{"id":1262,"date":"2026-06-23T14:45:03","date_gmt":"2026-06-23T14:45:03","guid":{"rendered":"https:\/\/convly.ai\/?p=1262"},"modified":"2026-06-23T14:45:03","modified_gmt":"2026-06-23T14:45:03","slug":"deepseek-r1-vs-deepseek-v4-pro","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/deepseek-r1-vs-deepseek-v4-pro\/","title":{"rendered":"DeepSeek R1 contre DeepSeek V4-Pro : Sp\u00e9cifications, tarifs et choix \u00e0 faire (2026)"},"content":{"rendered":"

DeepSeek R1<\/strong> contre DeepSeek V4-Pro<\/strong> \u2014 DeepSeek’s dedicated reasoning model versus its flagship. Below is the full side-by-side: specifications, API pricing, context window, local hardware requirements, and a clear, data-driven recommendation on which to pick.<\/p>\n

\n \n \n
Sp\u00e9cifications<\/th>DeepSeek R1<\/a><\/th>DeepSeek V4-Pro<\/a><\/th><\/tr><\/thead>\n
D\u00e9veloppeur<\/td>DeepSeek<\/td>DeepSeek<\/td><\/tr>\n
Type<\/td>LLM (MoE, raisonnement)<\/td>LLM (architecture MoE)<\/td><\/tr>\n
Param\u00e8tres<\/td>671 milliards au total \/ 37 milliards actifs (MoE)<\/td>1,6 T au total \/ ~49 milliards actifs (MoE)<\/td><\/tr>\n
Fen\u00eatre de contexte<\/td>128 K<\/td>1 million<\/td><\/tr>\n
Modalit\u00e9<\/td>Texte \u2192 Texte<\/td>Texte \u2192 Texte<\/td><\/tr>\n
Licence<\/td>MIT (ouverte)<\/td>MIT (ouverte)<\/td><\/tr>\n
Poids ouverts<\/td>\u2705 Oui<\/td>\u2705 Oui<\/td><\/tr>\n
Prix d'entr\u00e9e ($\/1 million)<\/td>$0.50<\/td>$0.435<\/td><\/tr>\n
Prix de sortie ($\/1 million)<\/td>$2.15<\/td>$0.87<\/td><\/tr>\n
VRAM (4 bits)<\/td>~400 Go<\/td>~800 Go<\/td><\/tr>\n
GPU minimal requis (en local)<\/td>Serveur multi-GPU<\/td>Serveur multi-GPU (ex. : 8 \u00d7 H100 80 Go)<\/td><\/tr>\n
Date de sortie<\/td>2025<\/td>2026-04<\/td><\/tr>\n <\/tbody>\n <\/table>\n\n
\n

Principales diff\u00e9rences<\/h3>\n