We're excited to announce that Meta's Llama 4 AI models are rolling out on the Silatus platform. Meta claims that these models represent "the beginning of a new era of natively multimodal AI innovation," and we're committed to providing our users with access to this groundbreaking technology. However, in line with our dedication to transparency and excellence, we want to highlight some important considerations regarding Llama 4's performance and capabilities.
Introducing Meta's Llama 4 Family
The Llama 4 family includes Scout (with 109B total parameters, 17B active parameters) and Maverick (400B total parameters, 17B active parameters), both built on a Mixture-of-Experts (MoE) architecture. These models are designed to process both text and images, offering true multimodal capabilities that represent a significant advancement in open-source AI technology.
Llama 4 Scout features an impressive 10-million token context window, allowing for complex tasks such as multi-document analysis and reasoning over large codebases. Meanwhile, Maverick offers high-quality output for general assistant and chat use cases, with strong capabilities in image understanding and creative writing.
Our Findings
While we're excited to offer these models, our initial research has uncovered several points worth noting:
Benchmarking Discrepancies
There appears to be a discrepancy between benchmark results published by Meta and the actual model available to developers. TechCrunch recently reported that Meta used "an unreleased, custom version" of Maverick to boost its benchmark score on LM Arena. The version of Llama 4 Maverick that ranks highly on the LM Arena leaderboard is described by Meta as an "experimental chat version" that has been "optimized for conversationality."
This means that the performance metrics displayed on benchmarking sites may not accurately reflect the experience developers will have with the publicly available version.
Multiple Reddit users have conducted commonly-run independent experiments including the "Bouncing Ball in a Polygon" test and other standardized evaluations including Harmful Q and NER.
We believe in transparency and want our users to have realistic expectations about model performance.
Comparison to Leading Models
Our independent evaluations show that while Llama 4 represents a significant step forward for open-source models, it doesn't consistently outperform leading closed-source options in all tasks. According to TechCrunch, although Meta's internal testing shows Maverick exceeding models like OpenAI's GPT-4o and Google's Gemini 2.0 on certain benchmarks, it "doesn't quite measure up to more capable recent models like Google's Gemini 2.5 Pro, Anthropic's Claude 3.7 Sonnet, and OpenAI's GPT-4.5."
Artificial Analysis's leaderboard similarly indicates that models like "Gemini 2.5 Pro Experimental and o3-mini (high)" currently lead in overall quality. We recognize that different models excel in different areas, and Llama 4's strengths may align well with many of our users' specific needs.
Why We're Still Adding Llama 4 to Silatus
Despite these considerations, we've decided to add Llama 4 to our platform for several compelling reasons:
- Open-source innovation: As an open-source model, Llama 4 represents a significant advancement in democratizing access to powerful AI technology.
- Impressive technical features: Llama 4 Scout's 10-million token context window is among the largest available, offering unique capabilities for processing lengthy documents and complex codebases.
- Multimodal capabilities: The native integration of text and image understanding opens up new possibilities for developers building sophisticated applications.
- Future potential: Meta has demonstrated a strong commitment to rapidly improving their models, with the forthcoming Behemoth model promising even greater capabilities.
- User choice: Maybe most importantly, we believe in empowering our users to select the models that best fit their specific use cases, even if those models may not lead every benchmark category.
The Future of Llama on Silatus
We're particularly excited about Meta's upcoming Behemoth model, which is still in training but has reportedly "outperformed GPT-4.5, Claude 3.7 Sonnet, and Gemini 2.0 Pro on several STEM benchmarks" according to Meta's internal testing. We plan to evaluate and potentially add this model to our platform once it becomes available.
Our commitment to providing the best AI technology available remains unchanged. We'll continue to rigorously assess each new model release, providing transparent information about performance characteristics and optimal use cases.
