{"id":317,"date":"2026-11-04T08:16:28","date_gmt":"2026-11-04T02:46:28","guid":{"rendered":"https:\/\/akshaymaurya.in\/blog\/?p=317"},"modified":"2026-11-04T08:16:31","modified_gmt":"2026-11-04T02:46:31","slug":"longcat-2-5-chinese-model-hindi","status":"publish","type":"post","link":"https:\/\/akshaymaurya.in\/blog\/longcat-2-5-chinese-model-hindi\/","title":{"rendered":"LongCat 2.5 \u0915\u094d\u092f\u093e \u0939\u0948: 1.6 Trillion Parameter \u0935\u093e\u0932\u093e Chinese AI Model \u091c\u094b \u0938\u093f\u0930\u094d\u092b Chinese Chips \u092a\u0930 Train \u0939\u0941\u0906"},"content":{"rendered":"\n

2 \u0928\u0935\u0902\u092c\u0930 2026 \u0915\u094b \u091a\u0940\u0928 \u0915\u0940 food delivery \u0926\u093f\u0917\u094d\u0917\u091c Meituan \u0928\u0947 \u0905\u092a\u0928\u093e \u0928\u092f\u093e AI model LongCat-2.5 release \u0915\u093f\u092f\u093e\u0964 \u092f\u0939 1.6 trillion parameter \u0935\u093e\u0932\u093e natively multimodal mixture-of-experts model \u0939\u0948 \u091c\u094b \u092a\u0942\u0930\u0940 \u0924\u0930\u0939 \u0918\u0930\u0947\u0932\u0942 Chinese AI accelerators \u092a\u0930 train \u0939\u0941\u0906 \u0939\u0948, \u092f\u093e\u0928\u0940 Nvidia chips \u092a\u0930 \u0928\u0939\u0940\u0902\u0964 \u092e\u0947\u0930\u0940 \u0928\u091c\u0930 \u092e\u0947\u0902 \u092f\u0939 2026 \u0915\u0940 \u0938\u092c\u0938\u0947 \u0905\u0939\u092e AI \u0915\u0939\u093e\u0928\u093f\u092f\u094b\u0902 \u092e\u0947\u0902 \u0938\u0947 \u090f\u0915 \u0939\u0948, \u0914\u0930 \u0938\u093f\u0930\u094d\u092b benchmark scores \u0915\u0940 \u0935\u091c\u0939 \u0938\u0947 \u0928\u0939\u0940\u0902, \u092c\u0932\u094d\u0915\u093f chip politics \u0915\u0940 \u0935\u091c\u0939 \u0938\u0947 \u092d\u0940\u0964<\/p>\n

LongCat 2.5 \u0915\u094d\u092f\u093e \u0939\u0948<\/h2>\n

\u0938\u0940\u0927\u0947 \u0936\u092c\u094d\u0926\u094b\u0902 \u092e\u0947\u0902 \u0915\u0939\u0947\u0902 \u0924\u094b LongCat-2.5 \u090f\u0915 \u0910\u0938\u093e AI model \u0939\u0948 \u091c\u093f\u0938\u0947 long-horizon agentic tasks \u0915\u0947 \u0932\u093f\u090f \u092c\u0928\u093e\u092f\u093e \u0917\u092f\u093e \u0939\u0948\u0964 Agentic task \u0915\u093e \u092e\u0924\u0932\u092c \u0939\u0948 \u0910\u0938\u093e \u0915\u093e\u092e \u091c\u0939\u093e\u0902 AI \u0915\u094b \u090f\u0915 prompt \u0915\u093e \u090f\u0915 \u091c\u0935\u093e\u092c \u0928\u0939\u0940\u0902 \u0926\u0947\u0928\u093e \u0939\u094b\u0924\u093e, \u092c\u0932\u094d\u0915\u093f \u0932\u0902\u092c\u0947 \u0914\u0930 messy software projects \u092a\u0930 \u0932\u0917\u093e\u0924\u093e\u0930 \u0915\u093e\u092e \u0915\u0930\u0928\u093e \u0939\u094b\u0924\u093e \u0939\u0948: \u0915\u0908 files \u092a\u0922\u093c\u0928\u093e, code \u092c\u0926\u0932\u0928\u093e, tests \u091a\u0932\u093e\u0928\u093e \u0914\u0930 error \u0926\u0947\u0916\u0915\u0930 \u0926\u094b\u092c\u093e\u0930\u093e \u0915\u094b\u0936\u093f\u0936 \u0915\u0930\u0928\u093e\u0964<\/p>\n

LongCat-2.5 text \u0914\u0930 images \u0926\u094b\u0928\u094b\u0902 \u0915\u094b natively \u0938\u092e\u091d\u0924\u093e \u0939\u0948, \u092f\u093e\u0928\u0940 multimodal \u0939\u0948\u0964 \u0907\u0938\u092e\u0947\u0902 \u0915\u0941\u0932 1.6 trillion parameters \u0939\u0948\u0902, \u0932\u0947\u0915\u093f\u0928 mixture-of-experts (MoE) design \u0915\u0940 \u0935\u091c\u0939 \u0938\u0947 \u0939\u0930 token \u092a\u0930 \u0938\u093f\u0930\u094d\u092b \u0915\u0930\u0940\u092c 48 billion parameters active \u0939\u094b\u0924\u0947 \u0939\u0948\u0902, \u091c\u093f\u0938\u0938\u0947 inference cost \u0915\u092e \u0930\u0939\u0924\u0940 \u0939\u0948\u0964 \u0938\u093e\u0925 \u092e\u0947\u0902 \u0907\u0938\u0947 1-million-token context window \u092e\u093f\u0932\u0924\u093e \u0939\u0948, \u091c\u094b \u0932\u0902\u092c\u0947 project \u0935\u093e\u0932\u0947 agentic workflows \u0915\u0947 \u0932\u093f\u090f \u0939\u0940 \u092c\u0928\u093e \u0939\u0948\u0964<\/p>\n

\u0905\u0902\u0926\u0930 \u0915\u0940 \u0924\u0915\u0928\u0940\u0915: LongCat Sparse Attention<\/h2>\n

\u0939\u0930 \u092c\u0921\u093c\u0947 model launch \u0915\u0947 \u0938\u093e\u0925 \u0915\u094b\u0908 \u0928\u0908 \u0924\u0915\u0928\u0940\u0915\u0940 trick \u0906\u0924\u0940 \u0939\u0948, \u0914\u0930 \u092f\u0939\u093e\u0902 \u0935\u0939 \u0939\u0948 LongCat Sparse Attention, \u091c\u093f\u0938\u0947 \u0938\u0902\u0915\u094d\u0937\u0947\u092a \u092e\u0947\u0902 LSA \u0915\u0939\u093e \u091c\u093e \u0930\u0939\u093e \u0939\u0948\u0964 Sparse attention \u0915\u093e basic idea \u092f\u0939 \u0939\u0948 \u0915\u093f attention mechanism \u0915\u094b \u0939\u0930 token \u0915\u0947 \u0932\u093f\u090f \u0939\u0930 \u0926\u0942\u0938\u0930\u0947 token \u0938\u0947 \u092c\u093e\u0924 \u0928\u0939\u0940\u0902 \u0915\u0930\u0928\u0940 \u092a\u0921\u093c\u0924\u0940\u0964 Model \u0916\u0941\u0926 \u0924\u092f \u0915\u0930\u0924\u093e \u0939\u0948 \u0915\u093f \u0915\u093f\u0938 \u0939\u093f\u0938\u094d\u0938\u0947 \u092a\u0930 \u0927\u094d\u092f\u093e\u0928 \u0926\u0947\u0928\u093e \u0939\u0948, \u091c\u093f\u0938\u0938\u0947 1 million token \u0915\u0940 context window \u092d\u0940 practical \u0930\u0939\u0924\u0940 \u0939\u0948\u0964<\/p>\n

Mere hisaab se \u0905\u0938\u0932\u0940 engineering \u0915\u0939\u093e\u0928\u0940 MoE scale \u0939\u0948\u0964 1.6 trillion \u092e\u0947\u0902 \u0938\u0947 \u0938\u093f\u0930\u094d\u092b 48 billion active \u0930\u0916\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f routing \u0915\u094b \u0907\u0924\u0928\u093e smart \u0939\u094b\u0928\u093e \u092a\u0921\u093c\u0924\u093e \u0939\u0948 \u0915\u093f \u0938\u0939\u0940 expert \u0938\u0939\u0940 token \u0924\u0915 \u092a\u0939\u0941\u0902\u091a\u0947\u0964 \u092f\u0939\u0940 \u0935\u091c\u0939 \u0939\u0948 \u0915\u093f \u092f\u0939 model production agentic workloads \u0915\u0947 \u0932\u093f\u090f \u0921\u093f\u091c\u093c\u093e\u0907\u0928 \u0915\u093f\u092f\u093e \u0917\u092f\u093e \u0939\u0948\u0964<\/p>\n

Chinese chips \u092a\u0930 training: \u0938\u092c\u0938\u0947 \u092c\u0921\u093c\u0940 \u0916\u092c\u0930<\/h2>\n

LongCat-2.5 \u0915\u0940 \u0905\u0938\u0932\u0940 headline \u0907\u0938\u0915\u0947 parameters \u0928\u0939\u0940\u0902, \u0907\u0938\u0915\u0940 training \u0939\u0948\u0964 \u092f\u0939 \u0926\u0942\u0938\u0930\u093e \u0910\u0938\u093e model \u0939\u0948 \u091c\u094b end-to-end Chinese chips \u092a\u0930 train \u0939\u0941\u0906 \u0939\u0948, \u0914\u0930 \u092f\u0939 \u0905\u092a\u0928\u0947 \u092a\u0942\u0930\u094d\u0935\u0935\u0930\u094d\u0924\u0940 LongCat-2.0 (\u091c\u0942\u0928 2026) \u0915\u093e successor \u0939\u0948\u0964 Meituan \u0928\u0947 chip vendor \u0915\u093e \u0928\u093e\u092e \u0928\u0939\u0940\u0902 \u092c\u0924\u093e\u092f\u093e \u0939\u0948\u0964 Community \u092e\u0947\u0902 Huawei Ascend 910C \u0915\u093e \u0905\u0928\u0941\u092e\u093e\u0928 \u0932\u0917 \u0930\u0939\u093e \u0939\u0948, \u0932\u0947\u0915\u093f\u0928 \u092f\u0939 \u092a\u0942\u0930\u0940 \u0924\u0930\u0939 unconfirmed \u0939\u0948, \u0907\u0938\u0932\u093f\u090f \u0907\u0938\u0947 fact \u0915\u0940 \u0924\u0930\u0939 \u0928 \u0932\u0947\u0902\u0964<\/p>\n

Dekhne wali baat yeh hai \u0915\u093f \u092f\u0939 launch US export controls \u0915\u0940 \u0915\u0939\u093e\u0928\u0940 \u0915\u094b \u092c\u0926\u0932 \u0926\u0947\u0924\u093e \u0939\u0948\u0964 \u0905\u092c \u0924\u0915 \u092e\u093e\u0928\u094d\u092f\u0924\u093e \u0925\u0940 \u0915\u093f Nvidia chips \u0915\u0947 \u092c\u093f\u0928\u093e frontier-scale model train \u0915\u0930\u0928\u093e \u0932\u0917\u092d\u0917 \u0905\u0938\u0902\u092d\u0935 \u0939\u0948\u0964 \u0905\u0917\u0930 domestic accelerators \u092a\u0930 1.6T-scale model train \u0939\u094b \u0938\u0915\u0924\u093e \u0939\u0948, \u0924\u094b chip restriction \u0915\u093e leverage \u0915\u092e\u091c\u094b\u0930 \u0939\u094b\u0924\u093e \u0939\u0948\u0964 \u092f\u0939\u0940 \u0935\u091c\u0939 \u0939\u0948 \u0915\u093f \u092f\u0939 \u0916\u092c\u0930 Silicon Valley \u0938\u0947 \u0932\u0947\u0915\u0930 Beijing \u0924\u0915 \u091a\u0930\u094d\u091a\u093e \u092e\u0947\u0902 \u0939\u0948\u0964<\/p>\n

Benchmark \u0926\u093e\u0935\u0947: Claude Opus 5 \u0914\u0930 GPT-5.6-Sol \u0938\u0947 \u0924\u0941\u0932\u0928\u093e<\/h2>\n

Meituan \u0915\u0947 \u0905\u092a\u0928\u0947 test \u0915\u0947 \u092e\u0941\u0924\u093e\u092c\u093f\u0915 LongCat-2.5 mainstream evals \u092e\u0947\u0902 Claude Opus 4.8 \u0914\u0930 Claude Opus 5 \u0915\u0947 \u092c\u0940\u091a \u092c\u0948\u0920\u0924\u093e \u0939\u0948, \u0914\u0930 \u0915\u0941\u091b tasks \u092a\u0930 Opus 5 \u0915\u094b beat \u092d\u0940 \u0915\u0930\u0924\u093e \u0939\u0948\u0964 FourWeekMBA \u0915\u0947 analysis \u0915\u0947 \u0905\u0928\u0941\u0938\u093e\u0930 27 shared tests \u092e\u0947\u0902 \u0938\u0947 20 \u092e\u0947\u0902 \u092f\u0939 OpenAI \u0915\u0947 GPT-5.6-Sol \u0938\u0947 \u090a\u092a\u0930 \u0930\u0939\u093e\u0964 Frontier models \u0915\u0940 \u0907\u0938 \u0926\u094c\u0921\u093c \u092e\u0947\u0902 Gemini 4 Carbon<\/a> \u092d\u0940 \u090f\u0915 \u0928\u092f\u093e \u0926\u093e\u0935\u0947\u0926\u093e\u0930 \u0939\u0948\u0964<\/p>\n

\u092f\u0939\u093e\u0902 \u090f\u0915 \u092c\u093e\u0924 \u0938\u093e\u092b \u0915\u0930\u0928\u093e \u091c\u0930\u0942\u0930\u0940 \u0939\u0948\u0964 \u092f\u0947 \u0938\u093e\u0930\u0947 \u0926\u093e\u0935\u0947 Meituan \u0915\u0947 \u0905\u092a\u0928\u0947 benchmark runs \u0939\u0948\u0902, \u0914\u0930 \u0905\u092d\u0940 \u0924\u0915 \u0915\u094b\u0908 independent verification \u0928\u0939\u0940\u0902 \u0939\u0941\u0908 \u0939\u0948\u0964 Vendor \u0915\u0947 benchmark numbers \u0915\u094b \u0939\u092e\u0947\u0936\u093e \u0925\u094b\u0921\u093c\u0947 doubt \u0915\u0947 \u0938\u093e\u0925 \u0926\u0947\u0916\u0928\u093e \u091a\u093e\u0939\u093f\u090f\u0964 \u0905\u0938\u0932\u0940 \u0924\u0938\u094d\u0935\u0940\u0930 \u0924\u092d\u0940 \u0938\u093e\u092b \u0939\u094b\u0917\u0940 \u091c\u092c third-party labs \u0907\u0938\u0947 real workloads \u092a\u0930 test \u0915\u0930\u0947\u0902\u0917\u0947\u0964<\/p>\n

\u0924\u0941\u0932\u0928\u093e: LongCat-2.0, open-weight models \u0914\u0930 Kimi<\/h2>\n

LongCat-2.0 (June 2026) \u0915\u0947 \u092e\u0941\u0915\u093e\u092c\u0932\u0947 2.5 \u090f\u0915 \u092c\u0921\u093c\u093e jump \u0939\u0948: parameter count, context window \u0914\u0930 agentic capability, \u0924\u0940\u0928\u094b\u0902 \u092e\u0947\u0902 \u0906\u0917\u0947\u0964 \u0932\u0947\u0915\u093f\u0928 \u090f\u0915 \u0905\u0939\u092e \u092b\u0930\u094d\u0915 \u0939\u0948\u0964 LongCat-2.5 \u0915\u0947 weights \u0905\u092d\u0940 public \u0928\u0939\u0940\u0902 \u0939\u0948\u0902, GitHub \u0914\u0930 Hugging Face \u092a\u0930 open release \u0938\u093f\u0930\u094d\u092b “planned” \u0939\u0948\u0964<\/p>\n

\u0907\u0938\u0915\u0940 \u0924\u0941\u0932\u0928\u093e \u092e\u0947\u0902 Kimi \u091c\u0948\u0938\u0947 Chinese open-weight models \u092a\u0939\u0932\u0947 \u0938\u0947 downloadable weights \u0915\u0947 \u0938\u093e\u0925 \u0909\u092a\u0932\u092c\u094d\u0927 \u0939\u0948\u0902\u0964 \u092f\u093e\u0928\u0940 weights download \u0915\u0930\u0915\u0947 \u0916\u0941\u0926 experiment \u0915\u0930\u0928\u0947 \u0935\u093e\u0932\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u0905\u092d\u0940 Kimi \u091c\u094d\u092f\u093e\u0926\u093e useful \u0939\u0948\u0964 LongCat-2.5 \u092b\u093f\u0932\u0939\u093e\u0932 API-first model \u0939\u0948, \u0914\u0930 open release \u0915\u0940 \u0915\u094b\u0908 fixed date \u0928\u0939\u0940\u0902 \u0939\u0948\u0964<\/p>\n

Developers \u0915\u0947 \u0932\u093f\u090f \u0909\u092a\u0932\u092c\u094d\u0927\u0924\u093e<\/h2>\n

\u0905\u091a\u094d\u091b\u0940 \u0916\u092c\u0930 \u092f\u0939 \u0939\u0948 \u0915\u093f model \u0905\u092d\u0940 live \u0939\u0948\u0964 LongCat<\/a> \u092a\u0930 LongCat API \u0909\u092a\u0932\u092c\u094d\u0927 \u0939\u0948, \u0914\u0930 endpoints OpenAI\/Anthropic-format \u092e\u0947\u0902 \u0939\u0948\u0902, \u092f\u093e\u0928\u0940 OpenAI API use \u0915\u0930\u0928\u0947 \u0935\u093e\u0932\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f integration \u0906\u0938\u093e\u0928 \u0939\u094b\u0917\u0940\u0964 \u0938\u093e\u0925 \u092e\u0947\u0902 Meituan \u0915\u0940 \u0905\u092a\u0928\u0940 chat service \u092a\u0930 \u092d\u0940 \u092f\u0939 model \u091a\u0932 \u0930\u0939\u093e \u0939\u0948\u0964<\/p>\n

Aam user ke liye iska matlab<\/h2>\n

\u0924\u094b \u0906\u092e developer \u0915\u0947 \u0932\u093f\u090f \u0907\u0938\u0915\u093e \u092e\u0924\u0932\u092c \u0915\u094d\u092f\u093e \u0939\u0948? \u092a\u0939\u0932\u093e, agentic coding workloads \u0915\u0947 \u0932\u093f\u090f \u090f\u0915 \u0914\u0930 serious option, OpenAI-format endpoints \u0915\u0947 \u0938\u093e\u0925\u0964 \u0905\u0917\u0930 pricing competitive \u0930\u0939\u0940 \u0924\u094b experiment \u0915\u0930\u0928\u093e \u0906\u0938\u093e\u0928 \u0939\u094b\u0917\u093e\u0964 \u0926\u0942\u0938\u0930\u093e, \u0914\u0930 \u092e\u0947\u0930\u0947 \u0939\u093f\u0938\u093e\u092c \u0938\u0947 \u092c\u0921\u093c\u093e \u092e\u0924\u0932\u092c: Chinese chips \u092a\u0930 train \u0939\u0941\u0906 frontier-scale model \u0938\u093e\u092c\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948 \u0915\u093f hardware monopoly \u091f\u0942\u091f \u0930\u0939\u0940 \u0939\u0948\u0964<\/p>\n

Bigger picture \u092f\u0939 \u0939\u0948 \u0915\u093f 2026 \u092e\u0947\u0902 AI race \u0926\u094b fronts \u092a\u0930 \u091a\u0932 \u0930\u0939\u0940 \u0939\u0948: models \u0915\u0940 capability \u0914\u0930 chips \u0915\u0940 independence\u0964 LongCat-2.5 \u0926\u094b\u0928\u094b\u0902 fronts \u092a\u0930 \u090f\u0915 \u0938\u093e\u0925 \u0916\u092c\u0930 \u0939\u0948\u0964 \u0915\u094d\u092f\u093e \u092f\u0939 \u0935\u093e\u0915\u0908 Claude Opus 5 \u0915\u094b \u091f\u0915\u094d\u0915\u0930 \u0926\u0947\u0924\u093e \u0939\u0948, \u092f\u0939 independent tests \u092c\u0924\u093e\u090f\u0902\u0917\u0947\u0964 \u0932\u0947\u0915\u093f\u0928 Chinese chips \u092a\u0930 1.6T model \u0915\u093e train \u0939\u094b \u091c\u093e\u0928\u093e \u0905\u092a\u0928\u0947 \u0906\u092a \u092e\u0947\u0902 \u090f\u0915 verified fact \u0939\u0948, \u0914\u0930 \u0935\u0939\u0940 \u0907\u0938 \u0915\u0939\u093e\u0928\u0940 \u0915\u0940 \u0938\u092c\u0938\u0947 \u092c\u0921\u093c\u0940 \u092c\u093e\u0924 \u0939\u0948\u0964<\/p>\n

FAQ<\/h2>\n

LongCat 2.5 \u0915\u094d\u092f\u093e \u0939\u0948?<\/h3>\n

\u092f\u0939 Meituan \u0915\u093e \u0928\u092f\u093e natively multimodal mixture-of-experts AI model \u0939\u0948, \u091c\u093f\u0938\u0947 long-horizon agentic tasks \u0915\u0947 \u0932\u093f\u090f \u092c\u0928\u093e\u092f\u093e \u0917\u092f\u093e \u0939\u0948\u0964 \u092f\u0939 2 \u0928\u0935\u0902\u092c\u0930 2026 \u0915\u094b release \u0939\u0941\u0906\u0964<\/p>\n

\u092f\u0939 model \u0915\u093f\u0924\u0928\u093e \u092c\u0921\u093c\u093e \u0939\u0948?<\/h3>\n

\u0915\u0941\u0932 1.6 trillion parameters \u0939\u0948\u0902, \u0932\u0947\u0915\u093f\u0928 \u0939\u0930 token \u092a\u0930 \u0938\u093f\u0930\u094d\u092b \u0915\u0930\u0940\u092c 48 billion parameters active \u0939\u094b\u0924\u0947 \u0939\u0948\u0902\u0964 Context window 1 million token \u0915\u0940 \u0939\u0948\u0964<\/p>\n

\u092f\u0939 \u0915\u093f\u0928 chips \u092a\u0930 train \u0939\u0941\u0906 \u0939\u0948?<\/h3>\n

\u092a\u0942\u0930\u0940 \u0924\u0930\u0939 domestic Chinese AI accelerators \u092a\u0930\u0964 Meituan \u0928\u0947 vendor \u0915\u093e \u0928\u093e\u092e \u0928\u0939\u0940\u0902 \u092c\u0924\u093e\u092f\u093e\u0964 Huawei Ascend 910C \u0915\u093e \u0905\u0928\u0941\u092e\u093e\u0928 community \u0915\u093e \u0939\u0948, unconfirmed\u0964<\/p>\n

Benchmark \u092e\u0947\u0902 \u092f\u0939 \u0915\u093f\u0924\u0928\u093e \u0905\u091a\u094d\u091b\u093e \u0939\u0948?<\/h3>\n

Meituan \u0915\u0947 \u0905\u092a\u0928\u0947 \u0926\u093e\u0935\u0947 \u0915\u0947 \u092e\u0941\u0924\u093e\u092c\u093f\u0915 \u092f\u0939 Claude Opus 4.8 \u0914\u0930 Opus 5 \u0915\u0947 \u092c\u0940\u091a \u0939\u0948, \u0915\u0941\u091b tasks \u092a\u0930 Opus 5 \u0938\u0947 \u092c\u0947\u0939\u0924\u0930\u0964 Independent verification \u0905\u092d\u0940 \u092c\u093e\u0915\u0940 \u0939\u0948\u0964<\/p>\n

\u0915\u094d\u092f\u093e \u0907\u0938\u0915\u0947 weights download \u0915\u093f\u090f \u091c\u093e \u0938\u0915\u0924\u0947 \u0939\u0948\u0902?<\/h3>\n

\u0905\u092d\u0940 \u0928\u0939\u0940\u0902\u0964 GitHub \u0914\u0930 Hugging Face \u092a\u0930 open release \u0938\u093f\u0930\u094d\u092b planned \u0939\u0948\u0964 \u092b\u093f\u0932\u0939\u093e\u0932 API \u0915\u0947 \u091c\u0930\u093f\u090f \u0907\u0938\u094d\u0924\u0947\u092e\u093e\u0932 \u0915\u093f\u092f\u093e \u091c\u093e \u0938\u0915\u0924\u093e \u0939\u0948\u0964<\/p>\n

Developers \u0907\u0938\u0947 \u0915\u0948\u0938\u0947 use \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902?<\/h3>\n

longcat.ai \u092a\u0930 LongCat API \u0915\u0947 \u091c\u0930\u093f\u090f, \u091c\u094b OpenAI\/Anthropic-format endpoints \u0926\u0947\u0924\u093e \u0939\u0948\u0964 Meituan \u0915\u0940 chat service \u092a\u0930 \u092d\u0940 \u092f\u0939 live \u0939\u0948\u0964<\/p>\n","protected":false},"excerpt":{"rendered":"

2 \u0928\u0935\u0902\u092c\u0930 2026 \u0915\u094b \u091a\u0940\u0928 \u0915\u0940 food delivery \u0926\u093f\u0917\u094d\u0917\u091c Meituan \u0928\u0947 \u0905\u092a\u0928\u093e \u0928\u092f\u093e AI model LongCat-2.5 release \u0915\u093f\u092f\u093e\u0964 \u092f\u0939 1.6 trillion parameter \u0935\u093e\u0932\u093e natively multimodal mixture-of-experts model \u0939\u0948 \u091c\u094b \u092a\u0942\u0930\u0940 \u0924\u0930\u0939 \u0918\u0930\u0947\u0932\u0942 Chinese AI accelerators \u092a\u0930 train \u0939\u0941\u0906 \u0939\u0948, \u092f\u093e\u0928\u0940 Nvidia chips \u092a\u0930 \u0928\u0939\u0940\u0902\u0964 \u092e\u0947\u0930\u0940 \u0928\u091c\u0930 \u092e\u0947\u0902 \u092f\u0939 2026 \u0915\u0940 \u0938\u092c\u0938\u0947 \u0905\u0939\u092e AI \u0915\u0939\u093e\u0928\u093f\u092f\u094b\u0902 \u092e\u0947\u0902 […]<\/p>\n","protected":false},"author":1,"featured_media":311,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[1],"tags":[147,144,146,143,145],"class_list":["post-317","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-ai-benchmark","tag-chinese-ai-model","tag-chinese-chips","tag-longcat-2-5","tag-meituan"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"https:\/\/akshaymaurya.in\/blog\/wp-content\/uploads\/2026\/11\/thumb-longcat-2-5-chinese-model-hindi.jpg","_links":{"self":[{"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/posts\/317","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/comments?post=317"}],"version-history":[{"count":1,"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/posts\/317\/revisions"}],"predecessor-version":[{"id":318,"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/posts\/317\/revisions\/318"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/media\/311"}],"wp:attachment":[{"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/media?parent=317"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/categories?post=317"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/akshaymaurya.in\/blog\/wp-json\/wp\/v2\/tags?post=317"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}