\n| \n GLM-5.3<\/p>\n<\/td>\n | \n 2.68<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u7efc\u5408\u8868\u73b0\u5c45\u5f00\u6e90\u6a21\u578b\u7b2c\u4e00\u3002\u91d1\u878d\u667a\u80fd\u4f53\u516c\u53f8\u8baf\u5154\u7684\u5185\u90e8\u8bc4\u6d4b IRAB \u4e0a，Hy4 \u6bd4 Preview \u63d0\u5347 12.03 \u4e2a\u767e\u5206\u70b9\u3002<\/p>\n \u79d1\u5b66\u7814\u7a76<\/h3>\n\u5c0f\u9f20\u80da\u80ce\u4e09\u7ef4\u91cd\u5efa\u4efb\u52a1，84 \u5f20\u65e0\u5e8f\u4e8c\u7ef4\u5207\u7247\u3001\u542b 699 \u4e07\u4e2a\u7ec6\u80de\u3001\u8fd8\u6709\u955c\u50cf\u7ffb\u8f6c\u95ee\u9898，\u5728\u79bb\u7ebf\u3001\u6ca1\u6709\u4e13\u7528\u914d\u51c6\u5de5\u5177\u3001\u6ca1\u6709\u7ec6\u80de\u7c7b\u578b\u6807\u6ce8\u7684\u6761\u4ef6\u4e0b，1.5 \u5c0f\u65f6\u5185\u5b8c\u6210\u5207\u7247\u5bf9\u9f50\u4e0e\u4e09\u7ef4\u91cd\u5efa，\u5168\u7a0b\u65e0\u4eba\u5e72\u9884，\u6548\u679c\u4f18\u4e8e\u5e38\u7528\u914d\u51c6\u5de5\u5177\u7684\u9ed8\u8ba4\u6d41\u7a0b\u3002\u8fd9\u4e2a\u6848\u4f8b\u7684\u4ef7\u503c\u4e0d\u5728\u4e8e"\u5feb"，\u800c\u5728\u4e8e"\u5de5\u5177\u53d7\u9650\u73af\u5883\u4e0b\u7684\u81ea\u4e3b\u63a8\u8fdb"\u2014\u2014\u8fd9\u624d\u662f\u79d1\u7814 Agent \u771f\u6b63\u96be\u7684\u5730\u65b9\u3002<\/p>\n \u6e38\u620f\u5f00\u53d1<\/h3>\n\u6df1\u5ea6\u9002\u914d UE\u3001Unity\u3001Godot，GameDev \u8bc4\u6d4b\u6bd4 Preview \u63d0\u5347 19.1%，GameLogic \u63d0\u5347 9.8%，\u5e76\u5f00\u59cb\u5f80\u591a\u4eba\u8054\u673a\u573a\u666f\u62d3\u5c55\u3002<\/p>\n \u4e94\u3001\u4e0a\u4e0b\u6587\u3001\u534f\u8bae\u4e0e\u63a8\u7406\u5f00\u5173<\/h2>\n\n- \n
\u4e0a\u4e0b\u6587\u7a97\u53e3 1M，TokenHub \u4fa7\u6700\u5927\u8f93\u5165 960K，\u6700\u5927\u8f93\u51fa 64K\u3002\u5bf9\u6bd4 Hy3 \u7684 256K\/192K\/128K，\u8f93\u5165\u4e0a\u9650\u6da8\u4e86\u4e94\u500d\u3002960K \u5927\u7ea6\u76f8\u5f53\u4e8e\u51e0\u5341\u4e07\u6c49\u5b57，\u4e00\u6574\u672c\u6280\u672f\u624b\u518c\u585e\u8fdb\u53bb\u4e0d\u7528\u5207\u7247\u3002<\/p>\n<\/li>\n - \n
\u540c\u65f6\u517c\u5bb9\u4e09\u5957\u534f\u8bae：OpenAI Chat Completions\u3001OpenAI Responses\u3001Anthropic Messages\u3002\u8fd9\u610f\u5473\u7740\u4f60\u73b0\u6709\u7684 OpenAI SDK \u4ee3\u7801\u6539\u4e00\u4e2a base_url \u548c model \u540d\u5c31\u80fd\u8dd1；Claude \u90a3\u5957\u5de5\u5177\u8c03\u7528\u5199\u6cd5\u4e5f\u80fd\u76f4\u63a5\u8fc1\u79fb\u3002<\/p>\n<\/li>\n - \n
\u6df1\u5ea6\u601d\u8003\u7528 thinking: {"type": "enabled"} \u6253\u5f00，\u8fd4\u56de\u4f53\u91cc\u591a\u4e00\u4e2a reasoning_content \u5b57\u6bb5；reasoning_effort \u652f\u6301 high \u8fd9\u7c7b\u6863\u4f4d\u3002<\/p>\n<\/li>\n<\/ul>\n \u516d\u3001\u699c\u5355\u4e0e\u5b9a\u4ef7：\u80fd\u529b\u6210\u672c\u6bd4\u662f\u5b83\u771f\u6b63\u7684\u5356\u70b9<\/h2>\nArtificial Analysis 11 \u6708 5 \u65e5\u53d1\u5e03\u7684\u667a\u80fd\u6307\u6570\u699c\u5355\u4e0a，Hy4 \u5728 724 \u6b3e\u6a21\u578b\u4e2d\u8dfb\u8eab\u524d\u5217，\u4e2d\u56fd\u6a21\u578b\u91cc\u6392\u7b2c\u4e8c，\u4ec5\u6b21\u4e8e Kimi K3.1 Pro\u3002\u66f4\u5173\u952e\u7684\u662f\u6210\u672c\u6548\u7387：\u5355\u4efb\u52a1\u6210\u672c\u7ea6 0.78 \u7f8e\u5143，\u540c\u5206\u6570\u533a\u95f4\u91cc\u6bd4 Grok 4.7（xhigh）\u4fbf\u5b9c\u7ea6 10 \u500d\u3001\u6bd4 Qwen3.8 Max \u4fbf\u5b9c\u7ea6 13 \u500d\u3002\u5728\u504f\u771f\u5b9e\u667a\u80fd\u4f53\u4efb\u52a1\u7684 Agent Arena \u4e0a，Hy4 \u603b\u699c\u7b2c 13，\u662f\u5168\u7403\u5f00\u6e90\u6a21\u578b\u7b2c\u4e00\u3002<\/p>\n \u5b9a\u4ef7\u7ef4\u6301 Preview \u6c34\u5e73\u4e0d\u53d8：<\/p>\n \n\n\u8ba1\u8d39\u9879<\/p>\n \u4ef7\u683c（\u5143\/\u767e\u4e07 tokens）<\/p>\n<\/tr>\n \n\n| \n \u8f93\u5165<\/p>\n<\/td>\n | \n 6<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u8f93\u51fa<\/p>\n<\/td>\n | \n 18<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u7f13\u5b58\u547d\u4e2d<\/p>\n<\/td>\n | \n 0.3<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u5373\u65e5\u8d77\u81f3 2026 \u5e74 12 \u6708 31 \u65e5，TokenHub \u6309\u520a\u4f8b\u4ef7 5 \u6298\u8ba1\u8d39；WorkBuddy \/ CodeBuddy \u5728 11 \u6708 4 \u65e5\u81f3 15 \u65e5\u7ed9\u4e00\u5b9a\u514d\u8d39\u989d\u5ea6\u3002\u5b98\u65b9\u540c\u65f6\u9884\u544a\u4e86 Hy4 flash，\u7ee7\u627f Hy4 \u80fd\u529b\u4f46\u5927\u5e45\u538b\u4f4e\u4efb\u52a1\u6210\u672c\u3002<\/p>\n \u4e03\u3001\u4e09\u79cd\u7528\u6cd5：\u4ea7\u54c1\u3001API\u3001\u5f00\u6e90\u6743\u91cd<\/h2>\n\u76f4\u63a5\u7528\u73b0\u6210\u4ea7\u54c1<\/h3>\n\n- \n
\u5143\u5b9d \/ ima：\u65e5\u5e38\u95ee\u7b54\u3001\u6587\u6863\u4e0e\u77e5\u8bc6\u5e93\u573a\u666f，\u80fd\u76f4\u63a5\u5403\u5230\u89c6\u89c9\u80fd\u529b\u3002<\/p>\n<\/li>\n - \n
WorkBuddy \/ CodeBuddy：\u529e\u516c\u5199\u4f5c\u4e0e\u7f16\u7801，\u4eba\u673a\u53cc\u5199\u5728\u6587\u6863\u7f8e\u89c2\u5ea6\u548c\u7a33\u5b9a\u6027\u4e0a\u6709\u660e\u663e\u63d0\u5347\u3002<\/p>\n<\/li>\n - \n
\u4f01\u4e1a\u5fae\u4fe1\u667a\u80fd\u52a9\u7406"\u5927\u5706"：\u4f01\u4e1a\u5185\u90e8\u95ee\u7b54\u4e0e\u6d41\u7a0b\u52a9\u624b\u3002<\/p>\n<\/li>\n - \n
\u817e\u8baf\u4e91 TokenHub：\u9762\u5411\u5f00\u53d1\u7684\u7edf\u4e00\u5165\u53e3\u3002<\/p>\n<\/li>\n - \n
\u7b2c\u4e09\u65b9\u5e73\u53f0：API \u5df2\u63a5\u5165 OpenRouter\u3001Hermes\u3001OpenCode，\u6d77\u5916\u7684\u53ef\u4ee5\u8d70\u8fd9\u4e9b\u3002<\/p>\n<\/li>\n<\/ul>\n \u8c03 API（\u5927\u591a\u6570\u4eba\u7684\u8def\u5f84）<\/h3>\n\u51c6\u5907：\u6ce8\u518c\u817e\u8baf\u4e91\u8d26\u53f7 \u2192 \u5f00\u901a TokenHub \u2192 \u63a7\u5236\u53f0\u62ff API Key\u3002\u57df\u540d\u6309\u533a\u57df\u9009：<\/p>\n \n- \n
\u5e7f\u5dde（\u4e2d\u56fd\u5927\u9646\u8c03\u5ea6）：https:\/\/tokenhub.tencentmaas.com<\/p>\n<\/li>\n - \n
\u65b0\u52a0\u5761（\u5168\u7403\u8c03\u5ea6）：https:\/\/tokenhub-intl.tencentcloudmaas.com<\/p>\n<\/li>\n - \n
\u7845\u8c37（\u7f8e\u56fd）：https:\/\/tokenhub-us.tencentcloudmaas.com<\/p>\n<\/li>\n<\/ul>\n \u6700\u5c0f\u53ef\u7528\u8bf7\u6c42：<\/p>\n curl -X POST 'https:\/\/tokenhub.tencentmaas.com\/v1\/chat\/completions' \\\\ -H 'Authorization: Bearer YOUR_API_KEY' \\\\ -H 'Content-Type: application\/json' \\\\ -d '{ "model": "hy4", "messages": [{"role": "user", "content": "\u628a\u8fd9\u6bb5\u5408\u540c\u91cc\u7684\u4ed8\u6b3e\u8282\u70b9\u6574\u7406\u6210\u8868"}], "temperature": 0.9, "stream": false }' <\/p>\n Python \u76f4\u63a5\u7528\u5b98\u65b9 OpenAI SDK：<\/p>\n from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https:\/\/tokenhub.tencentmaas.com\/v1", ) resp = client.chat.completions.create( model="hy4", messages=[{"role": "user", "content": "\u4f60\u597d，\u7b80\u5355\u4ecb\u7ecd\u4e00\u4e0b\u81ea\u5df1"}], temperature=0.9, ) print(resp.choices[0].message.content)<\/p>\n \u6d41\u5f0f\u8f93\u51fa\u8981\u52a0 stream: true；\u60f3\u62ff\u5230\u5b8c\u6574 usage，\u518d\u52a0 stream_options: {"include_usage": true}，\u5426\u5219\u6700\u540e\u4e00\u4e2a chunk \u91cc\u6ca1\u6709 token \u7edf\u8ba1\u3002<\/p>\n \u6df1\u5ea6\u601d\u8003：<\/p>\n resp = client.chat.completions.create( model="hy4", messages=[{"role": "user", "content": "\u5c0f\u660e\u67095\u4e2a\u82f9\u679c，\u7ed9\u5c0f\u7ea22\u4e2a，\u53c8\u4e703\u4e2a，\u8fd8\u5269\u51e0\u4e2a？"}], extra_body={"thinking": {"type": "enabled"}}, ) msg = resp.choices[0].message print(getattr(msg, "reasoning_content", "")) # \u601d\u7ef4\u94fe print(msg.content) # \u6700\u7ec8\u7b54\u6848<\/p>\n reasoning_content \u4e0d\u662f OpenAI SDK \u58f0\u660e\u7684\u5b57\u6bb5，Python \u7aef\u5fc5\u987b\u7528 getattr \u53d6；Node.js \u7aef SDK \u4f1a\u628a thinking \u5e73\u94fa\u5230\u9876\u5c42，\u76f4\u63a5\u4f20\u5373\u53ef\u3002<\/p>\n \u56fe\u7247\u7406\u89e3\u8d70\u6807\u51c6 OpenAI \u591a\u6a21\u6001\u683c\u5f0f，content \u4f20\u6570\u7ec4：<\/p>\n resp = client.chat.completions.create( model="hy4", messages=[{ "role": "user", "content": [ {"type": "text", "text": "\u8fd9\u5f20\u56fe\u8868\u91cc，2025\u5e74Q4\u7684\u6bdb\u5229\u7387\u662f\u591a\u5c11？\u5e2e\u6211\u6838\u5bf9\u7eb5\u8f74\u662f\u5426\u4ece\u96f6\u5f00\u59cb"}, {"type": "image_url", "image_url": {"url": "https:\/\/example.com\/chart.png"}}, ], }], )<\/p>\n \u5de5\u5177\u8c03\u7528\u8fd9\u91cc\u6709\u4e2a\u5751\u5fc5\u987b\u8bb2\u6e05\u695a：\u591a\u8f6e Agent \u5faa\u73af\u91cc，\u7b2c\u4e00\u8f6e\u8fd4\u56de\u7684 assistant \u6d88\u606f\u8981\u539f\u6837\u56de\u586b，\u5305\u62ec reasoning_content \u548c tool_calls；\u7136\u540e\u8ffd\u52a0\u4e00\u6761 role: "tool"\u3001\u5e26 tool_call_id \u7684\u6d88\u606f\u3002\u5c11\u4e86 reasoning_content，\u6a21\u578b\u7684\u601d\u8003\u94fe\u5c31\u65ad\u4e86，\u4f1a\u51fa\u73b0\u5de5\u5177\u7ed3\u679c\u660e\u660e\u5bf9\u4e86\u4f46\u7ed3\u8bba\u8dd1\u504f\u7684\u60c5\u51b5\u3002\u5b98\u65b9\u6587\u6863\u4e13\u95e8\u5f3a\u8c03\u8fd9\u4e00\u70b9\u3002<\/p>\n from openai import OpenAI client = OpenAI(api_key="YOUR_API_KEY", base_url="https:\/\/tokenhub.tencentmaas.com\/v1") # \u7b2c 1 \u8f6e：\u6a21\u578b\u53ef\u80fd\u9009\u62e9\u8c03\u7528\u5de5\u5177 r1 = client.chat.completions.create(model="hy4", messages=messages, tools=tools, tool_choice="auto", extra_body={"reasoning_effort": "high"}) msg1 = r1.choices[0].message # \u56de\u586b assistant（\u52a1\u5fc5\u4fdd\u7559 reasoning_content + tool_calls） messages.append({ "role": "assistant", "content": msg1.content or "", "reasoning_content": getattr(msg1, "reasoning_content", ""), "tool_calls": [tc.model_dump() for tc in (msg1.tool_calls or [])], }) for tc in (msg1.tool_calls or []): messages.append({"role": "tool", "tool_call_id": tc.id, "content": run_tool(tc.function.name, tc.function.arguments)}) # \u7b2c 2 \u8f6e：\u628a\u5de5\u5177\u7ed3\u679c\u9001\u56de，\u6a21\u578b\u7ee7\u7eed\u63a8\u7406 r2 = client.chat.completions.create(model="hy4", messages=messages, tools=tools)<\/p>\n \u7701\u94b1\u7684\u56db\u4e2a\u5b9e\u64cd\u70b9<\/h3>\n - \n
\u628a\u7a33\u5b9a\u524d\u7f00\u653e\u524d\u9762\u3002\u7cfb\u7edf\u63d0\u793a\u3001few-shot\u3001\u683c\u5f0f\u8bf4\u660e\u8fd9\u4e9b\u6bcf\u6b21\u90fd\u4e0d\u53d8\u7684\u5185\u5bb9\u653e\u6700\u524d，\u80fd\u5403\u5230 0.3 \u5143\/\u767e\u4e07 tokens \u7684\u7f13\u5b58\u547d\u4e2d\u4ef7，\u6bd4 6 \u5143\u4fbf\u5b9c 20 \u500d\u3002<\/p>\n<\/li>\n - \n
\u8f93\u5165\u63a7\u5236\u5728\u591f\u7528\u7684\u957f\u5ea6\u3002960K \u662f\u4e0a\u9650\u4e0d\u662f\u76ee\u6807，\u957f\u4e0a\u4e0b\u6587\u662f\u6309 token \u7ebf\u6027\u8ba1\u8d39\u7684\u3002<\/p>\n<\/li>\n - \n
\u8f93\u51fa\u7528 max_tokens \u5361\u4f4f\u3002\u8f93\u51fa\u5355\u4ef7\u662f\u8f93\u5165\u7684\u4e09\u500d，\u6a21\u578b"\u8bdd\u591a"\u662f\u6700\u8d35\u7684\u6bdb\u75c5\u3002<\/p>\n<\/li>\n - \n
\u80fd\u6d41\u5f0f\u5c31\u6d41\u5f0f，\u914d\u5408\u524d\u7aef\u9010\u5b57\u6e32\u67d3，\u7528\u6237\u7b49 3 \u79d2\u548c\u7b49 8 \u79d2\u7684\u4f53\u9a8c\u5dee\u522b\u5f88\u5927，\u6210\u672c\u4e00\u6837\u3002<\/p>\n<\/li>\n \u4e00\u4e2a\u7c97\u7b97<\/h3>\n\u5047\u8bbe 100 \u4e07\u6b21\u8c03\u7528，\u6bcf\u6b21\u8f93\u5165 2000 tokens（\u5176\u4e2d 1500 \u547d\u4e2d\u7f13\u5b58）\u3001\u8f93\u51fa 300 tokens：<\/p>\n \n- \n
\u7f13\u5b58\u547d\u4e2d：1500 \u00d7 100 \u4e07 = 1500 \u767e\u4e07 tokens \u00d7 0.3 \u5143 = 450 \u5143<\/p>\n<\/li>\n - \n
\u672a\u547d\u4e2d\u8f93\u5165：500 \u00d7 100 \u4e07 = 500 \u767e\u4e07 \u00d7 6 \u5143 = 3000 \u5143<\/p>\n<\/li>\n - \n
\u8f93\u51fa：300 \u00d7 100 \u4e07 = 300 \u767e\u4e07 \u00d7 18 \u5143 = 5400 \u5143<\/p>\n<\/li>\n - \n
\u5408\u8ba1 8850 \u5143，5 \u6298\u671f\u7ea6 4425 \u5143，\u644a\u5230\u6bcf\u6b21\u4e0d\u5230\u534a\u5206\u94b1\u3002<\/p>\n<\/li>\n<\/ul>\n \u540c\u6837\u7684\u91cf\u5982\u679c\u5168\u4e0d\u547d\u4e2d\u7f13\u5b58，\u8f93\u5165\u4fa7\u5c31\u662f 12000 \u5143，\u5dee 7500 \u5143\u3002\u7f13\u5b58\u7b56\u7565\u505a\u4e0d\u505a，\u6210\u672c\u5dee\u4e00\u500d\u3002<\/p>\n \u5f00\u6e90\u6743\u91cd<\/h3>\n\u5b98\u65b9\u8bf4\u4f1a\u5f00\u6e90\u6a21\u578b\u6743\u91cd\u5e76\u53d1\u5e03\u6a21\u578b\u7cfb\u7edf\u5361\u7247\u3002\u79c1\u6709\u5316\u90e8\u7f72\u3001\u6570\u636e\u4e0d\u51fa\u5185\u7f51\u3001\u505a\u4e8c\u6b21\u5fae\u8c03，\u90fd\u7b49\u8fd9\u4e00\u6b65\u843d\u5730\u3002\u4e0d\u8fc7\u8981\u6ce8\u610f，\u6743\u91cd\u5f00\u6e90\u4e0d\u7b49\u4e8e"\u80fd\u8dd1\u8d77\u6765"\u2014\u2014770B \u603b\u53c2\u6570\u5373\u4f7f\u53ea\u6fc0\u6d3b 49B，\u663e\u5b58\u548c\u63a8\u7406\u6846\u67b6\u7684\u8981\u6c42\u4e5f\u4e0d\u4f4e，\u4e2d\u5c0f\u56e2\u961f\u505a\u91cf\u5316\u90e8\u7f72\u524d\u5148\u7b97\u6e05\u695a\u8d26\u3002<\/p>\n \u516b\u3001\u5fc5\u987b\u77e5\u9053\u7684\u8fb9\u754c<\/h2>\n\u80fd\u529b\u8fb9\u754c\u3002\u5b98\u65b9\u81ea\u5df1\u8bf4\u4e86\u4e0e\u9876\u5c16\u95ed\u6e90\u6a21\u578b\u4ecd\u6709\u8f83\u5927\u5dee\u8ddd\u3002\u7ffb\u8bd1\u6210\u4eba\u8bdd：\u590d\u6742\u903b\u8f91\u94fe\u3001\u8d85\u957f\u4efb\u52a1\u7a33\u5b9a\u6027\u3001\u8fb9\u7f18 case \u7684\u5904\u7406，\u522b\u6307\u671b\u5b83\u4e00\u628a\u8fc7\u3002<\/p>\n \u5b89\u5168\u7b56\u7565\u3002\u6df7\u5143\u5728\u8bad\u7ec3\u9636\u6bb5\u5bf9\u7f51\u7edc\u5b89\u5168\u6570\u636e\u505a\u4e86\u88c1\u526a，\u53ea\u7eb3\u5165\u4e0e PoC \u751f\u6210\u3001\u6f0f\u6d1e\u4fee\u590d\u76f8\u5173\u7684\u9632\u5b88\u6027\u6570\u636e，\u5254\u9664\u653b\u51fb\u6027\u8bad\u7ec3\u5185\u5bb9；\u7ed3\u5408\u5185\u90e8\u4e13\u5bb6\u5ba1\u8ba1\u548c\u81ea\u52a8\u8bc4\u6d4b，\u5224\u65ad\u5176\u7f51\u7edc\u8fdb\u653b\u80fd\u529b\u672a\u5c55\u73b0\u7a81\u51fa\u98ce\u9669，\u4e5f\u672a\u53d1\u73b0\u80fd\u663e\u8457\u964d\u4f4e\u5b9e\u65bd\u751f\u7269\u5371\u5bb3\u884c\u4e3a\u7684\u95e8\u69db\u3002\u8fd9\u662f"\u8d1f\u8d23\u4efb\u5f00\u6e90"\u7684\u8bf4\u6cd5，\u4f46\u610f\u5473\u7740\u4f60\u62ff\u5b83\u505a\u6e17\u900f\u6d4b\u8bd5\u7c7b\u5de5\u4f5c\u4f1a\u53d7\u9650\u3002<\/p>\n Computer Use \u8981\u6709\u6c99\u7bb1\u3002\u8ba9\u6a21\u578b\u64cd\u4f5c\u4f60\u7684\u771f\u673a，\u98ce\u9669\u7b49\u540c\u4e8e\u8ba9\u5b9e\u4e60\u751f\u7528\u4f60\u7684\u8d26\u53f7\u3002\u5bb9\u5668\u9694\u79bb\u3001\u6743\u9650\u6700\u5c0f\u5316\u3001\u5173\u952e\u64cd\u4f5c\u4eba\u5de5\u786e\u8ba4，\u8fd9\u4e09\u6837\u4e00\u4e2a\u90fd\u4e0d\u80fd\u7701\u3002<\/p>\n \u7a33\u5b9a\u6027\u6709\u6ce2\u52a8\u3002\u53d1\u5e03\u6b21\u65e5\u8c03\u7528\u91cf\u6fc0\u589e，\u817e\u8baf\u5bf9\u63a8\u7406\u96c6\u7fa4\u505a\u4e86 10 \u500d\u6269\u5bb9\u3002\u65b0\u6a21\u578b\u4e0a\u7ebf\u521d\u671f\u7684\u9ad8\u5cf0\u6296\u52a8\u662f\u5e38\u6001，\u751f\u4ea7\u73af\u5883\u8981\u5907\u597d\u91cd\u8bd5\u548c\u964d\u7ea7（\u53ef\u5148\u9000\u5230 Hy3 \u6216\u540c\u65cf\u5176\u4ed6\u7248\u672c）\u3002<\/p>\n \u89c6\u89c9\u7684\u9690\u6027\u53ef\u9760\u5ea6\u3002\u5c0f\u5b57\u3001\u4f4e\u5bf9\u6bd4\u5ea6\u3001\u624b\u5199\u4f53\u3001\u52a0\u5bc6\u5b57\u4f53，\u4efb\u4f55\u6a21\u578b\u90fd\u4f1a\u7ffb\u8f66\u3002\u6d89\u53ca\u5408\u540c\u91d1\u989d\u3001\u7968\u636e\u7f16\u53f7\u8fd9\u7c7b\u5fc5\u987b\u51c6\u786e\u7684\u5185\u5bb9，OCR \u7ed3\u679c\u8981\u4ea4\u53c9\u6821\u9a8c，\u522b\u628a\u6a21\u578b\u8bfb\u51fa\u6765\u7684\u6570\u5b57\u76f4\u63a5\u843d\u5e93\u3002<\/p>\n \u4e5d\u3001\u9009\u578b\u5efa\u8bae<\/h2>\n\n\n\u9700\u6c42\u573a\u666f<\/p>\n \u63a8\u8350<\/p>\n \u7406\u7531<\/p>\n<\/tr>\n \n\n| \n \u56fe\u6587\u6df7\u6392\u3001\u56fe\u8868\u53d6\u6570\u3001\u6587\u6863\u5ba1\u9605<\/p>\n<\/td>\n | \n Hy4<\/p>\n<\/td>\n | \n \u89c6\u89c9 + \u63a8\u7406 + \u5de5\u5177\u8c03\u7528\u4e00\u4f53，\u5b9a\u4f4d\u7cbe\u5ea6\u662f\u5c11\u89c1\u7684\u5f3a\u9879<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u957f\u6587\u6863\u957f\u62a5\u544a（\u51e0\u5341\u4e07\u5b57）<\/p>\n<\/td>\n | \n Hy4<\/p>\n<\/td>\n | \n 960K \u8f93\u5165\u5c5e\u7b2c\u4e00\u68af\u961f<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u91d1\u878d\u3001\u8d22\u7a0e\u7c7b\u4e13\u4e1a\u5206\u6790<\/p>\n<\/td>\n | \n Hy4<\/p>\n<\/td>\n | \n 213 \u4f4d\u4e13\u5bb6\u3001579 \u4efb\u52a1\u76f2\u6d4b\u5f00\u6e90\u7b2c\u4e00<\/p>\n<\/td>\n<\/tr>\n | \n| \n GUI \u81ea\u52a8\u5316\u3001\u4e13\u4e1a\u8f6f\u4ef6\u64cd\u4f5c<\/p>\n<\/td>\n | \n Hy4<\/p>\n<\/td>\n | \n \u5c11\u6570\u505a\u4e86 Blender \/ CAD \u4e13\u9879\u8bad\u7ec3\u7684\u5f00\u6e90\u6a21\u578b<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u6781\u81f4\u6027\u4ef7\u6bd4\u7684\u6279\u91cf\u4efb\u52a1<\/p>\n<\/td>\n | \n \u7b49 Hy4 flash<\/p>\n<\/td>\n | \n \u5b98\u65b9\u9884\u544a\u5c06\u5927\u5e45\u538b\u4f4e\u5355\u4efb\u52a1\u6210\u672c<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u8ffd\u6c42\u7edd\u5bf9\u667a\u80fd\u4e0a\u9650<\/p>\n<\/td>\n | \n Kimi K3.1 Pro \u7b49<\/p>\n<\/td>\n | \n Artificial Analysis \u4e2d\u56fd\u533a\u5b83\u6392\u7b2c\u4e00<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u5341\u3001\u4e00\u53e5\u8bdd\u6536\u5c3e<\/h2>\nHy4 \u7684\u610f\u4e49\u4e0d\u5728\u4e8e"\u53c8\u4e00\u4e2a\u56fd\u4ea7\u5927\u6a21\u578b"，\u800c\u5728\u4e8e\u5f00\u6e90\u9635\u8425\u7b2c\u4e00\u6b21\u6709\u4e86\u80fd\u770b 28 \u4ebf\u50cf\u7d20\u56fe\u3001\u80fd\u81ea\u5df1\u5728 Blender \u91cc\u5efa\u6a21\u3001\u80fd\u5728 1.5 \u5c0f\u65f6\u91cc\u65e0\u4eba\u5e72\u9884\u5b8c\u6210\u79d1\u7814\u4e09\u7ef4\u91cd\u5efa\u7684\u6a21\u578b，\u800c\u4e14\u4ef7\u683c\u538b\u5728 0.78 \u7f8e\u5143\u4e00\u4e2a\u4efb\u52a1\u3002\u5b83\u79bb\u9876\u5c16\u95ed\u6e90\u8fd8\u6709\u8ddd\u79bb，\u4f46\u90a3\u4e2a\u8ddd\u79bb\u73b0\u5728\u662f\u53ef\u4ee5\u7b97\u6210\u672c\u53bb\u8ffd\u7684\u4e86\u3002<\/p>\n \u5982\u679c\u4f60\u8981\u628a\u5b83\u63a5\u8fdb\u73b0\u6709\u7cfb\u7edf，\u6700\u7701\u4e8b\u7684\u8def\u5f84\u5c31\u662f：\u62ff OpenAI SDK\u3001\u6539 base_url\u3001model \u586b hy4\u3001\u628a\u7a33\u5b9a\u524d\u7f00\u63d0\u5230\u6700\u524d\u9762\u5403\u7f13\u5b58\u4ef7\u3002\u4e09\u6b65，\u534a\u5c0f\u65f6\u80fd\u8dd1\u901a\u7b2c\u4e00\u4e2a\u8bf7\u6c42\u3002<\/p>\n \u8d44\u6599\u6765\u6e90<\/h2>\n\n- \n
\u817e\u8baf\u6df7\u5143\u5b98\u65b9\u53d1\u5e03：Hy4 \u6b63\u5f0f\u53d1\u5e03，\u8dfb\u8eab\u6700\u5f3a\u5f00\u6e90\u6a21\u578b\u4e4b\u5217（2026-11-04）<\/p>\n<\/li>\n - \n
\u817e\u8baf\u4e91\u5f00\u53d1\u8005\u793e\u533a：\u4ec0\u4e48\u662f\u6df7\u5143 Hy4（2026-11-05 \u66f4\u65b0）<\/p>\n<\/li>\n - \n
\u817e\u8baf\u4e91 TokenHub：Hy API Guide（2026-11-04 \u66f4\u65b0）<\/p>\n<\/li>\n - \n
\u817e\u8baf\u4e91 TokenHub：API Usage Instructions（\u57df\u540d\u4e0e\u9274\u6743\u8bf4\u660e）<\/p>\n<\/li>\n - \n
\u5357\u65b9\u90fd\u5e02\u62a5：\u5f15\u5165\u89c6\u89c9\u7406\u89e3\u80fd\u529b，\u817e\u8baf\u65b0\u6a21\u578b\u957f\u51fa\u773c\u775b，\u8c03\u7528\u6fc0\u589e\u6269\u5bb9 10 \u500d（2026-11-05）<\/p>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":" \/* MD \/ \u5bcc\u6587\u672c\u4e2d\u7684 .toc\uff08\u542b\u535a\u5ba2\u56ed\u642c\u5bb6\u7b49\u5d4c\u5957\u7ed3\u6784\uff09\uff1b.toc-box \u5728\u4fa7\u680f\uff0c\u4e0d\u53d7\u5f71\u54cd *\/#content_views .toc,\/* \u7f16\u8f91\u5668\u5e38\u5728\u76ee\u5f55\u524d\u540e\u63d2\u5165\u7a7a p\uff08:empty \u4ecd\u5360 20px\uff09\uff0c\u4e00\u5e76\u53bb\u6389\u907f\u514d\u9876\u7a7a\u9699 *\/#content_views.markdown_views > p:empty:has(+ .toc),#content_views.markdown_views > .toc + p:empty,\/* \u5bcc\u6587\u672c\u65e7\u7248\u76ee\u5f55\u6807\u8bb0 *\/#content<\/p>\n","protected":false},"author":2,"featured_media":123448,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[14000,14143,66,227],"topic":[],"class_list":["post-123449","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-hy4","tag-14143","tag-ai","tag-227"],"yoast_head":"\n \u817e\u8baf\u6df7\u5143 Hy4\uff1a\u4ece\u201c\u4f1a\u5199\u201c\u5230\u201c\u4f1a\u770b\u3001\u4f1a\u52a8\u624b\u201c\u7684\u5f00\u6e90\u65d7\u8230 - 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