Under the Spotlight, Behind the Curtain: The Behemoth That Wasn’t
In the echo chamber of celebratory tweets, technical live streams, and YouTube breakdowns that erupted on April 5, one truth stood quietly in the shadows—Meta’s most important large language model, Llama 4 Behemoth, is not here yet. While the world cheered for the release of Llama 4 Scout and Maverick, Meta’s flagship—its answer to the deepening rivalry with OpenAI, Anthropic, and Google—remains unshipped, still in training, and possibly behind schedule.
Meta declared the launch of “a new era of multimodal AI,” but beneath the sleek engineering and bold claims, insiders describe a mounting pressure cooker—a frantic race to stay ahead as competitors ready their next wave of open-weight models.
“We needed to show something - especially after you guys reported bout our delay - Anything really. Before the opponents’ new releases kill our new baby as well,” a Meta Gen AI engineer told us today.
This is the untold story behind Llama 4.
The Models That Did Drop: Scout and Maverick
On paper, the launch of Llama 4 Scout and Llama 4 Maverick is a milestone for the open-source community. These models, built on mixture-of-experts architectures, push the frontier of inference efficiency, context length, and multimodal capability. Scout—an agile 17B active parameter model with 16 experts—boasts an unprecedented 10 million token context window, a feat unmatched by any released model today. It runs on a single H100 GPU, targeting small-scale researchers, developers, and product teams.
Maverick, by contrast, is the workhorse: same active size, but powered by 128 experts and 400 billion total parameters. It competes directly with DeepSeek V3, Gemini 2.0 Flash, and GPT-4o, delivering similar performance at lower inference costs.
“This is the best performance-to-cost ratio on the market right now,” says one AI benchmark analyst. “If you’re building with Llama 4 Maverick, you’re not just getting OpenAI-level reasoning—you’re doing it for a fraction of the GPU cycles.”
Early metrics bear that out: Maverick has already scored 1417 on LM Arena (CTOL Editor Ken: We don't really trust LM Arena but it is the only one available by far), placing it in the top-tier bracket, albeit with a wide confidence interval due to limited votes.
Yet, as impressive as these models are, they weren’t the headline act Meta had originally planned.
The Phantom of the Launch: Behemoth Remains in the Wings
Behind the spectacle of Scout and Maverick, Llama 4 Behemoth is conspicuously absent. With 288 billion active parameters, 16 experts, and a total size brushing 2 trillion parameters, it is designed not just to match, but to eclipse GPT-o3 mini, Claude Sonnet 3.7, and Gemini 2.5 Pro on technical benchmarks. Internally, it’s considered Meta’s first “Frontier Model”—the one with the raw IQ to change the LLM leaderboard.
But Behemoth’s training is ongoing. Its release date remains vague. And that silence is telling.
“The training process is eating up a lot of resources,” the Meta engineer told us. “It’s not smooth. I’m personally not sure where it will land against the top three right now—Gemini 2.5 Pro, Claude 3.7, O3 Mini.”
While Meta has teased benchmark wins in STEM domains like MATH-500 and GPQA Diamond, they are notably silent on generalist or conversational performance—a red flag for many AI analysts.
One senior machine learning engineer at an AI infrastructure company speculated that “resource bottlenecks and unstable scaling of RL pipelines at this parameter count” might be behind the delays. Others point to strategy: “Meta didn’t want to risk releasing Behemoth until it could guarantee top-tier results—too much is riding on this.”
That includes not just prestige, but a deeper existential bet: If Llama 4 Behemoth fails to clearly outperform Claude or Gemini, Meta risks ceding its position in the arms race of AI dominance, even in the open-source realm it helped define.
Maverick and Scout: Elegant Engineering, Tactical Play
What Maverick and Scout do offer is best-in-class innovation in the middleweight segment. Meta’s choice of MoE architecture—long dismissed as too complex to tune or deploy—has now become its ace in the hole.
In Scout, each token routes only to one of 16 experts plus a shared layer, enabling compute efficiency without sacrificing quality. Its 10 million token context length is not just a technical marvel—it could be a paradigm shift.
“You’re talking about summarizing entire code repositories, multi-document reasoning, or persistent memory for agents,” a researcher said. “It’s a functional revolution.”
Maverick, on the other hand, brings that efficiency to the one-host level, boasting 400B total parameters, mixture-of-experts routing, and enhanced multimodal fluency. It supports text+image input and dominates on visual QA and coding benchmarks like ChartQA and LiveCodeBench.
Their training process was no less rigorous. Using a progressive curriculum-based post-training pipeline, Meta removed “easy data,” filtered prompts using internal judgment models, and looped reinforcement learning with hard-only prompt selection—a brutal but effective recipe for performance uplift.
Scout and Maverick were both distilled from Behemoth—what Meta calls “codistillation.” But the full implications of that teacher model are still unknown.
Early Test Failure against Leading Models: Llama 4 Maverick vs. Claude 3.7 Sonnet
In an early head-to-head logic test, Meta’s Llama 4 Maverick struggled to match the performance of Claude 3.7 Sonnet. Both models were tasked with solving a custom 4×7 matrix reasoning puzzle involving fantasy elements and 15 complex clues. While Claude completed the task quickly and delivered a consistent, verifiable solution on the first attempt, Maverick required multiple continuations and repeatedly failed verification checks—initially assigning duplicate artifacts to a character and later acknowledging "fatal contradictions" in its logic. Even after several correction passes, it continued to miss clues and introduce new inconsistencies. The tester noted that Maverick’s informal style, including emojis and abbreviations, further muddled its reasoning. Though this is just one test, it raises early concerns about Maverick’s reliability in structured problem-solving, particularly when Meta has yet to release its flagship Behemoth model. And again, Meta needs to release a solid Behemoth model to stay relevant against top models.
Open Source with an Asterisk
Meta has long positioned Llama as the spearhead of open-source AI. But the license for Llama 4 has drawn fire. The “700 million MAU” clause prohibits use by any entity with more than 700 million monthly active users—effectively blocking tech giants from adopting it freely.
“It’s a contradiction,” said one AI advocate. “You can't call it open if it's booby-trapped for your competitors.”
Worse, distribution is throttled: to download, users must fill out a form, receive a time-limited link, and are allowed five downloads within 48 hours.
These artificial constraints are frustrating many developers. In the words of a community builder who had early access to Scout:
“It’s the best small model I’ve used. But the rollout? It felt more like applying for a passport than downloading an open-source model.”
The Stakes: AI Strategy in 2025
Why does Behemoth’s absence matter?
Because we are now in the age of open-weight AI warfare, where latency, cost-per-token, and performance on hard reasoning tasks define not just product viability—but national strategy.
Meta’s Scout and Maverick models beat Gemini 2.0 Flash on most metrics. But they do not beat Claude 3.7 Sonnet Thinking or Gemini 2.5 Pro. Only Behemoth has a shot at that.
And the competition is not waiting.
DeepSeek is rumored to release their next generation of open-weight model with full code reasoning capabilities by early May. OpenAI is reportedly preparing its first open-weight model.
If Meta fails to land Behemoth before these drops, the Llama 4 hype wave may dissipate before it can solidify market dominance.
What’s Next: Behemoth, LlamaCon, and the Real Frontier
Meta is placing its bets on April 29, when it will host LlamaCon, promising more technical details and—possibly—a release window for Behemoth. Industry watchers say this could be a defining moment for the company’s AI roadmap.
Until then, we have Scout and Maverick: technically brilliant, publicly released, but strategically interim.
As one analyst put it:
“Llama 4 is Meta’s opening move—but the endgame hinges on Behemoth.”
The future of AI isn’t just being built in public. It’s being trained, behind the scenes, on 32K GPUs, with every hour, every token, a race against time.
Summary:
- Llama 4 Scout: A 17B parameter, 10M context window model that fits on a single H100 GPU. It’s the best-in-class for compact multimodal models.
- Llama 4 Maverick: Larger, 400B parameter model with 128 experts. Beats Gemini 2.0 Flash on most metrics with impressive cost-performance.
- Llama 4 Behemoth: Still training. At 2T parameters, it aims to challenge Gemini 2.5 Pro, Claude 3.7, and O3 Mini—but faces internal doubts.
- Scout and Maverisk are mid-range products & cannot beat top models like Claude Sonnet 3.7 or Gemini 2.5 Pro
- Openness questions: Licensing restrictions and download gating have sparked criticism from the open-source community.
- April 29 at LlamaCon: All eyes turn to whether Meta can finally unveil Behemoth—and whether it will be worth the wait.
The story isn’t over. But for now, the stage is set. The Scout is fast. The Maverick is strong. And the Behemoth? It’s still in the shadows, still training, still uncertain.