French Startup Mistral Unveils AI Model With Enhanced Cognitive Reasoning Abilities

On 10 June , French AI startup Mistral unveiled Magistral, a new “reasoning” model designed to tackle complex problems through structured, step-by-step logic.

Available immediately on Mistral’s own platforms and Hugging Face, Magistral represents a significant evolution in large language models, targeting use cases that demand extended thought processing and higher accuracy than its predecessors.

In a blog post, Mistral described the model as “designed to think things through—in ways familiar to us,” highlighting its ability to generate transparent, traceable reasoning chains in natural language.

This so-called “chain of thought” approach is particularly relevant in sectors like law, finance, healthcare, and government, where compliance and accountability require that every conclusion be logically and verifiably sourced.

By offering such interpretability, Mistral directly addresses one of AI’s central challenges: understanding how these systems arrive at their outputs.

Because AI models are trained on vast datasets rather than explicitly programmed, their internal logic often remains opaque—even to their creators.

With Magistral, Mistral aims to bridge that gap, setting a new standard for clarity and reliability in AI-driven reasoning.

Is AI Capable of Reasoning?

Mistral also highlighted Magistral’s enhanced capabilities in software coding and creative writing, positioning it as a strong contender among a growing class of “reasoning” models.

These include OpenAI’s o3, select versions of Google’s Gemini, Anthropic’s Claude, and the rising Chinese entrant, DeepSeek’s R1.

Yet, the concept of AI “reasoning” remains a subject of debate.

This week, Apple—still striving to catch up with top AI developers—challenged the narrative with a research paper titled "The Illusion of Thinking."

In it, Apple researchers argue that current language models face fundamental limitations, asserting they “fail to develop generalisable reasoning capabilities beyond certain complexity thresholds.”

The critique underscores an ongoing tension in the AI community: while developers promote models as increasingly capable of rational thought, the question of whether these systems truly “reason” remains unresolved.