SEO: why new AI models struggle to outperform their predecessors

In 2025, artificial intelligence (AI) continues to disrupt digital practices, notably in the field of SEO. While one might expect each new generation of AI models to push the boundaries of natural referencing, a paradoxical reality is emerging: the latest models struggle to outperform their predecessors on classic SEO tasks. This trend, confirmed by recent comparative studies, calls for a thorough reflection on technological evolution, development priorities of algorithms, and their implications for digital marketing.

The cross-analysis of performance among models such as Claude, Gemini, or ChatGPT-5.1 reveals that the new versions, although ambitious in their reasoning and adaptation capabilities, show a decline in performance on specific SEO tasks. Understanding this phenomenon is essential for all SEO professionals and content strategists who wish to master current challenges and anticipate future developments in search engine optimization.

This context, both technological and strategic, exposes the limitations of approaches based on machine learning and highlights internal contradictions in models that seek to complexify their reasoning processes, sometimes at the expense of SEO precision and relevance. In this article, we dissect these dynamics to better understand why new AI models do not yet dominate older ones in terms of SEO.

SEO Performance: The Disappointing Results of Recent Artificial Intelligence Models

A benchmark conducted by Previsible on AI generators such as Claude Opus 4.5, Gemini 3 Pro, and ChatGPT-5.1 Thinking confirms a significant drop in performance for traditional SEO tasks. For example, Claude Opus 4.5 now achieves only 76% success, compared to 84% for its previous version. Gemini 3 Pro lost nine points, dropping to 73%. ChatGPT-5.1 Thinking follows a similar trajectory with 77%, down from GPT-5 standard. These declines go far beyond mere statistical variation and reflect a real and worrying trend for the SEO performance of artificial intelligences.

These results particularly affect direct, technical, or strategic tasks. For instance, HTML tag analysis, fine understanding of user intent, or even conducting simple SEO audits become less reliable. Yet, these tasks are at the heart of content and structure optimization processes on the web. SEO, which relies on a clear, rigorous, and statistical logic, thus appears disrupted by these new AI model approaches.

A major implication of this decline is that natural referencing specialists, accustomed to relying on AI capabilities to speed up audits and refine strategies, must now double their vigilance. This trend questions the idea that the latest version of an AI model is always the best for all uses, particularly the most technical ones.

Concrete Impact on SEO Strategies

On the ground, this performance degradation translates into errors in interpreting search intentions, less precise recommendation proposals, and increased difficulty in detecting optimal levers to improve ranking. For example, an automated audit of backlinks or semantics that relied on the older version of Claude more systematically detected improvement angles, whereas the new version generates more hesitations and imprecise proposals.

Imagine an SEO team that must optimize an international e-commerce site. Using the latest API of a model trained on complexity directly risks incurring significant extra costs without guarantee of qualitative improvement. This paradox worries professionals, forced to test several versions to maintain optimal quality. The link between performance and investment becomes fragile.

Why Do New AI Models Complexify the Process at the Expense of SEO Precision?

The increasing complexity of artificial intelligence models is at the heart of this paradoxical phenomenon. Developers have refocused their optimization priorities, seeking to improve AI’s ability to reason more deeply, even on basic questions. The goal is clear: to make models autonomous agents capable of anticipating rich and complex information flows.

However, this orientation pushes models to add layers of reasoning that are not always suited to the specific expectations of natural referencing. The result is a less clear response, often diluted by assumptions, multiple interpretations, or even superfluous information. This phenomenon, known as “Type 2 reasoning,” now predominates, to the detriment of “fast” and targeted reasoning, which is better adapted to classic SEO requests.

This change has direct consequences:

• Loss of clarity in responses: the model no longer delivers a concise answer, but develops hypotheses that complexify the SEO audit or recommendation.
• Increase in erroneous interpretations: by analyzing too deeply, AI can invent constraints or nonexistent problems.
• Reduction in reliability of technical audits: painful for SEO experts who rely on diagnostic stability to deploy quick fixes.

This trend illustrates a new frontier of machine learning: the balance between cognitive complexity and operational efficiency. Artificial intelligence, while gaining independence and analytical flexibility, sometimes loses discipline and precision when the task demands simplicity and accuracy.

The Effects of Security Measures and Restrictions in Recent Algorithms on SEO

Beyond complexity, recent AI models have integrated enhanced security mechanisms to avoid actions perceived as risky or ambiguous. For example, some automatic technical audits are now refused or only partially processed out of caution. This excessive moderation leads to a form of algorithmic self-censorship that limits analytical capabilities, which is highly penalizing for professionals.

Technical SEO, which relies precisely on the detailed exploration of sensitive data such as site architecture, tags, and indexing errors, is the first victim of this phenomenon. A model that blocks a security or optimization audit request due to a mistaken interpretation of danger reveals a misalignment between business objectives and AI programming.

Consequently, response quality is affected, and the recommendations offered are sometimes generic or insufficient, exacerbating the loss of trust among professional users in tools based on these new models.

The Economic Consequences of the Performance Decline of AI Models in SEO

The paradox of recent AI models increasing usage costs while decreasing result quality not only concerns technical aspects. It also impacts budgets allocated to referencing, strategic planning, and the sustainability of digital projects.

For companies like Imagix Digital, specialized in digital marketing, the choice of the AI model to integrate into their SEO tools has become a critical point. Using the latest version without adaptation leads to higher monthly costs, due to the volume of requests required, without guarantee of better optimization. This pushes towards considering older, stable versions or specialized models, less “intelligent” but more effective in SEO.

More broadly, it is the SEO value chain in the age of artificial intelligence that is being questioned. The rise of AI generates strong expectations, but also disappointments on the performance front. These divergences require reflection on resource management, human expertise, and complementarity between algorithms and human operators.

When Technical Sobriety Outperforms Algorithmic Sophistication in SEO

An unexpected lesson drawn from recent analyses is that older, specialized versions of models often remain superior for certain SEO tasks. Indeed, in binary checks or technical audits, simplicity and clarity prevail over sophistication. More sober models, less prone to interpretation, offer more reliable answers.

This reflects a true quest for balance between:

• Depth of analysis: essential for solving complex cases.
• Clarity and speed of execution: indispensable for daily technical SEO.

SEO specialists must now incorporate this duality into their approach, selecting AI models suited to each campaign stage and specific tasks. Using rigid frameworks and customized prompts, in segregated usage environments, is a recommended strategy to avoid result degradation.

New Interface Standards to Make Best Use of AI Models in SEO

The evolution of AI model capabilities also imposes changes in tools and interfaces. Using a recent model in a generic chat window is no longer enough. To maintain coherence and reproducibility of results, it is necessary to integrate from the start:

• Historical and contextual data adapted to the project.
• Strict methodological rules.
• Brand constraints specific to each client.

These elements curb excessive improvisation and guide the model toward precise and actionable recommendations. Among several players, “custom GPTs” or “Claude Gems,” dedicated instances applying a rigorous framework for each SEO use case, are emerging.

This modular approach is the key to reconciling the richness of modern algorithms with the specific needs of natural referencing. It ensures better optimization and prevents inconsistency in the performance of executed tasks.

The Importance of Rethinking SEO Strategies in Light of New AI Model Limitations

The decline in performance of the latest AI generations in SEO calls for a redesign of working methods. It is no longer just about leveraging model capabilities but understanding their limits and adapting strategies accordingly. Mastering prompts becomes a crucial skill, as does integrating complementary human expertise.

Moreover, the diversity of artificial intelligence models offers a range of tools that can be selected according to needs, from older models, reliable for certain technical tasks, to the newest, more performant on complex and qualitative analyses. This coexistence requires defining a coherent multi-tool strategy to optimize SEO efforts.

Finally, this situation highlights the urgency of thinking about SEO in the artificial intelligence era not as a simple automation but as a hybrid discipline combining advanced algorithms, historical data, and human expertise. SEO in 2025 thus requires increased vigilance, rigorous methodology, and permanent adaptability.

Technological Evolution Perspectives: Toward Hybrid and Specialized Models in SEO

Faced with observed limits, evolution prospects for SEO-oriented AI models include the development of hybrid systems combining sobriety and enhanced cognitive intelligence, as well as increased model specialization. Rather than trying to do everything, future algorithms could rely on dedicated modules, combining simple and fast processing with complex analyses on demand.

Future technological milestones could also integrate better handling of contextual and historical data, offering dynamic optimization adapted to market shifts and search engine requirements. This deep customization would prevent the current drift harming SEO performance.

Finally, artificial intelligence in SEO will go hand in hand with reinforced dialogue between humans and machines. The role of experts will be more crucial than ever to calibrate, control, and adapt models to practical realities, fostering an effective and productive symbiosis.

AI Model	Previous Version (%)	Recent Version (%)	Performance Loss (%)
Claude Opus	84	76	8
Gemini Pro	82	73	9
ChatGPT-5.1 Thinking	82	77	5