{"id":6216,"date":"2025-04-15T13:04:53","date_gmt":"2025-04-15T13:04:53","guid":{"rendered":"https:\/\/www.myshirtai.com\/?p=6216"},"modified":"2025-04-15T13:04:53","modified_gmt":"2025-04-15T13:04:53","slug":"openai-%e5%8f%91%e5%b8%83-gpt-4-1-%e7%b3%bb%e5%88%97%ef%bc%9a%e7%bc%96%e7%a0%81%e3%80%81%e6%8c%87%e4%bb%a4%e6%89%a7%e8%a1%8c%e4%b8%8e%e9%95%bf%e4%b8%8a%e4%b8%8b%e6%96%87%e8%83%bd%e5%8a%9b%e5%a4%a7","status":"publish","type":"post","link":"https:\/\/www.myshirtai.com\/de\/archives\/6216","title":{"rendered":"OpenAI ver\u00f6ffentlicht GPT-4.1-Serie: Dramatische Verbesserungen bei Codierung, Befehlsausf\u00fchrung und langen Kontexten"},"content":{"rendered":"

I. Einleitung<\/h3>\n
Am 15. April 2025 stellte OpenAI offiziell die neue Modellreihe GPT-4.1 vor, darunter GPT-4.1, GPT-4.1 mini und GPT-4.1 nano. Diese Version markiert einen weiteren bedeutenden Durchbruch bei der Leistung, Kosteneffizienz und den realen Anwendungsm\u00f6glichkeiten der OpenAI-Modelle, insbesondere in den Bereichen Codierungsaufgaben, Befehlsbefolgung und Verarbeitung langer Kontexte, und bietet Entwicklern eine bessere Auswahl bei niedrigeren Preisen und Latenzen.<\/div>\n

GPT-4.1 mini ist jetzt bei ShirtAI kostenlos und unbegrenzt nutzbar, nur einen Klick von der offiziellen Website entfernt:www.lsshirtai.com<\/a> <\/span><\/strong><\/p>\n

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Wenn Sie als API aufrufen m\u00f6chten<\/strong><\/strong> GPT-4.1<\/strong> finden Sie auf der Website:https:\/\/coultra.blueshirtmap.com\/<\/a><\/span><\/strong><\/p>\n

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II. \"Leapfrogging\" bei den Kodierf\u00e4higkeiten: St\u00e4rkung der gesamten Dimension von der Codegenerierung bis zur technischen Praxis<\/h3>\n
Auf dem zentralen Schlachtfeld der Softwareentwicklung zeigt die GPT-4.1-Serie einen qualitativen Wandel von der \"Codefragment-Generierung\" zur \"komplexen technischen Verarbeitung\". Als Antwort auf die realen Anforderungen des Software-Engineerings erreicht das Modell im SWE-Bench Verified Test eine Task Completion Rate von 54,6%, was 21% h\u00f6her ist als bei seinem Vorg\u00e4nger GPT-4o und sogar die noch nicht ver\u00f6ffentlichte GPT-4.5-Vorschauversion um 26,6 Prozentpunkte \u00fcbertrifft. Dieser Durchbruch spiegelt sich nicht nur in der Genauigkeit der Codelogik wider, sondern auch im tiefgreifenden Verst\u00e4ndnis mehrsprachiger Codebasen - im Aider-Multi-Language-Diff-Benchmark-Test erzielte GPT-4.1 doppelt so viele Punkte wie GPT-4o, und es kann dem Diff-Format genau folgen, um nur die ge\u00e4nderten Zeilen auszugeben, und die Obergrenze der Ausgabe-Tokens stabil auf 32.768 kontrollieren, was die Anzahl der Entwickler deutlich reduziert. Es kann dem Diff-Format genau folgen, um nur die ge\u00e4nderten Zeilen auszugeben, und die Obergrenze der Ausgabe-Token stabil auf 32.768 kontrollieren, was die Debugging-Kosten f\u00fcr die Entwickler erheblich reduziert. Im Frontend-Entwicklungsszenario zeigt die manuelle Bewertung, dass die Wahrscheinlichkeit, dass die generierte Webanwendung in Bezug auf Funktionalit\u00e4t und \u00c4sthetik bevorzugt wird, 80% erreicht, und die Full-Stack-Entwicklungsf\u00e4higkeit \u00fcbertrifft zum ersten Mal die meisten dedizierten Code-Modelle.<\/div>\n
Vergleich der Kernindikatoren:<\/div>\n
\n\n\n\n\n\n\n\n\n
Modellierung<\/th>\nSWE-Bank Gepr\u00fcft<\/th>\nAider Mehrsprachige Benchmarks<\/th>\nManuelle Bewertung der Front-End-Entwicklung<\/th>\nObere Grenze des Tokens ausgeben<\/th>\nCode diff Genauigkeit<\/th>\n<\/tr>\n<\/thead>\n
GPT-4.1<\/td>\n54.6%<\/td>\n11.2<\/td>\n80%<\/td>\n32768<\/td>\n53%<\/td>\n<\/tr>\n
GPT-4.5 Vorschau<\/td>\n38.0%<\/td>\n7.4<\/td>\n52%<\/td>\n16384<\/td>\n45%<\/td>\n<\/tr>\n
o3-mini-high<\/td>\n49.3%<\/td>\n9.8<\/td>\n65%<\/td>\n16384<\/td>\n60%<\/td>\n<\/tr>\n
o1<\/td>\n41.2%<\/td>\n6.1<\/td>\n48%<\/td>\n128000<\/td>\n62%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

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 <\/p>\n

III. der Durchbruch bei der Befehlsausf\u00fchrung: Genauigkeit und Zuverl\u00e4ssigkeit bei der Bearbeitung komplexer Aufgaben<\/h3>\n
Angesichts komplexer Anweisungen mit mehreren Schritten und Einschr\u00e4nkungen hat GPT-4.1 den Sprung vom \"Fuzzy Matching\" zur \"pr\u00e4zisen Ausf\u00fchrung\" geschafft. Im MultiChallenge-Benchmark von Scale erreicht die Anweisungstreue einen Wert von 38,3% und liegt damit 10,5% \u00fcber dem Wert von GPT-4o. Der IFEval-Benchmark liegt bei 87,4% und \u00fcbertrifft damit den Wert des Vorg\u00e4ngers von 81,0% bei weitem. Das Modell st\u00e4rkt insbesondere die drei Hauptschwierigkeiten Formatkonformit\u00e4t (z.B. XML\/YAML verschachtelte Strukturen), negative Anweisungen (explizite Ablehnung sensibler Anfragen) und ordnungsgem\u00e4\u00dfe Aufgaben (schrittweise Ausf\u00fchrung von Workflows), und die H\u00e4ufigkeit ung\u00fcltiger Bearbeitungen in den schwierigen Cueing-Szenarien sank in einer internen OpenAI-Evaluierung von 9% in GPT-4o auf 2%. In mehreren Dialogrunden erreicht die kontextuelle Koh\u00e4renz 92%, wobei die in den historischen Anweisungen geforderten Details genau erfasst werden, was eine industrietaugliche Zuverl\u00e4ssigkeit f\u00fcr intelligenten Kundenservice, automatisierte Arbeitsabl\u00e4ufe und andere Szenarien bietet.<\/div>\n
Vergleich der Kernindikatoren:<\/div>\n
\n\n\n\n\n\n\n\n\n
Modellierung<\/th>\nMultiChallenge<\/th>\nIFEval<\/th>\nKoh\u00e4renz des Mehrrunden-Dialogs<\/th>\nNegative Weisungen werden befolgt<\/th>\nOrdnungsgem\u00e4\u00dfe Erledigung des Mandats<\/th>\n<\/tr>\n<\/thead>\n
GPT-4.1<\/td>\n38.3%<\/td>\n87.4%<\/td>\n92%<\/td>\n98%<\/td>\n95%<\/td>\n<\/tr>\n
GPT-4.5 Vorschau<\/td>\n44.2%<\/td>\n81.0%<\/td>\n78%<\/td>\n89%<\/td>\n82%<\/td>\n<\/tr>\n
o3-mini-high<\/td>\n40.1%<\/td>\n85.2%<\/td>\n88%<\/td>\n96%<\/td>\n91%<\/td>\n<\/tr>\n
o1<\/td>\n45.1%<\/td>\n87.1%<\/td>\n89%<\/td>\n97%<\/td>\n94%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

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 <\/p>\n

Innovation mit langem Kontext: Millionen von Token-Fenstern er\u00f6ffnen neue M\u00f6glichkeiten f\u00fcr tiefgreifende Multiszenario-Anwendungen.<\/h3>\n
GPT-4.1 wird standardm\u00e4\u00dfig mit einem Kontextfenster von 1 Million Token geliefert, das die Verarbeitung von Langtexten in eine neue Dimension bringt - es kann etwa 8 komplette React-Codebases oder 3.000 Seiten juristischer Dokumente aufnehmen, was den Schmerzpunkt \"Out-of-Context\" des Vorg\u00e4ngermodells vollst\u00e4ndig l\u00f6st. \"Schmerzpunkt des vorherigen Modells. In der Video-MME-Aufgabe zur Analyse von langen Videos ohne Skript erzielte das Modell 72%, was eine Verbesserung von 6,7% gegen\u00fcber GPT-4o bedeutet. Tests mit dem Open-Source-Datensatz Graphwalks zeigten, dass seine Multi-Hop-Inferenzgenauigkeit bei Millionen von Token 61,7% erreichte und damit weit \u00fcber der des o1-Modells lag, das auf kurzen Kontexten basiert (48,7%). OpenAI optimiert synchron die Wirtschaftlichkeit von Anfragen mit langen Kontexten: ein Fenster von 1 Million Token ist im Standardpreis enthalten, der Cache-Rabatt wurde von 50% auf 75% erh\u00f6ht, und die Antwortlatenz von 128K Token wurde auf 15 Sekunden reduziert, was 30% schneller ist als GPT-4.5 und eine fundierte technische L\u00f6sung f\u00fcr Szenarien wie die \u00dcberpr\u00fcfung von Rechtsvertr\u00e4gen und die Pr\u00fcfung gro\u00dfer Codebasen darstellt.<\/div>\n
Vergleich der Kernindikatoren:<\/div>\n
\n\n\n\n\n\n\n\n\n
Modellierung<\/th>\nKontextfenster<\/th>\nVideo-MME ohne Untertitel<\/th>\nGraphwalks Argumentation<\/th>\nCache-Rabatt<\/th>\n128K Token-Verz\u00f6gerung<\/th>\n<\/tr>\n<\/thead>\n
GPT-4.1<\/td>\n1,000,000<\/td>\n72.0%<\/td>\n61.7%<\/td>\n75%<\/td>\n15 Sekunden.<\/td>\n<\/tr>\n
GPT-4.5 Vorschau<\/td>\n128,000<\/td>\n65.3%<\/td>\n42.0%<\/td>\n50%<\/td>\n22 Sekunden.<\/td>\n<\/tr>\n
o3-mini-high<\/td>\n256,000<\/td>\n68.5%<\/td>\n55.2%<\/td>\n50%<\/td>\n18 Sekunden.<\/td>\n<\/tr>\n
o1<\/td>\n128,000<\/td>\n64.1%<\/td>\n48.7%<\/td>\n50%<\/td>\n25 Sekunden.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

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V. Kosten und Effizienz: ein pragmatisches Upgrade f\u00fcr Entwickler<\/h3>\n
OpenAIs \"abgestufte Preis- und Leistungsoptimierungsstrategie\" erm\u00f6glicht es Entwicklern aller Gr\u00f6\u00dfenordnungen, eine kosteng\u00fcnstige Option zu erhalten. Das Einsteigermodell GPT-4.1 nano reduziert die Eingabekosten auf $2\/Millionen Token und die Ausgabekosten auf $8\/Millionen Token unter Beibehaltung eines Fensters von einer Million Token und reduziert die Latenzzeit um 50% im Vergleich zu GPT-4o, was es zur bevorzugten Wahl f\u00fcr leichte Aufgaben wie Textkategorisierung und Autovervollst\u00e4ndigung macht; das Mittelklassemodell GPT-4.1 mini \u00fcbertrifft GPT-4o in Szenarien mit mittlerer Belastung wie Codegenerierung und Dialogen mit mehreren Runden, w\u00e4hrend es die Kosten um 60% reduziert. Das Mittelklassemodell GPT-4.1 mini \u00fcbertrifft GPT-4o bei der Codegenerierung, Mehrrunden-Dialogen und anderen Szenarien mit mittlerer Belastung und kostet dabei 60% weniger. Im Vergleich dazu betragen die Input-Kosten der GPT-4.5-Vorschau bis zu 75 $\/Millionen Token, was nur ein F\u00fcnfundzwanzigstel des Preis-\/Leistungsverh\u00e4ltnisses von GPT-4.1 ausmacht, was der Hauptgrund daf\u00fcr ist, dass es bis Juli 2025 veraltet sein wird und das neue Modell einheitlich das Modell \"GPT-4\" \u00fcbernimmt. Dar\u00fcber hinaus sieht das neue Modell eine einheitliche Politik \"kein Aufschlag f\u00fcr lange Kontexte\" vor, was den Kostenschmerz des vorherigen Modells bei der Bearbeitung langer Texte v\u00f6llig ver\u00e4ndert.<\/div>\n
Vergleich der Kernindikatoren:<\/div>\n
\n\n\n\n\n\n\n\n\n\n\n
Modellierung<\/th>\nInputkosten ($ \/ Million Token)<\/th>\nProduktionskosten ($\/Million Token)<\/th>\nVerz\u00f6gerung (128K Token)<\/th>\n<\/tr>\n<\/thead>\n
GPT-4.1 nano<\/td>\n0.10<\/td>\n0.40<\/td>\n5 Sekunden.<\/td>\n<\/tr>\n
GPT-4.1 mini<\/td>\n0.40<\/td>\n1.60<\/td>\n8 Sekunden.<\/td>\n<\/tr>\n
GPT-4.1<\/td>\n2.00<\/td>\n8.00<\/td>\n15 Sekunden.<\/td>\n<\/tr>\n
GPT-4.5 Vorschau<\/td>\n75.0<\/td>\n150.0<\/td>\n22 Sekunden.<\/td>\n<\/tr>\n
o3-mini-high<\/td>\n1.10<\/td>\n4.40<\/td>\n18 Sekunden.<\/td>\n<\/tr>\n
o1<\/td>\n15.00<\/td>\n60.00<\/td>\n25 Sekunden.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
* :: Kosten-Leistungs-Index = (Kodierf\u00e4higkeit + Befehlspunkte + Kontextfenster)\/(Kosten + Latenz), je h\u00f6her der Wert, desto besser<\/span><\/div>\n
\n

 <\/p>\n

Wenn Sie GPT Plus, Claude Pro, Grok Super offizielles, bezahltes Exklusivkonto nutzen m\u00f6chten, k\u00f6nnen Sie unser professionelles Team (wx: abch891) kontaktieren, wenn Sie nicht wissen, wie Sie Ihr Konto aufladen k\u00f6nnen.<\/strong><\/p>\n\n\n\n\n\n\n\n\n
\n

Weitere Produkte finden Sie unter<\/h4>\n<\/th>\n

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Siehe mehr unter<\/h4>\n<\/th>\n<\/tr>\n<\/thead>\n

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1:1 Wiederherstellung von Claude und GPT Offizielle Website - AI Cloud Native<\/a><\/td>\nLive Match App Global HD Sports Viewing Player (empfohlen) - Blueshirt Technology<\/a><\/td>\n<\/tr>\n
Transitdienst auf der Grundlage der offiziellen API - GPTMeta API<\/a><\/td>\nHilfe, kann jemand von Ihnen Tipps geben, wie man Fragen auf GPT stellt? - Wissen<\/a><\/td>\n<\/tr>\n
Global Virtual Goods Digital Store - Global SmarTone (Feng Ling Ge)<\/a><\/td>\nWie leistungsf\u00e4hig ist Claude airtfacts, dass GPT sofort nicht mehr gut riecht? -BeepBeep<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>","protected":false},"excerpt":{"rendered":"

\u4e00\u3001\u5f15\u8a00 2025 \u5e74 4 \u6708 15 \u65e5\uff0cOpenAI \u6b63\u5f0f\u63a8\u51fa\u5168\u65b0\u7684 GPT-4.1 \u7cfb\u5217\u6a21\u578b\uff0c\u5305\u542b GPT 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