Gemma vs. Falcon: A Tale of Two Titans in the Open Source AI Arena

The landscape of open-source large language models (LLMs) is a dynamic and fiercely competitive one. In this arena, Google’s Gemma and the Technology Innovation Institute’s (TII) Falcon have emerged as two of the most prominent and powerful model families.¹ While both offer impressive capabilities, the question of which is “a lot better” is nuanced, depending heavily on the specific model versions being compared and the intended application.

Recent developments, including the release of Google’s Gemma 2 and TII’s Falcon 3, have pushed the boundaries of performance for open-source models. Generally, the newer and larger models within each family tend to outperform their predecessors and smaller counterparts.

Head-to-Head on the Benchmark Battlefield

Direct comparisons on standardized benchmarks reveal a tight race. The Falcon 3 series, particularly the 7B and 10B models, have demonstrated state-of-the-art performance in their respective size classes on the Hugging Face Open LLM Leaderboard. They have shown strong results across a range of tasks measuring reasoning, knowledge, and coding abilities, including benchmarks like MMLU (Massive Multitask Language Understanding), Big-Bench Hard (BBH), and MATH.²

For instance, the Falcon 3 10B model has been positioned as a top performer among models under the 13B parameter scale.³

On the other hand, Google’s Gemma 2 models have also showcased significant improvements over the initial Gemma release.⁴ The gemma-2-9b-it (instruction-tuned) model, for example, has been noted to be highly competitive, even outperforming some larger models on specific reasoning and instruction-following tasks.

It’s important to note that direct, side-by-side comparisons of the very latest models (Gemma 2 and Falcon 3) across a comprehensive and consistent set of benchmarks are still emerging. However, the available data suggests a neck-and-neck competition, with each model family having its own set of strengths. For example, Falcon 2 11B has been reported to be on par with Gemma 7B.

Architectural and Training Distinctions

The differences in performance can be partly attributed to their distinct architectures and training data.

Gemma, developed by Google, leverages the research and technology behind the powerful, closed-source Gemini models.⁵ It is trained on a massive dataset of up to 6 trillion tokens of text and code. Gemma models are known for their strong generalist capabilities and are designed to be both powerful and safe.⁶

Falcon, developed by TII in the UAE, utilizes a custom data pipeline and is trained on the high-quality “RefinedWeb” dataset.⁷ The latest Falcon 3 models have also benefited from techniques like knowledge distillation from larger, more capable models. Falcon has been praised for its performance, particularly in multilingual capabilities and its open and permissive Apache 2.0 license, which is a significant advantage for commercial applications.

Key Differentiators at a Glance

Feature	Gemma	Falcon
Developer	Google	Technology Innovation Institute (TII)
Key Strengths	Strong generalist capabilities, backed by Gemini research, focus on safety.	State-of-the-art performance in its size class, strong multilingual capabilities, permissive Apache 2.0 license.
Training Data	Up to 6T tokens of text and code.	“RefinedWeb” dataset with a custom data pipeline.
Architecture	Based on Google’s Gemini architecture.	Custom architecture with optimizations for performance.
Licensing	Custom license.	Apache 2.0.

The Verdict: It’s a Matter of “Fit”

To definitively say one model is “a lot better” than the other is an oversimplification. The “better” model is the one that best fits the specific needs of a project.

• For cutting-edge performance in specific size categories, the latest Falcon 3 models currently hold a strong position on leaderboards.
• For those seeking a model with the backing of Google’s extensive research and a strong focus on safety and responsible AI, Gemma 2 is a compelling choice.
• For commercial applications where a permissive license is crucial, Falcon’s Apache 2.0 license is a significant advantage over Gemma’s custom license.

The rapid pace of development in the open-source LLM space means that the leadership position can be transient. As both Google and TII continue to innovate, the capabilities of both Gemma and Falcon are likely to see further advancements, making the choice between them an ongoing evaluation of the latest releases and their performance on relevant benchmarks.