DeepSeek：全球AI影响（应用）

2025!1"DeepSeek!"#AI$%&'()#$%SAC NO. S0570519080006 | SFC NO. BQZ938&'(SAC NO. S05701220801381!"#$)*+,-./0)*+,-.123456789:;<=>?@ABCD1)*+,-.EFGH AI IJ2%KLCD1MNOPQRS3TUV6WX/89YZV6WX/0 AI 89[\]^89YZV6WX1?@H_`:S3;<ab2%VcCD/defghijkl*mnQo1VpqIrst/uvefwxyzwAI ARR{|}1CapEx~•ÄÅÇ2025-01-30ÉÑghÖefÜá1DeepSeek!"#$%&'3!"#$%&'()DeepSeek%&'()*+,-./012,34/01•DeepSeek!"#$%&'()"#*+,-DeepSeek./01iOS./234567•89:;<=!"#>?@ABCDEF()"#GHI%B<=!"#J*()G!"#$%DeepSeek()*Bloomberg*+,-.4!"#$%&'()DeepSeek vs Meta5/06789:;<•Meta>Llama"#KL>MNOPQRSTLlama License 1.0UVWXYZP/[\/]^_`ab"#-cd6efF>\/gh7iT-Llama>()jklmnopqrstMetauv>wx2yz!n">{|}~•7•ÄÅ`ÇÉ\/ÑÖVÜáÉàQâ23ä\/ Llama "#yzÄÅãÇÉåç-éèêëíÖ/7•ì|\/ghVTîì|> ïñó/[òSMAUUôö 7 õÉ-úêù ûü Meta >fFPQ-†Q°¢£ {|ëí7•§•¶{VTîß®•%§•¶{-•¶ Llama •% API ã©™´¨-Q°êùûü Meta PQ-ä≠ÆjkëíÖ/7•DeepSeek-R1 Ø/∞ MIT PQR yz()V†±≤≥ÜáÉàQ⥵∂\/]^_`ab∑"#-∏π/;{|ç>7•ÜáÉàQ⥵∂\/∑"#-∏πÇÉ]∫ªºΩ`{|æø7•ZP\/"#yzÜá¿¡>ñ¬-TÄÅ](b]√ƒ]≈∆}C7•/[Qâ:"#yz^_]«}`_y7•ZP/[≠Æ¥»>êùoh"#-yz… (b7•/[QâÀÃÕ"#ã^_Œ>œ–yz—h]ab`“”7•‘êù’÷>PQãëíÖ/7•MIT PQRZP"#>{|}\/7†±≤≥QâÀ"#/;{|≈∆<-èêëíœvÖ/7!"#$%DeepSeek()*Bloomberg*+,-.5!"#$%&'()DeepSeek%)*=>?@•◊;()"#>ÿ6 ®Ÿ⁄¤‹-›fi"#>$–>fl‡2·-‚$–±≤≥„‚>‰ÂÊÁ`„Ë>ÈÍÎÏÌ-M./n"}ÓÔ>ùÒÚ7DeepSeek◊;ÛÙıˆ±˜¯ÛToken˘˙˚!¸˝¤‹-ZP\/˛ˇ>!¸Ôfi"#`$%&d'(°˜>"#7•Anthropic>)*+z,-./·012SDario AmodeiU?%-¸˝>y345„fl-Qâ≈6107>_y78GPT-39Ô>$%o:é;-$–2·<1/12007$1M Tokens!"1M Tokens!#DeepSeek-chat0.140.28DeepSeek-reasoner0.552.19OpenAI o11560OpenAI o1-mini312OpenAI 4o515OpenAI 4o-mini0.150.60Meta Llama 3.2 Instruct$70B%0.720.72!"#$%DeepSeek()*Bloomberg*+,-.6!"#$%&'()DeepSeek%)*AB•◊;˛!◊="#>?>$%@¡-:;•Ë"#>$%°˜5Òù7DeepSeek-R1≈∆jklmQB"#>?G7•\/DeepSeek-R1ABScuratedU>80CÇD–-EFßG∞Qwen`LlamaC()"#7ÄÅHîIl-JK>>?L˝M≥NO∞˛P"#>$%°˜7:;>?"#-Q./SFT-‘\/RLSR™HSRLQâ!T•Ë"#U°U7•&√"#()VDeepSeek-R1-Zero]DeepSeek-R1â£◊;Qwen`LlamaADeepSeek-R1<>?>VÇWX"#S1.5B]7B]8B]14B]32B]70BU7!"#$%&'(•1!"#$%&'()*+,-./0,-!•2!1+,-2"#3456789.:;<=>?@ABC89DEF*GHIJ!•3!K1L,-.M6,-!NO:;<PQR;<BC"#!"#$%DeepSeek()*Bloomberg*+,-.7!"#$%&'()DeepSeek%)*AB•>?P"#>U°-«;:P"#EFyzRL7'Y;DeepSeek-R1ZP"#rRL[\]∞^v7•rQwen-32B-Base[\/ò∫]_``STEMòÆyz!n"RL"#-"#ôö10K3-ü<DeepSeek-R1-Zero-Qwen-32B7•HaV\/Qwen2.5 32B•%◊="#-EFADeepSeek-R1yz>?ü<>"#U°-«;rQwen2.5 32B[./O}∫b7•1UÀ„O!>"#>?$„P>"#Qâûücd>Hî-éef–g•<>!n"RL>„P>"#-‘Qêùh!>!¸°˜-45Q°i-‘<>?>U°7•2UXY>?jkltmÿdn-cùôop°>qr-Q°sYêù„O!>◊="#`„!n">O}∫b7•◊="#O>t{-uP"#(D„d«v7wxM:;RL¸˝7ST*UVRL*UVTherefore, we can draw two conclusions: First, distilling more powerful models into smaller ones yields excellent results, whereas smaller models relying on the large-scale RL mentioned in this paper require enormous computational power and may not even achieve the performance of distillation. Second, while distillation strategies are both economical and effective, advancing beyond the boundaries of intelligence may still require more powerful base models and larger-scale reinforcement learning.!"#$%DeepSeek()*Bloomberg*+,-.2()*+DeepSeek!"#,-9!"#$%&'()DeepSeek%CD)*EFAIGHInfraModelSoftware!"#$%/&'()*bloomberg*+,-.10!"#$%&'()IJKLMNOPQRDeepSeek!"#$%/&'()*bloomberg*+,-