Llama 4: Meta's New AI Model - Evolution, Features, and Comparison

Meta’s newest AI model, Llama 4, has arrived. In this blog post, we’ll explore what makes Llama 4 special, how it evolved from earlier Llama models (like Llama 2 and Llama 3), and how it stacks up against other top AI models as of April 2025, including OpenAI’s GPT-4, Google’s Gemini 1.5, Anthropic’s Claude 3, and Mistral’s Mixtral. We’ll break down the technical details in an accessible way, using analogies to make it easy to grasp, and include a handy comparison table for a quick overview. Let’s dive in!

From Llama 2 to Llama 4: The Evolution of Meta’s AI “Herd”

Meta’s Llama series has galloped forward quickly in just a couple of years. To appreciate Llama 4, it helps to see how far the “herd” has come.

• LLaMA 1 (2023) – The original LLaMA (sometimes called Llama 1) was released in early 2023 as a research model. It came in sizes from 7 billion to 65 billion parameters, and while its weights weren’t fully open to the public at first, it demonstrated that a relatively smaller model (13B) could match the performance of larger models like GPT-3 (175B) on many tasks. LLaMA 1 had a context window (memory for text) of 2,048 tokens. It wasn’t openly available for commercial use, but it set the stage for what was to come.

• Llama 2 (July 2023) – Llama 2 was Meta’s big step toward open AI. It was released free for research and commercial use (with some restrictions for very large-scale services), signaling Meta’s commitment to “open-source” AI models . Llama 2 came in 7B, 13B, and 70B parameter versions – much like offering small, medium, and large "brain" sizes. Despite having fewer parameters than some closed models, Llama 2 was trained on 40% more data than LLaMA 1, which boosted its knowledge and abilities. It also doubled the context window to 4,096 tokens (roughly equivalent to keeping 3–4 pages of text in mind). This meant it could handle longer prompts or conversations more coherently than LLaMA 1. Llama 2 also introduced fine-tuned “chat” versions with reinforcement learning from human feedback (RLHF) for better alignment with user intentions. At a glance, Llama 2 made advanced AI more accessible – anyone could tinker with it locally or in their products, without relying on a closed API. It quickly became the go-to model for the open-source AI community in late 2023.

• Llama 3 (April 2024) – The next leap came with Llama 3. Meta released Llama 3 models in 2024 with improved capabilities and a focus on scale. The initial Llama 3 family included models of 8B and 70B parameters (again with both base and instruction-tuned versions). One of the standout upgrades was an extended context length of 8,192 tokens, double that of Llama 2 (Llama 3 Guide: Everything You Need to Know About Meta's New Model and Its Data). This is like giving the model a much larger working memory – about 6–7 pages of text at once – allowing more complex interactions or larger documents to be processed in one go. Meta also made Llama 3 more multilingual, covering over 30 languages, whereas earlier Llamas were primarily English-focused. Another unique focus of Llama 3 was on safety and security: Meta introduced a cybersecurity evaluation benchmark (CyberSecEval) to test Llama 3 against prompt injection attacks and other adversarial inputs, showing an emphasis on making the model robust in real-world usage.

How did Llama 3 perform? In benchmarks, even the 8B and 70B Llama 3 models showed strong results. They outperformed other open models like Mistral’s 7B and Google’s smaller PaLM models on many academic benchmarks (MMLU, GSM8K, HumanEval, etc.), demonstrating improvements in reasoning, coding, and math problem-solving. Llama 3 was on par with other leading models of the time in many tasks. Meta’s strategy seemed to be quality over sheer size – optimize training and data to get top performance from relatively moderate model sizes.

• Llama 3.1 (July 2024) – Meta didn’t stop at Llama 3. Just a few months later, they unveiled Llama 3.1, which introduced a massive model: a 405 billion parameter variant. This 405B model (available through partnership platforms like IBM’s watsonx.ai) was, at the time, the largest open-access LLM, and it reached unprecedented performance levels. On knowledge tests and reasoning benchmarks, Llama 3.1’s 405B model essentially achieved parity with top proprietary models like GPT-4, Google’s Gemini, and Anthropic’s Claude. For example, on an academic exam benchmark (MMLU), Llama 3.1’s giant model scored ~87.3%, slightly ahead of GPT-4 Turbo and Anthropic Claude 3, and on par with the best from Google. It also excelled in coding and math – on a math word problems test (MATH benchmark), it scored 73.8%, very close to GPT-4’s 76.6% and above other peers. This was a stunning achievement: an open model matching the mighty GPT-4 on tough tasks . The catch was the model’s size – 405B parameters are huge (the model file is nearly 2 TB!), making it hard to run outside of specialized hardware. Still, Llama 3.1 showed that Meta could push to the cutting edge. It also expanded context length further (up to 128k tokens in some versions) and improved tool-use and multilingual abilities. Meta hinted that the next goals were making models more multimodal (able to handle images, etc.) and pushing context lengths even further.

Enter Llama 4 (2025): Building on this progress, Meta’s Llama 4 takes a bold new approach to achieve even greater capability without simply blowing up the model size. It introduces a new architecture and features that set it apart from previous Llamas. Let’s unpack what’s inside Llama 4.

Llama 4 – What’s New and Why It Matters

Llama 4 isn’t just a bigger copy of Llama 3 – it’s a re-engineered beast altogether. Meta describes Llama 4 as the start of “a new era for the Llama ecosystem”, and for good reason. Here are the key innovations of Llama 4:

A. Mixture-of-Experts Architecture (More “Brain” for Less Compute)

One of the most exciting changes in Llama 4 is under the hood: it uses a Mixture-of-Experts (MoE) architecture. Don’t let the jargon scare you – the idea is actually intuitive. Instead of a single giant neural network that has to “know” everything in its weights, Llama 4 is like an ensemble of specialists that work together. Think of it like a panel of 128 experts: when you ask a question or give it a prompt, the model intelligently activates only the few experts that are most relevant, rather than all 128 at once. This way, you get the benefit of a huge total knowledge base, but the computation per token stays much lower.

In traditional models (like GPT-4 or Llama 2), every parameter may be involved in producing each word of output. MoE changes that. As Meta explains, the model’s layers are subdivided into multiple experts, and a gating mechanism decides which experts handle each token. If the prompt is about math, maybe the “math expert” nodes fire; if it’s about code, the “coding experts” do the heavy lifting, and so on. By not activating the entire network for every input, the model can have a far larger total capacity without a proportional increase in runtime. It’s a bit like having a 400- billion-parameter brain but only using a 17B-sized slice of it at any given moment.

Llama 4 comes in two main variants (so far) leveraging MoE:

• Llama 4 Scout – the smaller model – has a total of 109B parameters divided into 16 experts, but only ≈17B active parameters are used per token on average. It’s as if Scout has 16 mini-brains that together contain a lot of knowledge, but querying it feels as fast as using a 17B model. Meta trained Scout on a whopping 40 trillion tokens of data (text and images), which is an enormous dataset – roughly ten times what Llama 2 was trained on. Thanks to the MoE design, Scout’s performance rivals or exceeds models that have much larger active sizes, while keeping cost low. In other words, Scout is efficient: it’s tuned to give strong results (on coding, reasoning, long documents, etc.) without needing a supercomputer to run. Notably, Scout can even run on a single high-end GPU (like an NVIDIA H100) for inference, making it relatively accessible to deploy.

• Llama 4 Maverick – the flagship model – takes it up a notch. Maverick has 128 experts and about 400B total parameters, yet it also uses only 17B active parameters per token (the same computational cost as Scout). Essentially, Maverick has many more experts, meaning a much broader base of knowledge and specialization. With 128 expert “personalities” in the model, Meta can cram in a diversity of training data and skills. Maverick “draws from the knowledge” of a 400B-parameter network while still being as fast as a 17B model in use. This design is cutting-edge – a similar approach was seen in models like Google’s Switch Transformers and more recently Mistral’s Mixtral (which we’ll discuss later), but Meta’s scale here is unprecedented. According to Meta’s internal evaluations, Llama 4 Maverick beats OpenAI’s GPT-4 and Google’s Gemini 2.0 on a wide range of benchmarks. We’ll dig into comparisons soon, but the takeaway is: Maverick offers GPT-4-level performance “across the board” in many tasks, thanks to its massive MoE architecture.

What do these numbers mean for a non-expert? It means Llama 4 managed to build a smarter model not just by making one giant brain, but by creating a federation of smaller brains that cooperate. This makes it extremely computationally efficient. It’s like having a library of 128 specialized books but only opening the two or three that have the info you need, instead of flipping through one huge encyclopedia every time. The result: speed and scale. Users get answers as fast as a smaller model would respond, but with the knowledge (almost) of a much bigger model. (What about Llama 4 Behemoth?) Meta has also teased an even larger model, code-named “Behemoth.” It’s currently in preview and aimed to be a “teacher model” for distilling knowledge to smaller models. Behemoth is reportedly around 2 trillion total parameters with 16 experts, and a staggering 288B active parameters per token (meaning it’s truly enormous). This is not something you’d run yourself – it likely requires a server farm – but it shows Meta is exploring the high end of scaling.

B. Native Multimodal Training (Text and Images Together)

Another big highlight of Llama 4 is multimodality – the ability to handle not just text, but images (and even video) as input. While previous Llama models (and many other LLMs) were trained purely on text and then later adapted to images via additional fine-tuning, Llama 4 did this natively from the start. Meta pre-trained Llama 4 on large amounts of unlabeled text, image, and video data together. This means the model learned to understand pictures and text in a unified way, rather than treating vision as an afterthought.

In practical terms, Llama 4 can accept image inputs alongside text prompts and generate text responses about those images. For example, you could show Llama 4 a photo and ask, “What’s happening in this image?” or even something complex like “In this diagram, which part represents the power supply?” and it can answer. Meta reports that Llama 4 models have “broad visual understanding”. They can handle multiple images at once, and even refer to specific regions within an image if you ask (like “What is this person holding in the highlighted region of the photo?”). Essentially, vision is built into the model’s core training, resulting in what Meta calls “native multimodality.”

This is similar to what OpenAI did with GPT-4 (which has a vision mode) and what Google’s Gemini is designed for, but Meta’s twist is making it part of an open model release. For developers and researchers, having an open multimodal model is huge – it’s like having the powers of ChatGPT with Vision, but in a model you can run and fine-tune yourself.

From a training perspective, mixing text and images from the get-go can enrich the model’s understanding. An analogy: if a model only reads text, it’s like someone who learned about the world solely through reading books. Adding images and video is like also letting them watch documentaries and examine pictures – they develop a more rounded knowledge. Llama 4’s training fused different data types “early in the processing pipeline,” which helped it align visual and textual concepts. The result is a model that seamlessly handles multimodal queries. You can chat with Llama 4 about an essay, then show it a chart and ask a question, all in one session.

Why it matters: Multimodal AI opens up a world of possibilities. Llama 4 can aid in tasks like image analysis, generating captions for pictures, explaining memes, or helping with design by analyzing diagrams – all through the same chat interface used for text. For general tech enthusiasts, this means AI is getting closer to how we humans process information: we use both language and vision together. Llama 4 is one of the first major open models to really embody that.

C. Massive Context Window – “Infinite” Memory?

Perhaps one of the most jaw-dropping features of Llama 4 (especially the Scout model) is its context window size. Llama 4 Scout supports an industry-leading context length of up to 10 **million** tokens. For comparison, GPT-4’s max context is 32,000 tokens in its extended version, and even Claude 3 (known for long context) initially offered 200,000 tokens – which is tiny compared to 10 million! This essentially means Llama 4 can read and remember an entire library’s worth of text at once. In more relatable terms, 10 million tokens is about 8 million words, or roughly 15,000+ pages of text. It’s far beyond what most use cases require today, but it future- proofs the model for extremely long documents or continuous conversations.

How is this even possible? Normally, transformer models face scaling issues with context (computational cost grows quadratically with the number of tokens). Meta achieved this with a novel approach called “interleaved Rope” (iRope) and some clever engineering in the attention mechanism. They dropped positional embeddings and used interleaved attention layers, combined with an inference-time trick (temperature scaling on attention) to allow the model to extend its window dramatically. In essence, they found a way to keep the model stable and accurate even as you feed in ridiculously long input sequences. Meta refers to it as a step toward “infinite context length” – a bit of an overstatement, but 10 million is so high it might as well be infinite for practical purposes. Llama 4 can maintain context over days of dialogue or summarize entire datasets in one go.

Now, practically speaking, not everyone will be able to shove 10M tokens into the model due to memory and speed constraints (and few have a need to). But even using, say, a few hundred thousand tokens reliably is a big deal – you could feed in hundreds of pages of reference material and then ask questions that require synthesizing information from all of it, without the model forgetting earlier parts. This could enable applications like deep legal document analysis, massive codebase understanding (imagine debugging across an entire code repo in one prompt), or very long-running chatbots that accumulate knowledge over time.

In tests of long-context capabilities like the “Needle in a Haystack” benchmark (where a single fact is hidden in a huge text), Llama 4 maintained high accuracy, finding the needle with ~99% success even in gigantic inputs. Meta’s goal here is likely to enable new use cases and research. It’s an order of magnitude leap over what others offer publicly.

For a general enthusiast, you can think of context length as the AI’s working memory. Llama 4 has an unprecedented memory span – it’s like talking to someone who never forgets anything you said, even if the conversation lasts for a year! That can be powerful, but also raises challenges (like how do we ensure the model doesn’t get confused or stuck on irrelevant details from 5 million tokens ago?). For now, it’s a bragging rights feature that also points to Meta’s research prowess.

D. Performance and Benchmark Highlights

At the end of the day, what most people care about is: How good are Llama 4’s answers and capabilities? The short answer: extremely good – Llama 4 is among the best AI models in the world as of 2025, especially considering it’s openly available. But it’s worth noting the nuance in comparisons.

Meta’s internal benchmarks (and some third-party evaluations) show that Llama 4 Maverick often matches or exceeds the performance of OpenAI’s GPT-4 and Google’s Gemini 2.0 on many tasks. This includes things like coding problems, reasoning puzzles, answering questions that require knowledge, understanding long contexts, and even interpreting images. In particular, Meta claims Maverick beats GPT-4 and Gemini 2.0 “across the board” on a wide range of multimodal (text+image) benchmarks.

However, the AI landscape is a moving target. By April 2025, other players have also stepped up. OpenAI has an improved GPT-4.5 in the works, Google has Gemini 2.5, and Anthropic has Claude 3.7 – these next-gen models are slightly ahead of Llama 4 in some evaluations. According to TechCrunch, Llama 4 Maverick outperforms GPT-4 (2023) and Gemini 2.0, but doesn’t quite match the very latest Gemini 2.5 Pro, Claude 3.7 “Sonnet,” or GPT-4.5 on certain advanced benchmarks. In plain terms, Llama 4 is top-tier, but not an outright king of the hill in every category – the closed models from Google/Anthropic/OpenAI are still evolving too.

That said, the differences at the top end are not huge. Llama 4 is within striking distance of the best. And for many real-world tasks, you would find it just as capable, whether it’s writing an essay, debugging code, answering complex questions, or analyzing an image. It’s also likely to continue improving as the open community fine-tunes it or as Meta releases updates (perhaps Llama 4.1, etc.).

A few specific strengths of Llama 4 worth noting:

• Coding and Reasoning: Llama 4 shines in coding tasks and logical reasoning. It was built to rival models like GPT-4 in those domains. Early benchmarks show it can generate correct code solutions, explain its reasoning steps, and handle tricky logic puzzles on par with the best AIs. Its MoE architecture might contribute here, as some experts can be specialized for code or math.

• Multilingual Abilities: Building on Llama 3’s multilingual training, Llama 4 has been trained on a wide array of languages (and even on image+text pairs from various locales). It likely supports dozens of languages with high competency. Meta uses it in their apps across 40 countries already, with English and a few major languages fully supported in multimodal mode. So, it’s not just an English whiz; it’s global.

• Knowledge and Accuracy: With training on 40+ trillion tokens and hundreds of billions of parameters, Llama 4 has a vast store of knowledge. It’s up-to-date as of its training cutoff (likely 2024) and can recall facts or answer questions with high accuracy. Its performance on knowledge tests (like MMLU, a test of academic subjects) is near the top of the chart – 87% accuracy level as mentioned earlier, which is graduate-level expertise in many areas.

• Safety and Alignment: While not a “capability” in the raw sense, it’s worth mentioning that Meta has continued to implement safety features in Llama 4. They included “robust system-level safety measures and guardrails” in the model release. And interestingly, by keeping Llama 4 open, it allows the community to inspect and modify the model for alignment, which is a different approach from relying on a company’s closed model. Meta likely fine-tuned Llama 4 with human feedback and adversarial testing (possibly building on their work with Llama 3’s safety evaluations). The open model can be further refined by users to fit specific use cases and values, which is an advantage if done responsibly.

With these features in mind, let’s see how Llama 4 compares to its contemporaries in a more systematic way.

Llama 4 vs. Other Leading LLMs (April 2025)

The AI field is crowded with heavy hitters, each with their own strengths. How does Llama 4 stack up against OpenAI’s GPT-4, Google’s Gemini 1.5, Anthropic’s Claude 3, and Mistral’s Mixtral models? Below is a comparison table of some key specs and capabilities, followed by a discussion of each.

(Table: A comparison of Llama 4 and other leading large language models. “Active” parameters refers to the number of parameters used per inference token in MoE models. Context lengths marked as extended/experimental indicate limited availability features. Not included here is DeepSeek R1, which is also a top performing open-source LLM with MoE architecture.)

Now, let’s break down the comparison in words:

OpenAI GPT-4 (and GPT-4 Turbo)

GPT-4 is the model that kicked off the AI boom of 2023, and it remains a reference point for excellence. It’s a dense model (no Mixture-of-Experts) with an undisclosed size – estimated to be on the order of a trillion parameters – and it demonstrated remarkable abilities in reasoning, coding, and knowledge. GPT-4’s strengths include its strong reasoning and creativity; it can solve problems step-by-step and generate coherent, contextually rich responses. It also has a vision model (as of late 2023) that can interpret images. However, GPT-4 is a closed model – you can only access it via OpenAI’s API or ChatGPT interface, and its internal workings and training data are proprietary.

Compared to Llama 4, GPT-4 (original 2023 version) is roughly on par or slightly behind in many benchmarks. Llama 4 Maverick was shown to slightly outperform the original GPT-4 (sometimes referenced as GPT-4o) on various coding and reasoning tasks. That’s a big deal, considering GPT-4 was the gold standard. However, OpenAI has iterated as well – GPT-4 Turbo (a faster version) and rumored GPT-4.5 improvements narrow the gap. In some evaluations, Llama 4 Maverick matches GPT-4 Turbo’s performance, and only GPT-4.5 may edge it out on certain advanced tasks.

In terms of context length, GPT-4 can go up to 32K tokens with the extended version, which was impressive until these newer models came with hundreds of thousands or even millions of tokens context. So Llama 4 blows past GPT-4 in that regard (10M vs 32K). In terms of multimodality, GPT-4 does have image input capability, so it’s comparable to Llama 4. But there is one key difference: openness. Llama 4 is open (you can download and run Scout/Maverick yourself, given enough hardware), whereas GPT-4 is closed. This means with Llama 4 you have more control – you can fine-tune it on custom data, host it in your own compute infrastructure, and integrate it without calling an external API. Some companies and developers prefer this for privacy or cost reasons.

Google Gemini (1.5 and 2.0)

Google’s Gemini is another top contender. Gemini was developed by Google DeepMind and designed from the ground up to be multimodal and to incorporate some of the advanced planning/reasoning techniques (inspired by AlphaGo, reportedly). Gemini 1.0 launched late 2023, and Gemini 1.5 (Pro) was announced in early 2024 with major improvements. The Gemini 1.5 Pro model boasts a 128K token standard context window, with an experimental 1 million token mode for select users. This was a breakthrough at the time – Google showed that 1.5 Pro could consistently handle up to 1M tokens in tests, making it the first model to really push context that far in production.

Gemini 1.5 is fully multimodal, handling text and images, and is deeply integrated into Google’s ecosystem (it powers features in Gmail, Google Docs, etc., and is available via Google’s Vertex AI platform). One of its celebrated strengths is in-context learning – it can learn new skills or follow examples provided in a prompt very effectively, thanks in part to that long context. For instance, if you show it how to format a few entries of a table, it can continue the pattern for many more, all within one interaction. Google also emphasized Gemini’s efficient architecture – while details are not public, it likely uses a mixture of techniques (maybe some parallelism or adaptive computation, though not exactly MoE as far as known) to achieve good speed.

When comparing to Llama 4: Gemini 1.5 Pro vs Llama 4 Maverick – they are in a similar league. Llama 4 slightly one-ups Gemini 1.5 on some reasoning benchmarks, but Google has since released Gemini 2.0 (late 2024) and even previewed Gemini 2.5 Pro. In Meta’s own words, Maverick beat Gemini 2.0 “Flash” across the board, but Gemini 2.5 Pro still holds an edge in certain areas like complex reasoning or coding challenges.

Spec-wise, Google has not disclosed Gemini’s parameter count. Rumors suggest it’s comparable to GPT-4, but details are sparse. It’s a dense model (not MoE), and Google leveraged huge compute resources to train and run it. Gemini 2.0 and 2.5 likely incorporate further optimizations and possibly chain-of-thought training.

Anthropic Claude 3 (and 3.5, 3.7)

Anthropic’s Claude model is known for its focus on safety and extremely friendly conversational style. Claude 2, released in 2023, wowed users with a 100K token context and a very helpful demeanor. In 2024, Claude 3 was introduced, and it came in a few flavors (codenamed Opus, Sonnet, Haiku) targeting different speed and capability trade-offs. Claude 3 models launched with 200K token context windows (and the architecture design capable of exceeding 1M tokens). They also expanded to have vision capabilities – able to process images like photos, charts, and even PDFs with diagrams. Anthropic’s approach heavily emphasizes reliable behavior: Claude tends to refuse problematic queries more appropriately and is trained with techniques like “Constitutional AI” to align its responses with ethical principles.

Comparatively, Claude 3’s strongest model (Claude 3.7 “Sonnet”) vs Llama 4 are fairly close in raw capability. In fact, the internal Meta benchmarks cited Claude 3.7 Sonnet as slightly above Llama 4 Maverick on certain reading comprehension and reasoning tasks. Claude’s edge often comes in tasks that involve following complex instructions or maintaining a conversation over many turns – it has been praised for being very good at understanding user intent and keeping context. Its large context window (though now overshadowed by Llama 4’s crazy 10M) means it can handle long chats or documents too.

However, one area Llama 4 might outshine Claude is raw knowledge and coding/math – Meta’s data breadth and MoE specialization can give it an advantage on factual recall or tricky code generation where Claude sometimes falters. Earlier benchmarks showed the 405B Llama 3.1 model beating Claude 3 on a math test by a sizable margin. With Llama 4’s refinements, we can expect it to continue being excellent at those “hard” tasks.

Anthropic is likely working on Claude 4, but as of April 2025, Claude 3.5 or 3.7 is their latest. These models remain closed-source, available via API and a chat interface (Claude.ai). One unique feature Anthropic introduced makes it more appealing to the general audience: letting Claude perform some “computer use” actions, like browsing or running code (in controlled ways) as part of responding to queries. That edges into tool-use and agentic workflows. Llama 4, being open, doesn’t have built-in tool APIs, but developers can combine it with tools easily in their own setups (for example, hooking it up to a web browser or a calculator in an open-source project).

Mistral AI’s Mixtral Models

Mistral AI, a startup, made waves in late 2023 by releasing a very strong 7B open model (Mistral 7B) and then an innovative MoE model called Mixtral. Mixtral 8×7B (Dec 2023) and Mixtral 8×22B (Apr 2024) demonstrated that sparse MoE models could outperform larger dense models while staying efficient. For instance, Mixtral 8×22B (which effectively has up to ~176B parameters total) was shown to outperform Meta’s own Llama 2 70B on many benchmarks, while being 6× faster at inference. It used 8 experts and could dynamically choose which ones to use per query, similar conceptually to what Llama 4 does, but on a smaller scale. Mixtral 8×22B had an effective context window of 64K tokens, which at the time was among the best for open models.

Comparing Mixtral 8×22B to Llama 4 – Llama 4’s MoE is more advanced (128 experts vs 8), larger total params (400B vs 176B), and a hugely longer context (10M vs 64K). So purely on spec, Llama 4 eclipses Mixtral. Performance-wise, Mixtral was excellent for its time, but Llama 4’s extensive training and size likely put it well ahead on most tasks. Both Mistral and Llama are open models.

One thing Mixtral natively supports is function calling and constrained output modes – features to help developers get structured outputs. Llama 4 doesn’t explicitly advertise that, but one can implement similar constraints at application level or via fine-tuning.

Other Notable Models

There are a few other models that we did not explore in detail for this article:

• DeepSeek’s models (R1,V3) – These state-of-the-art open source MoE models are created by Chinese entrepreneurs. They perform on par with OpenAI's best reasoning models (o1 and o3-mini). They likely spurred Meta to accelerate Llama 4.

• OpenAI GPT-4.5/GPT-5 (rumored) – OpenAI is presumably working on the next iteration. If it arrives, the bar may move yet again. For now, GPT-4 remains the flagship from OpenAI.

• Smaller open models like Falcon, PaLM (Open), etc. – There are dozens of others, but by 2025, Llama 4 and Mixtral overshadow them in the open category for top performance. Closed rivals like Baidu’s ERNIE or Meta’s own CODE Llama (for code specifically) also fill niches.

Implications for the Future

Llama 4 represents another milestone: it shows that open models can reach state-of-the-art AI performance. For tech enthusiasts and developers, this is exciting. It means you’re not locked into using an API from a handful of big companies; you can actually get a state-of-the-art model and run it yourself (or through an open platform like Hugging Face). People can build new applications on top of Llama 4, customize it, inspect it for safety, and so on, without a gatekeeper. It’s akin to the impact Linux had in the operating system world, providing an open alternative to proprietary OSes.

Meta’s strategy seems to be “community over secrecy.” By releasing models like Llama 4, they gain a lot of goodwill and involvement from researchers and developers worldwide. Already, we see Llama 4 being fine- tuned for various domains: law, medicine, education, etc., by different organizations. This could greatly accelerate domain-specific AI applications. Of course, there are responsibilities too. Meta has license restrictions: notably, Llama 4 currently disallows usage by or in the European Union out of caution with forthcoming AI regulations, and companies with over 700M users need a special license (to prevent a competitor like Google from just taking it and using it freely). So it’s “open” but with some caveats. The open-source community is also tasked with using it ethically – ensuring it’s not misused for deep fakes, harassment, etc. Meta’s included safety measures, but open models can be fine-tuned off-track with certain specialized techniques.

Llama 4 reveals clear signals towards where AI is going:

• Mixture-of-Experts and efficiency – We’ll likely see more models adopt MoE or similar techniques to expand capacity without ballooning costs. It wouldn’t be surprising if OpenAI or Google incorporate some form of MoE in future models (if they haven’t quietly already). The idea of “having your cake and eating it too” – big model and fast – is very attractive.

• Long context and memory – Llama 4’s 10M context might be overkill, but we can expect context windows to keep growing. We might soon treat AI models like persistent assistants that can remember an entire lifetime of interactions. Techniques for retrieval (using external vector databases to give models even more info) combined with huge context will blur the line between what’s in the model and what it can just look up on the fly. Through effective implementation of retrieval augmented generation (RAG), general LLMs may be all we need even in domain-specific use cases.

• Multimodality – It’s clear that being good at just text is not enough for a cutting-edge AI; vision, and eventually audio and other modalities, are part of the package. Llama 4 did images; perhaps Llama 5 or later could incorporate audio or video understanding more directly. Models will become more like general AI agents that can see, speak, and possibly act (with tool use).

• Open vs Closed – The presence of Llama 4 ensures a healthy competition. OpenAI, Google, and Anthropic must stay on their feet. And vice versa, the open models have strong benchmarks to chase. For consumers, this means better models all around, and likely more cost- effective solutions (if an open model can do the job on your own hardware, then you might not need to pay for API calls).

In conclusion, Meta’s Llama 4 is an impressive step in AI model development. It provides high-quality conversational AI with the ability to handle vast amounts of text and imagery, all in an open framework. Whether you’re a developer looking to build the next big app, or an AI enthusiast curious about the tech, Llama 4 is worth paying attention to. It’s bridging the gap between research labs and the real world by putting an extremely powerful AI into the hands of the community.

The evolution from Llama 2 to Llama 4 shows how quickly AI is advancing – in a span of two years, we went from 70B-parameter models with 4K memory to 400B-parameter MoE models with vision and 10M tokens of memory. It’s almost exponential. As we look ahead, one can only imagine what the LLM landscape will look like (perhaps “infinite” context in practice, or fully multimodal understanding that can output images or video as well). For now, Llama 4 has positioned itself as one of the most capable AI models in 2025, and importantly, one that invites everyone to be a part of the journey.