Meta’s release of the Llama 4 family of AI models has been met with widespread disappointment across the AI community. Despite the hype and promises, these models have demonstrated substantial performance issues in real-world applications. I’ve spent a significant amount of time testing these models against alternatives, and frankly, the results aren’t good for Meta.
Performance Problems That Can’t Be Overlooked
The most significant issue with Llama 4 models is their coding capabilities—or rather, the lack thereof. Both Llama 4 Scout and Maverick struggle with even basic programming tasks that smaller models handle easily. In my testing, Gemini 2.0 Flash consistently outperformed Llama 4 on practical coding challenges, despite Meta’s claims. This is particularly relevant given my focus on practical coding skills using AI, as explored in my post on AI-assisted coding tools.
It’s striking that previous models like Llama 3.3 70B performed better at coding than these new offerings. This is a step backward rather than the advancement we expect from a major release. As I often say, focusing on flashy marketing misses the point when the fundamentals are lacking.
Personal vibe scores based on practical coding tasks including error debugging, function implementation, and code refactoring.
Size Doesn’t Always Matter: Smaller Models Excel
One of the most striking revelations is how models with significantly fewer parameters outperform Llama 4 across various tasks. DeepSeek, Phi 4 14B, and Gemma 3 27B all demonstrate abilities despite using dramatically fewer resources.
This raises questions about Meta’s training process and architectural decisions. It goes beyond just parameter count—efficiency and training quality are major factors. The fact that Google’s Gemma 3 27B outperforms Llama 4 models in most practical applications indicates fundamental issues with Meta’s overall strategy. Given my experience, I know that resources are never enough to compensate for poor planning. This is similar for AI in Business as referenced in my Q&A, it is better to use off-the-shelf models, so you should not ‘try and make your own model anyway’. A different strategy for coding model architectures should be a paramount goal Meta should be aiming for.
Context Window: A Feature Worth Ignoring
If there’s one area where Llama 4 stands out, it’s the context window size. Llama 4 Scout offers an impressive 10 million token context window, which competitors currently don’t match. However, this advantage comes with caveats:
- Technical issues with hosting platforms like Open Router and Root Code make it challenging to fully utilize this feature.
- The massive context window doesn’t make up for poor performance in other areas.
- Competitors with 1-2 million token context windows are sufficient for most applications.
If you don’t process documents with millions of tokens in a prompt, it is unlikely this supposed advantage outweighs other problems with Llama 4. The idea is nice, but there has not been enough refining to call it a useful feature.
Benchmarking Controversy: Questionable Practices
Meta’s benchmarking approach has been viewed with suspicion. They used an extra-chatty model for LMArena benchmarks—different from what users would access. While Meta was candid, this seems misleading and harms trust in their benchmarks.
Such a move creates unrealistic expectations for what Llama 4 models can achieve in real-world scenarios. When users try to replicate benchmark results with the available models, they notice severe performance gaps, as I have myself.
Competitive Analysis: Better Choices Out There
The AI model marketplace contains more effective alternatives to Llama 4:
Gemini 2.0 Flash
Google’s Gemini 2.0 Flash beats Llama 4 in real-world scenarios while delivering greater speed and cost effectiveness. Its reasoning skills are better, and it comes up with more accurate outputs for typical tasks. As Google is demonstrating, focusing on models that actually perform better in tests is ideal rather than an extremely huge context window that is more of a gimmick.
DeepSeek
DeepSeek has become a key open-source alternative that succeeds in specific fields. Its focused training leads to consistently better results than Llama 4 across a variety of tasks. For this and much more it is a must-use in your AI stack if you value coding, as specified in AI capabilities and development in the Q&A regarding multimodal reasoning, if the processes don’t require it, then it might not be needed, which is the case of Llama 4s massive context window, it doesn’t improve it in a significant enough way to prioritize it over DeepSeek which is going to generate a better output for the vast majority of contexts.
Smaller Purpose-Built Models
Phi 4 14B from Microsoft and Gemma 3 27B from Google, show how smaller, trained models can best larger ones. They have better outcomes with fewer resources, making them useful for many uses. Often in this industry, benchmarks are not a reflection of usefulness in the ‘real-world”, this is also reflected in the Q&A section regarding benchmarks vs real-world applications where models like Claude are far superior despite not performing as well on generic benchmarks.
For developers wanting production-ready models, alternatives provide better functionality, reliability, and often clearer licensing than Llama 4. For users looking a model with high jailbreak performance, Gemma 3 27B outperforms R1 in Jailbreak Classification.
The Coding Mistake
Llama 4’s inability to code well is a critical error. It could not do these things during my work:
- Find and fix basic syntax errors
- Understand how programs work and their logic
- Make practical code examples
- Explain technical concepts with accurate code snippets
Llama 4 fails developers wanting AI for programming help. Even Maverick, seen as the better model, performs worse than Llama 3.3 70B in coding.
If you want AI coding help, use alternatives like Claude or other coding models that reliably give great results.
Future: Can Meta Bounce Back?
Meta mentioned testing a “Behemoth” model. This bigger model might resolve problems with Llama 4, but given past performance, optimism isn’t high.
Llama 4’s problems seem deeper than scale—they indicate training, architecture, and alignment issues. A bigger model might not fix these.
For Meta to earn trust in the AI field, they should change their strategy by:
- Boosting coding and reasoning skills
- Delivering benchmarks that are more open and realistic
- Making models with practical use, rather than impressive stats
Licensing Issues
Llama 4’s licensing complicates things. Meta’s licensing for Llama 4 raises worries for developers and organizations, unlike other models that use MIT. As I mentioned earlier I am for open source models for privacy, and to drive costs down, but if the licensing is such a mess then that doesn’t achieve this goal.
Licensing difficulties make users jump ship when better-performing alternatives exist with more permissive licenses.
Strategic Consequences for AI Development
Llama 4’s problems affect overall AI development strategies. This suggests that:
- Bigger isn’t always better—training and architecture are more critical than raw parameters.
- Specialized models often perform better than general ones for targeted tasks.
- Open-source AI needs to focus on use.
The community should prioritize practical performance over metrics that sound great in marketing.
Conclusion: Lost Potential
Llama 4 is a missed chance for Meta to boost the open-source AI model space. The model’s coding performance is poor, making it hard to suggest it for most uses, despite having a large context window.
Users should consider Gemini 2.0 Flash, DeepSeek, Phi 4 14B, or Gemma 3 27B instead because they give awesome real-world performance. Hopes were high for Llama 4, but the product fell short.
Meta needs a new AI model strategy to stay competitive because scaling up model size won’t be enough if coding performance, training, and alignment are not addressed.
Until issues are fixed, developers should choose models that can provide that reliable performance as Llama 4 does not.
