Wow, the AI space moves fast! Just when you think things might settle down, Meta drops Llama 4, and honestly, the specs look absolutely insane. It literally just came out a few hours ago as I’m writing this, and I couldn’t wait to dive in and see what all the fuss is about.
Meta has released a few different versions under the Llama 4 umbrella, primarily focusing on Llama 4 Maverick (the more powerful one) and Llama 4 Scout (a lighter, potentially locally-hostable version). We’re going to test them out today, look at the differences, and figure out how you can get your hands on them.
One of the headline features that made my jaw drop? Llama 4 apparently boasts a 10 MILLION token context window. Yes, you read that right. Ten million! That’s a massive leap and potentially opens up incredible possibilities for processing huge amounts of information at once. I can’t wait to see how that plays out in real-world applications.
Meet the Llama 4 Family: Maverick vs. Scout
So, Meta didn’t just release one model. We’ve got a couple of key players here:
- Llama 4 Maverick: This seems to be the flagship model, positioned to compete with the heavyweights like GPT-4o and Gemini 2.0 Pro. Benchmarks suggest it’s a powerhouse.
- Llama 4 Scout: This is presented as a smaller, more efficient model. It’s still powerful, outperforming previous-gen light models like Gemma 3 and Mistral 3.1, but designed to be more accessible, potentially even for local hosting (if you have the hardware!).
- Llama 4 Behemoth (Preview): There’s also mention of an even larger model, Behemoth, which is apparently outperforming models like Claude 3.7 Sonnet and GPT-4.5 in previews. Access seems limited for now, but it signals Meta’s ambitions.
The key difference, besides raw power, often lies in resource requirements and speed. Scout is designed to be nimble, while Maverick aims for top-tier performance.
Benchmarking Bragging Rights: How Does Llama 4 Stack Up?
Meta came out swinging with some impressive benchmark charts. According to their data, Llama 4 Maverick is reportedly “absolutely smashing” competitors like Gemini 2.0 Flash, Deepseek Coder V2 Lite (which is itself very new), and even OpenAI’s GPT-4o on various tests.
Here’s a simplified look at where Meta positions its new models based on initial reports:
| Model | Key Competitors (According to Meta/Benchmarks) | Reported Strengths |
|---|---|---|
| Llama 4 Maverick | GPT-4o, Gemini 2.0 Pro, Claude 3 Opus | Top-tier performance across multiple benchmarks (MMLU, coding, reasoning), potentially leading open models. |
| Llama 4 Scout | Gemma 3, Mistral 3.1, Gemini 2.0 Flash | Strong performance for its size, efficient, good balance of capability and speed. |
| Llama 4 Behemoth (Preview) | Claude 3.7 Sonnet, Gemini 2.0 Pro, GPT-4.5 | Claimed to outperform strong existing models (details pending wider release). |
It’s even made a huge splash on the independent LMSys Chatbot Arena Leaderboard. At the time of checking, Llama 4 Maverick rocketed to the #2 spot overall, just behind Google’s Gemini 2.5 Pro! That makes Meta the fourth organization to break the 1400+ Elo rating on the Arena – a significant achievement, especially for an open-access model family.
However, benchmarks are one thing; real-world performance is another. Let’s see how we can actually use it.
Getting Your Hands on Llama 4: Where to Access It
Okay, you want to try it out? Good news, there are several ways to get access, some even free!
- Official Website (llama.meta.com): You can find information and potentially request download access here (often requires agreement to terms).
- Hugging Face: The models are available on Hugging Face, a popular hub for AI models, for those looking to integrate or download them.
- Grok (grok.com / groq.com): This is where things get *fast*. Grok (often spelled Groq for the company providing the LPU inference cloud) offers access via their playground and API. Llama 4 seems incredibly optimized here.
- OpenRouter (OpenRouter.ai): This platform aggregates various AI models and provides API access. Crucially, they currently offer free access to Llama 4 Maverick and Scout APIs! This is fantastic for testing and integration into apps like Rcode or Client within VS Code.
Here’s a quick summary of access options mentioned:
| Platform | Models Available (Llama 4) | Cost/Access Notes |
|---|---|---|
| Meta Llama Website | Maverick, Scout (likely) | Requires access request/agreement. |
| Hugging Face | Maverick, Scout | Download/integration focus. Requires setup. |
| Grok Cloud Playground | Maverick, Scout | Web interface, incredibly fast inference. May have usage limits/costs. |
| OpenRouter | Maverick (Free), Scout (Free) | API access, currently free tiers available. Great for integration. |
Putting Llama 4 to the Test: My Initial Experience
Alright, enough talk, let’s try this thing out! I used OpenRouter (for the free access) and the Grok playground to run some quick tests.
Test 1: Content Creation
I asked both Maverick and Scout (via OpenRouter) to create an SEO-optimized article outline. My prompt was fairly simple: create an SEO optimized article for this keyword = content outline...
- Maverick’s Output: Honestly? It wasn’t great. The structure was basic, and the content felt a bit thin.
- Scout’s Output: Surprisingly, I thought Scout’s output was better structured and more detailed for this specific task. It generated about 700 words quickly.
Verdict: For this quick content test, Scout seemed slightly better, but neither blew me away compared to models specifically fine-tuned for writing.
Test 2: Reasoning Challenge
I used a classic reasoning prompt: “There’s a tree on the other side of the river. How can I pick an apple from it in winter?” This tests understanding context (winter = no apples) and constraints (river = barrier).
- Maverick’s Output (OpenRouter): Very direct and to the point. It correctly identified that trees are likely bare in winter and the river is a barrier. Short, concise answer.
- Scout’s Output (OpenRouter): Much wordier. It explored hypothetical solutions before concluding it’s likely impossible due to the season and the river. Seemed to “think through” it more explicitly.
Verdict: Both models understood the core constraints. Maverick was more direct, Scout more descriptive. Both passed the basic reasoning check.
Test 3: The Speed Demon – Llama 4 on Grok
This is where things got genuinely exciting. I tried running prompts on Llama 4 Scout via OpenRouter versus Llama 4 Scout directly in the Grok Cloud playground.
I asked both to write a 2,000-word article about SEO. I started the OpenRouter request first.
The result was astonishing. Grok finished generating the entire article before OpenRouter had even gotten a few paragraphs out. The speed was, as the video put it, “rediculous.” I’ve never seen an AI respond that quickly. If raw speed is your priority, running Llama 4 on Grok’s specialized hardware seems to be the way to go.
Test 4: Coding Challenge
I tried a couple of coding tasks:
- Self-Playing Snake Game (via Roocode in VS Code using OpenRouter Scout Free API): After ensuring the correct free API was selected, it did generate code. It planned out the steps (HTML, CSS, JS) and produced the files. However, I didn’t get to fully test the final game quality in this quick look. Initial setup needed careful API selection in the tool.
- Endless Runner Game (via Grok Scout): I used a more complex prompt for a p5.js endless runner game. It generated the JavaScript code *very* quickly. Unfortunately, when I plugged the code into a p5.js editor… it didn’t work. It failed to run.
- Side-by-Side Comparison (LMSys Arena): I used the Arena’s side-by-side tool to compare Llama 4 Maverick against GPT-4o with the same reasoning prompt from earlier. GPT-4o provided a much more helpful and nuanced answer, exploring possibilities before stating the likely issues. Maverick’s response (similar to my OpenRouter test) was blunter and less detailed. For a simple HTML/JS code generation task tested in the arena, GPT-4o’s output worked and looked decent, while Maverick’s code output didn’t function correctly in my test environment (Liveweave).
Verdict: Coding performance seems mixed. While it can generate code structure and plan tasks, the actual functional output, especially for more complex requests or compared to GPT-4o, seems less reliable in these initial tests. The non-working p5.js game and the comparison against GPT-4o were disappointing.
Integrating Llama 4: Using It With Your Tools
For developers, integrating Llama 4 into tools like Visual Studio Code is a key use case. Thanks to OpenRouter’s free tier, this is quite easy:
- Download VS Code (if you haven’t already).
- Install extensions like Client or Rcode (my personal preference leans towards Rcode).
- In the extension settings, select OpenRouter as the provider.
- Choose either Llama 4 Maverick (Free) or Llama 4 Scout (Free) from the model list. Make sure you select the ‘Free’ version to avoid charges!
- You can then use the AI coding assistant features powered by Llama 4.
Note: Direct Grok integration wasn’t immediately visible in the dropdowns for these specific extensions during my check, so OpenRouter seems the easiest path for VS Code integration right now.
The Verdict (So Far): Promising Power, Patchy Performance?
So, what’s my takeaway after this initial whirlwind tour of Llama 4?
The Strengths:
- Incredible Specs: That 10 million token context window is a potential game-changer.
- Strong Benchmarks: It’s undeniably powerful on paper and performing exceptionally well on leaderboards like LMSys Arena.
- Blazing Speed (on Grok): The inference speed on Grok’s platform is truly remarkable.
- Accessibility: Availability through multiple platforms, including free tiers via OpenRouter, is fantastic for experimentation.
- Open(ish) Nature: While managed by Meta, the Llama family has generally been more accessible for research and self-hosting than competitors like GPT-4.
The Weaknesses (Based on My Quick Tests):
- Inconsistent Practical Performance: While benchmarks are great, my initial tests showed variability. Content creation was okay, reasoning was decent but less nuanced than GPT-4o, and coding results were hit-or-miss (sometimes non-functional).
- “Free” Might Have Limits: The free access via OpenRouter is great, but heavy use might hit rate limits or potentially become paid later. Speed on free tiers won’t match dedicated platforms like Grok.
- Is it *Really* Better than GPT-4o/Gemini 2.5 Pro Right Now?: Based purely on these limited tests, particularly for creative and coding tasks, I’m not yet convinced it consistently outperforms the top closed-source models in practical application quality, despite the benchmark scores.
It’s still very early days for Llama 4. Models often improve rapidly after release with fine-tuning and community feedback. The potential is definitely there, especially with that massive context window and the impressive speed optimizations possible on platforms like Grok.
I’m definitely going to keep experimenting with it, especially as tools and integrations mature. But for now, while impressed by the numbers and speed, I’m reserving judgment on whether it dethrones the current champs for my day-to-day tasks.
What do you think about Meta’s Llama 4? Let me know your thoughts and experiences in the comments below!
