{"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\/de\/deepseek-r1-vs-deepseek-v4-pro\/","title":{"rendered":"DeepSeek R1 vs. DeepSeek V4-Pro: Spezifikationen, Preise & Entscheidungshilfe (2026)"},"content":{"rendered":"

DeepSeek R1<\/strong> vs. 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
Spezifikation<\/th>DeepSeek R1<\/a><\/th>DeepSeek V4-Pro<\/a><\/th><\/tr><\/thead>\n
Entwickler<\/td>DeepSeek<\/td>DeepSeek<\/td><\/tr>\n
Typ<\/td>LLM (MoE, Reasoning)<\/td>LLM (MoE)<\/td><\/tr>\n
Parameter<\/td>671 Mrd. insgesamt \/ 37 Mrd. aktiv (MoE)<\/td>1,6 Bio. insgesamt \/ ~49 Mrd. aktiv (MoE)<\/td><\/tr>\n
Kontextfenster<\/td>128K<\/td>1 Mio.<\/td><\/tr>\n
Modalit\u00e4t<\/td>Text \u2192 Text<\/td>Text \u2192 Text<\/td><\/tr>\n
Lizenz<\/td>MIT (offen)<\/td>MIT (offen)<\/td><\/tr>\n
Offene Gewichte<\/td>\u2705 Ja<\/td>\u2705 Ja<\/td><\/tr>\n
Eingabepreis (US$\/1 Mio.)<\/td>$0.50<\/td>$0.435<\/td><\/tr>\n
Ausgabepreis (US$\/1 Mio.)<\/td>$2.15<\/td>$0.87<\/td><\/tr>\n
VRAM (4-Bit)<\/td>~400 GB<\/td>~800 GB<\/td><\/tr>\n
Mindest-GPU (lokal)<\/td>Multi-GPU-Server<\/td>Multi-GPU-Server (z. B. 8\u00d7 H100 mit 80 GB)<\/td><\/tr>\n
Ver\u00f6ffentlichung<\/td>2025<\/td>2026-04<\/td><\/tr>\n <\/tbody>\n <\/table>\n\n
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