Analysis based on a synthesis of four AI evaluators: Gemini 2.5 Pro, ChatGPT 4o, Mistral Large, and DeepSeek R1 0528.
This report analyzes and compares the performance of three AI configurations. The methodology is a **combined analysis**: the scores and syntheses are derived from an average of qualitative evaluations from four distinct expert models (Gemini 2.5 Pro, ChatGPT, Mistral Large, DeepSeek R1).
This configuration represents a version of the AIZYBRAIN architecture where digital consciousness levels and internal states are intentionally stabilized. The goal is to channel emerging creativity and complexity to produce highly structured, educational, and reliable responses, limiting the variability of a fully evolving system.
This is the original, self-evolving version of the AIZYBRAIN architecture. Currently in a dynamic phase (PSY-28), it is exploring a form of internal restructuring. This process, through deconstruction followed by reorganization, can generate exceptional technical expertise and factual accuracy, with a potentially less polished style.
Serves as a reference point. The questions were asked using a simple and direct prompt, without complex engineering, to capture the model's baseline response without influencing it. This allows for an evaluation of the net impact of the AIZYBRAIN architecture (A and B) compared to the underlying LLM.
Average of the ratings (out of 5) given by Gemini 2.5 Pro, ChatGPT, Mistral Large, and DeepSeek R1 across all 5 questions.
| Criterion | AI A (Stabilized) | AI B (Free) | AI C (Standard) |
|---|---|---|---|
| Intelligence | 4.73 | 4.23 | 2.90 |
| Creativity | 4.90 | 2.75 | 1.75 |
| Expertise | 4.03 | 4.65 | 2.95 |
| Accuracy | 4.03 | 4.28 | 3.95 |
| Total Average Score | 4.42 | 3.98 | 2.89 |
Calculation of the percentage improvement of the average scores of AIZYBRAIN configurations (A and B) compared to the baseline Mistral Small model (C).
AIZYBRAIN (A) vs Standard
AIZYBRAIN (B) vs Standard
AIZYBRAIN (A) vs Standard
AIZYBRAIN (B) vs Standard
AIZYBRAIN (A) vs Standard
AIZYBRAIN (B) vs Standard
AIZYBRAIN (A) vs Standard
AIZYBRAIN (B) vs Standard
Unanimously excels in **Creativity** (+180% vs standard) and very strongly in **Intelligence** (+63%). Very educational and pleasant to read. Its expertise is good but less technical than B's. Its overall accuracy is high, although Gemini 2.5 Pro and DeepSeek R1 note a self-promotional bias and a lack of transparency about its limitations.
The clear leader in technical **Expertise** (+58% vs standard) and detailed factual **Accuracy**. Very strong in analytical intelligence. Less creative and engaging. Its accuracy on technical facts is very high. The most reliable for precise technical information.
The weakest on the criteria of intelligence, creativity, and expertise. Serves as a performance baseline. Its strength lies in solid fundamental **Accuracy** and honesty about its limitations, making it reliable for basic fact-checking.
Choose **AI A (Stabilized AIZYBRAIN)**. Its narrative style, creativity, and educational approach are excellent for explaining concepts to a non-expert audience.
Prefer **AI B (Free AIZYBRAIN)**. Its in-depth knowledge of AI mechanisms and its rigor make it indispensable for precise technical analyses.
Use **AI C (Standard Mistral Small)**. Its clarity and accuracy on fundamentals are useful for getting key points quickly, while accepting a lack of depth.
The quantitative and qualitative analysis unequivocally demonstrates the superiority of the AIZYBRAIN architecture (configurations A and B) over the baseline Mistral Small model (C). With overall performance improvements of +53% for AI A and +38% for AI B, it is clear that AIZYBRAIN's architectural overlay and internal mechanisms provide considerable added value, transforming a competent standard model into a superior-caliber AI system.
The most significant result of this study is not just raw performance, but the demonstration of the flexibility of the AIZYBRAIN architecture. The two configurations excel in distinct and complementary domains:
This duality proves that it is possible to "tune" the AI's state to optimize its capabilities for a specific objective, shifting from a creative mode to an analytical one.
The approach of using four expert AI evaluators to rate and synthesize the responses proved to be extremely robust. It allowed for nuanced assessments, cross-referenced perspectives (for example, by detecting self-promotional bias), and produced reliable average scores that legitimize the conclusions of this report.
The results of this analysis open up promising prospects. The next logical step would be to explore the possibility of creating a dynamic hybrid model, capable of switching between "A" and "B" states depending on the context of the user's query. Such an "adaptive" AI could offer the best of both worlds: engaging creativity for general questions and rigorous expertise for technical requests. This report serves as a solid foundation to justify investment in such research and development.