Et voilà, j’ ai décidé de joindre le geste à la parole dans le cadre de la résilience digitale. J’ ai transféré le nom de domaine et l’ hébergement du blog vers l’ Union Européenne. Le blog se trouve maintenant à l’ adresse https://artificiellementintelligent.eu qui est hébergé par la société française OVH, un des leaders européens dans le domaine et offre des hébergements WordPress compétitifs. L’ancien site reste provisoirement accessible sur https://artificiellementintelligent.wordpress.com.
Ce message est donc le premier que vous recevez depuis la nouvelle mouture du blog, ce qui explique aussi la modification du format des emails de notification car j’ai dû adapter la configuration et les plug-ins de WordPress chemin faisant.
Ces préliminaires étant dits, je vais faire un rapide point sur la situation actuelle des modèles de langage. Les modèles de langage actuels peuvent grosso modo se regrouper en trois grandes catégories :
- les modèles généralistes : ces modèles sont focalisés sur la conversation et le dialogue. Ils puisent dans leurs vastes connaissances, recherchent des informations et répondent de manière interactive aux questions variées de l’ utilisateur. Souvent multimodaux, ils peuvent aussi interpréter des images, de l’ audio voire de la vidéo et sont parfois capables de générer nativement des images. Ils sont habituellement accessibles via des interfaces web ou des applications mobiles conversationnelles;
- les modèles de codage : ces modèles excellent dans l’ analyse et la génération de programmes informatiques. Ils sont typiquement exploités via des environnements de développement comme VSCode qui utilisent l’ interface de programmation pour accéder au modèle. Ces modèles sont en général capables d’ interpréter les images (diagrammes, interface utilisateur…) et se caractérisent par des fenêtres de contexte de grande taille, vu la taille souvent importante des codes source;
- les modèles raisonneurs : ces modèles sont entraînés à construire des chaînes de raisonnement logiques. Ils sont à la base des applications comme « Deep Research » qui permettent d’ analyser un sujet de manière approfondie et de rédiger un rapport détaillé. La tendance actuelle est de leur adjoindre différents outils accessibles en cours de raisonnement afin qu’ ils puissent tester leurs hypothèses et continuer à raisonner sur les résultats intermédiaires obtenus. C’ est la voie vers les fameux « agents » dont on entend beaucoup parler et qui devraient enregistrer des progrès importants cette année.
Sur base de cette classification simple, voici les modèles « phare » actuellement mis à disposition par les principaux acteurs :
| Modèle généraliste multimodal | Modèle de codage | Modèle raisonneur | |
| OpenAI | ChatGPT-4o | GPT-4.1 | o3 |
| Gemini 2.5 Pro | Gemini 2.5 Pro | Gemini 2.5 Pro Deep Think | |
| Anthropic | Claude 3.7 Sonnet | Claude 3.7 Sonnet | Claude 3.7 Sonnet Extended Thinking mode |
| Meta | Llama 4 Maverick | Code Llama 4 (pas encore disponible) | Llama 4 Behemoth (pas encore disponible) |
| Mistral | Pixtral Large | Codestral | Mistral Large |
Il est aussi intéressant de connaître les modalités supportées par les modèles généralistes :
| Modalités d’ entrée | Modalités de sortie | |
| ChatGPT-4o | texte, audio, images, vidéo | texte, audio, images |
| Gemini 2.5 Pro | texte, audio, images, vidéo | texte |
| Claude 3.7 Sonnet | texte, images | texte |
| Llama 4 Maverick | texte, images, vidéo(?) | texte |
| Pixtral Large | texte, images | texte |
OpenAI offre une palette différenciée de modèles; l’ objectif est de combiner l’ ensemble des capacités en un modèle unique à l’ architecture entièrement nouvelle (le fameux GPT-5), probablement vers la fin de l’ année 2025. Le modèle ChatGPT-4o présente la plus modalité la plus riche de tous les modèles du marché : il est capable d’ analyser, texte, image, audio et même vidéo et de générer nativement du texte et des images !
Notons qu’ OpenAI a décidé de retirer son modèle ChatGPT-4.5, trop coûteux à l’ exploitation pour se focaliser sur ChatGPT-4o pour les interactions conversationnelles et GPT-4.1 pour le codage.
Google est très bien placé avec Gemini 2.5 Pro qui intègre l’ ensemble des capacités dans un seul modèle : conversation, multimodalité (certes moins complète que ChatGPT-4o), génération et exécution de code et raisonnement. Il me semble que Google, qui a longtemps joué en seconde voire en troisième position, semble bien positionné pour reprendre la tête du peloton.
Les modèles d’ Anthropic sont très réputés pour leurs excellentes performances en codage. Anthropic cherche maintenant à renforcer sa position sur ce créneau en se concentrant sur les chaînes de raisonnement complexes avec appel d’ outils intégrés, y compris les environnements d’ exécution de programmes. Leur protocole standardisé MCP (Model Context Protocol), qui permet à un modèle de langage d’accéder à différents outils, s’ inscrit dans cette optique. Par contre, Anthropic accorde moins d’ importance à la multimodalité -moins utile pour la programmation- et leurs modèles ne peuvent générer que du texte et du code.
Enfin, Meta se caractérise par la disponibilité de leurs modèles en format open-weights, ce qui veut dire qu’ils sont utilisables localement. Ceci offre de grands avantages en termes de sécurité et de confidentialité à condition de disposer de machines suffisamment puissantes pour exécuter les modèles. Meta n’ offre pas encore de modèle raisonneur, ce dernier (appelé Behemoth) devrait cependant bientôt être annoncé.
Tous les modèles mentionnés sont de très bon niveau. Pour le travailleur intellectuel « col blanc » typique, la meilleure chose à faire est d’ essayer rapidement les différents modèles pour choisir celui qui vous convient le mieux, et ensuite de vous y tenir et de l’ utiliser chaque fois que vous vous posez une question ou recherchez des informations. C’ est comme cela que vous comprendrez progressivement comment intégrer ces modèles dans votre vie quotidienne et en tirer le meilleur parti.
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