 21 on Meta’s sprawling Menlo Park headquarters, live llamas meandered with languid indifference, drawing clusters of developers who momentarily abandoned technical discussions for selfies with the stoic, woolly ambassadors of Meta’s family of large language models.
Inside Building 21, I shivered. The cavernous auditorium’s air conditioning was cranked up high. Mood lighting bathed the space in Meta’s signature blue shade, and dance music blasted from speakers, lending a nightclub ambiance to the event that clashed oddly with the earnest, tech-focused agenda.
“Rise and shine!” a Meta PR person chirped as I took a seat.
This was LlamaCon, Meta’s first-ever conference for AI developers. Its timing felt oddly defensive. Earlier this year, DeepSeek, an open-source AI model from China that delivered groundbreaking performance with computational efficiency, had much of Silicon Valley, including Meta’s AI division, panicked.
Around the same time, Meta announced that it would spend $65 billion in 2025 to build out AI infrastructure. Weeks after that, the company released Llama 4, the latest version of its LLM family. Mark Zuckerberg called it “the beginning of a new era for the Llama ecosystem.” Almost immediately after, Meta was accused of artificially inflating Llama models’ performance benchmarks, a claim that executives pushed back against.
LlamaCon, I thought, was Meta’s moment to reclaim trust and clarify its AI strategy.
Pranav Dixit
Onstage, Meta’s Chief Product Officer Chris Cox framed the company’s open source strategy as principled rather than reactive: “We were a startup once, too,” he said in the keynote. “We built this place on open source.”
The subtext was clear: Meta wants developers to see Llama as their path to autonomy and flexibility in an increasingly closed AI ecosystem dominated by offerings from OpenAI, Microsoft, and Google.
AP Photo/Jeff Chiu
Llama knelt to competitors
LlamaCon featured several announcements, including the launch of a new Llama API that Meta says will make it easy for developers to integrate its models using familiar tools and interfaces. Some tasks will be possible with just a few lines of code.
Meta also announced partnerships with companies to make AI run faster; a security program with AT&T and others to fight AI-generated scams; and $1.5 million in grants to startups and universities around the world using Llama.
Conspicuously absent, however, was what many developers had actually come hoping to see: a new reasoning model to compete with what has rapidly become table stakes in the AI industry, including in Chinese open-source alternatives like DeepSeek and Alibaba’s Qwen.
In a conversation with Databricks CEO Ali Ghodsi, Zuckerberg seemed to tacitly acknowledge these shortcomings.
Pranav Dixit
“Part of the value around open source is that you can mix and match,” he said. “If another model, like DeepSeek, is better, or if Qwen is better at something, then, as developers, you have the ability to take the best parts of the intelligence from different models. This is part of how I think open source basically passes in quality all the closed source [models]…[It] feels like sort of an unstoppable force.”
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Vineeth Sai Varikuntla, a developer working on medical AI applications, echoed this sentiment when I spoke with him after the keynote.
“It would be exciting if they were beating Qwen and DeepSeek,” he said. “I think they will come out with a model soon. But right now the model that they have should be on par—” he paused, reconsidering, “Qwen is ahead, way ahead of what they are doing in general use cases and reasoning.”
Missing model improvements
The online reaction to LlamaCon reflected similar disappointment across developer communities.
On Reddit’s r/LocalLLaMA, the top post was titled “No new models in LlamaCon announced.” Users compared Meta unfavorably to Qwen 3, which Alibaba strategically released just one day before Meta’s event.
“Good lord. Llama went from competitively good Open Source to just so far behind the race that I’m beginning to think Qwen and DeepSeek can’t even see it in their rear view mirror anymore,” wrote one user. Others debated whether Meta had planned to release a reasoning model but pulled back after seeing Qwen’s performance.
On Hacker News, a popular forum for developers and tech industry professionals, some criticized the event’s focus on API services and partnerships rather than model improvements as “super shallow.” And one user on Threads summed up the event simply as “kinda mid.”
When I asked Meta how they measured the success of the event, they declined to comment.
“It did seem like a bit of a marketing push for Llama,” Mahesh Sathiamoorthy, cofounder of Bespoke Labs, a Mountain View-based startup that creates AI tools for data curation and training LLMs, told me. “They wanted to cast a wider net and appeal to enterprises, but I think the technical community was looking for more substantial model improvements.”
Still, LlamaCon won praise from Wall Street analysts tracking the company’s AI strategy. “LlamaCon was one giant flex of Meta’s ambitions and successes with AI,” Mike Proulx of Forrester told me.
Jefferies analyst Brent Thill called Meta’s announcement at the event “a big step forward” to becoming a “hyperscaler, a term referring to large cloud serve providers that offer computing resources and infrastructure to businesses.
Some developers using Llama models were equally enthusiastic about the technology’s benefits. For Yevhenii Petrenko of Tavus, which creates AI-powered conversational videos, Llama’s speed was crucial. “We really care about very low latency, like very fast response, and Llama helps us use other LLMs,” he told me after the event.
Hanzla Ramey, CTO of WriteSea, an AI-powered career services platform that helps job seekers prepare résumés and practice interviews, highlighted Llama’s cost-effectiveness: “For us, cost is huge,” he told me. “We are a startup, so controlling expenses is really important. If we go with closed source, we can’t process millions of jobs. No way.”
The future’s form and function
Toward the end of the day, Zuckerberg joined Microsoft CEO Satya Nadella onstage for a wide-ranging chat about AI’s future. One comment stood out.
Llama 4, Zuckerberg explained, had been designed around Meta’s preferred infrastructure — the H100 GPU, which shaped its architecture and scale. But he acknowledged that “a lot of the open source community wants even smaller models.” Developers “just need things in different shapes,” he said.
AP Photo/Jeff Chiu
“To be able to basically take whatever intelligence you have from bigger models,” he added, “and distill them into whatever form factor you want — to be able to run on your laptop, on your phone, on whatever the thing is…to me, this is one of the most important things,” he said.
It was a candid admission. For all the pageantry, LlamaCon wasn’t a coronation. It was Meta still mid-pivot, trying to convince developers — and maybe itself — that it can build not just models, but momentum.
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