{"id":1268,"date":"2026-06-23T14:45:04","date_gmt":"2026-06-23T14:45:04","guid":{"rendered":"https:\/\/convly.ai\/?p=1268"},"modified":"2026-06-23T14:45:04","modified_gmt":"2026-06-23T14:45:04","slug":"llama-3-3-70b-vs-qwen3-32b","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/llama-3-3-70b-vs-qwen3-32b\/","title":{"rendered":"Llama 3.3 70B contre Qwen3 32B : sp\u00e9cifications, tarifs et choix (2026)"},"content":{"rendered":"

Llama 3.3 70B<\/strong> contre Qwen3 32B<\/strong> \u2014 70B versus 32B for local power users. 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>Llama 3.3 70B<\/a><\/th>Qwen3 32B<\/a><\/th><\/tr><\/thead>\n
D\u00e9veloppeur<\/td>Meta<\/td>Alibaba<\/td><\/tr>\n
Type<\/td>LLM (dense)<\/td>LLM (dense)<\/td><\/tr>\n
Param\u00e8tres<\/td>70 milliards<\/td>32 milliards<\/td><\/tr>\n
Fen\u00eatre de contexte<\/td>128 K<\/td>128 K<\/td><\/tr>\n
Modalit\u00e9<\/td>Texte \u2192 Texte<\/td>Texte \u2192 Texte<\/td><\/tr>\n
Licence<\/td>Llama 3.3 Community (ouverte)<\/td>Apache 2.0 (ouverte)<\/td><\/tr>\n
Poids ouverts<\/td>\u2705 Oui<\/td>\u2705 Oui<\/td><\/tr>\n
Prix d'entr\u00e9e (en $\/million)<\/td>$0.10<\/td>$0.08<\/td><\/tr>\n
Prix de sortie (en $\/million)<\/td>$0.32<\/td>$0.28<\/td><\/tr>\n
VRAM (4 bits)<\/td>~40 Go<\/td>~20 Go<\/td><\/tr>\n
GPU minimal (local)<\/td>2 \u00d7 RTX 4090 \/ 1 \u00d7 GPU 48 Go<\/td>RTX 4090 24 Go (quantification Q4)<\/td><\/tr>\n
Date de sortie<\/td>2024<\/td>2025<\/td><\/tr>\n <\/tbody>\n <\/table>\n\n
\n

Principales diff\u00e9rences<\/h3>\n