This topic is best reviewed by LLM Evaluation Researchers, Machine Learning Engineers specializing in Reasoning Architectures, and Open-Source AI Strategy Analysts.
As a Senior AI Research Scientist, I have synthesized the performance data and architectural observations from the provided material:
Abstract
This technical evaluation examines the reasoning capabilities of Google’s Gemma 4 model family, released April 2, 2026, under an Apache 2.0 license. The analysis focuses on two primary variants: the 26B Mixture of Experts (MoE) model (utilizing 3.8B active parameters) and the 31B Dense model. Through a standardized "Elevator Puzzle"—a zero-shot logic test requiring complex causal reasoning and constraint satisfaction—the 4B-active MoE model demonstrates significant emergent reasoning capabilities, characterized by high self-reflectivity and iterative error correction.
The evaluation reveals a performance paradox: the smaller MoE variant (Gemma 4 4B) consistently outperforms the larger 31B Dense model in mathematical precision and boundary adherence. While the 31B Dense model appears better suited as a foundational base for domain-specific fine-tuning, the 4B MoE variant achieves a near-state-of-the-art (SOTA) result of 9 button presses, surpassing the "naked" proprietary GPT-5.4 and approaching the performance of Gemini 3.1 Pro.
Gemma 4 Performance Analysis: Causal Reasoning and Model Benchmarking
0:00 Gemma 4 Model Release: Google released the Gemma 4 family on April 2, 2026, featuring an Apache 2.0 license. The suite includes 2B, 4B, 26B MoE, and 31B Dense models, marketed for complex logic and causal reasoning.
0:46 26B MoE Architecture: The 26B Mixture of Experts model activates only 3.88 billion parameters during inference, making it a highly parameter-efficient "tiny" model compared to its dense counterparts.
1:34 Strategic Reasoning Traces: During live testing, the 4B MoE model displays a transparent strategic planning process ("Strategy 1," "Strategy 2"), whereas the 31B Dense model provides less immediate insight into its internal planning.
2:38 The Elevator Logic Puzzle: The models are tested on a "shortest path" logic puzzle involving 50 floors, mathematical functions tied to button presses, and strict energy/token constraints.
4:50 Emergent Self-Reflection: The 4B MoE model exhibits "extreme" self-reflection, frequently pausing to verify calculations (e.g., checking if 29 is a prime number) and recalculating sequences upon detecting potential errors.
6:21 4B vs. GPT-5.4: The 4B active model successfully identifies a valid 10-step solution, a task the evaluator notes the base ("naked") GPT-5.4 failed to complete.
8:23 31B Dense Model Limitations: The 31B Dense model struggles with the puzzle, becoming trapped in local minima and failing to optimize energy management. The evaluator concludes this variant is intended strictly as a base for supervised fine-tuning (SFT) or reinforcement learning (RLHF).
12:14 Iterative Validation: During a verification run, the 4B model identifies a critical error in its initial 10-step sequence regarding a button constraint, subsequently self-correcting to a valid 11-step solution.
19:11 Boundary Condition Violation: The 31B model attempts to optimize by "overshooting" the 50-floor limit (calculating for 57 floors), indicating a failure to adhere to hard logical constraints.
21:09 Full Precision Performance: The tests are conducted using full-precision weights rather than quantized versions (GGUF), which may impact the perceived reasoning "momentum" of the models.
25:44 Optimal Reasoning Efficiency: In a final optimization push, the 4B MoE model achieves a 9-press solution plus an emergency exit, totaling 10 actions.
31:54 Conclusion on Open-Source SOTA: The Gemma 4 4B MoE is characterized as an outstanding open-source model for pure thinking/causal reasoning, representing a significant advancement in parameter-efficient logic.