\n| \n GPT-5.2 Pro<\/p>\n<\/td>\n | \n 77.8%<\/p>\n<\/td>\n | \n $189.00<\/p>\n<\/td>\n | \n 286\u00d7<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n （\u5feb\u7167\u65e5\u671f 2026-09-15，\u699c\u5355\u4e2d 38 \u4e2a\u6a21\u578b\u6709\u516c\u5f00\u5206\u6570\u3002）<\/p>\n \u4e09\u4e2a\u8bfb\u6cd5：<\/p>\n \u9876\u7aef\u51e0\u4e2a\u70b9\u8981\u4ed8 90 \u500d\u3002 Claude Fable 5 \u62ff\u5230 83.3%\u3001\u82b1 90.9\u00d7；Hy3 \u62ff\u5230 79.1%\u3001\u82b1 1\u00d7\u3002\u5dee\u989d 4.2 \u4e2a\u767e\u5206\u70b9，\u4ee3\u4ef7 90 \u500d\u3002\u5bf9\u7edd\u5927\u591a\u6570\u5185\u90e8 Agent，\u8fd9 4.2 \u4e2a\u70b9\u4e70\u4e0d\u5230\u5bf9\u5e94\u7684\u4e1a\u52a1\u4ef7\u503c，\u6362\u4e00\u6863\u5c31\u80fd\u628a\u9884\u7b97\u91ca\u653e\u5230\u522b\u5904\u3002<\/p>\n \u4fbf\u5b9c\u4e0d\u7b49\u4e8e\u53ef\u9760，\u8fd9\u662f\u6700\u5bb9\u6613\u8e29\u7684\u3002 Nova 2 Lite \u662f Hy3 \u7684 4.2 \u500d\u4ef7\u94b1，\u5206\u6570\u53ea\u6709\u4eba\u5bb6\u7684\u4e09\u5206\u4e4b\u4e00，24.6% \u610f\u5473\u7740\u56db\u6b21\u4efb\u52a1\u91cc\u4e09\u6b21\u5de5\u5177\u94fe\u8d70\u4e0d\u901a\u3002\u5982\u679c\u53ea\u6309\u201c\u4fbf\u5b9c\u5c31\u4e0a\u201d，\u4f60\u5f88\u53ef\u80fd\u6b63\u597d\u6311\u4e2d\u5b83\u3002<\/p>\n \u6700\u8d35\u7684\u4e5f\u6ca1\u6709\u6700\u53ef\u9760\u3002 GPT-5.2 Pro \u8981\u4ef7 189 \u7f8e\u5143，\u5206\u6570 77.8%，\u6392\u5728 Hy3 \u540e\u9762\u3002\u4ef7\u683c\u53cd\u6620\u8bad\u7ec3\u6210\u672c\u548c\u54c1\u724c\u5b9a\u4f4d，\u4e0d\u662f\u5de5\u5177\u8c03\u7528\u7684\u7a33\u5b9a\u6027\u3002<\/p>\n \u7ed3\u8bba\u5f88\u76f4\u63a5：\u9009\u6a21\u578b\u8981\u6309\u201c\u5b9e\u6d4b\u7684\u5de5\u5177\u8c03\u7528\u901a\u8fc7\u7387\u201d\u5206\u6863，\u800c\u4e0d\u662f\u6309\u5355\u4ef7\u6216\u53c2\u6570\u89c4\u6a21\u3002\u524d\u63d0\u662f，\u4f60\u7684\u5907\u9009\u96c6\u5f97\u8db3\u591f\u5bbd\u3002<\/p>\n \u56db\u3001\u8def\u7531\u5c42\u5fc5\u987b\u6539\u7684\u4e09\u4ef6\u4e8b<\/h3>\n\u7b2c\u4e00，\u628a\u5de5\u5177\u7684\u201c\u5b58\u6d3b\u201d\u5f53\u4f5c\u53ef\u89c2\u6d4b\u6307\u6807\u3002 \u522b\u7b49\u6a21\u578b\u62a5\u9519\u624d\u77e5\u9053\u5de5\u5177\u6302\u4e86\u3002\u5b9a\u671f\u5bf9\u6bcf\u4e2a MCP \u670d\u52a1\u5668\u505a\u63e1\u624b\u63a2\u6d4b，\u628a 48.8% \u90a3\u4e2a\u6982\u7387\u6321\u5728\u5e94\u7528\u4e4b\u5916：\u8d77\u4e0d\u6765\u7684\u76f4\u63a5\u6458\u51fa\u53ef\u7528\u6c60，\u522b\u8ba9\u6a21\u578b\u53bb\u731c\u4e3a\u4ec0\u4e48\u8c03\u7528\u5931\u8d25\u3002<\/p>\n \u7b2c\u4e8c，\u65e5\u5fd7\u8981\u8bb0\u5b9e\u9645\u6267\u884c\u7684\u6a21\u578b\u540d\u548c\u5de5\u5177\u8c03\u7528\u6b21\u6570\u3002 \u4e00\u6b21\u4efb\u52a1\u8d70\u4e86 3 \u6b65\u8fd8\u662f 6 \u6b65，\u76f4\u63a5\u51b3\u5b9a\u6210\u672c\u3002\u5de5\u5177\u8c03\u7528\u6b21\u6570\u662f\u6210\u672c\u7684\u4e00\u7b49\u53d8\u91cf，\u6bd4 token \u5355\u4ef7\u66f4\u80fd\u89e3\u91ca\u8d26\u5355\u3002<\/p>\n \u7b2c\u4e09，\u91cd\u8bd5\u9884\u7b97\u8981\u663e\u5f0f\u5199\u8fdb\u7f16\u6392\u3002 \u5de5\u5177\u94fe\u672c\u8eab\u6709\u957f\u5c3e\u6296\u52a8，\u4efb\u4f55\u201c\u4e00\u6b21\u8c03\u7528\u6210\u529f\u201d\u7684\u5047\u8bbe\u90fd\u662f\u4e50\u89c2\u7684\u3002\u7ed9\u6bcf\u4e2a\u4efb\u52a1\u7559\u51fa\u7ea0\u9519\u989d\u5ea6，\u5e76\u5728\u8fbe\u5230\u4e0a\u9650\u65f6\u964d\u7ea7\u5230\u4e0b\u4e00\u6863\u6a21\u578b，\u800c\u4e0d\u662f\u4e00\u76f4\u91cd\u8bd5\u540c\u4e00\u6863\u3002<\/p>\n import os
\nfrom openai import OpenAI<\/p>\n client = OpenAI(
\n base_url="https:\/\/router.accels.tech\/v1",
\n api_key=os.environ["ACCELS_API_KEY"],
\n)<\/p>\n # 1. \u5148\u6478\u6e05\u5907\u9009\u96c6\u6709\u591a\u5bbd\u2014\u2014\u6309\u53ef\u9760\u6027\u9009\u8def\u7684\u524d\u63d0\u662f\u578b\u53f7\u591f\u591a
\nroster = [m.id for m in client.models.list()]
\nassert {"hy3", "seed-2-1-pro", "claude-opus-5"} <= set(roster)<\/p>\n # 2. \u6309\u5b9e\u6d4b\u7684\u5de5\u5177\u8c03\u7528\u901a\u8fc7\u7387\u5206\u6863，\u800c\u4e0d\u662f\u6309\u5355\u4ef7\u6216\u53c2\u6570\u89c4\u6a21
\nTIER = {
\n "hy3": 0.791, # \u5355\u670d\u52a1\u5668\u3001\u5c11\u6b65\u6570\u4efb\u52a1\u591f\u7528
\n "seed-2-1-pro": 0.838, # \u9ed8\u8ba4\u6863
\n "claude-opus-5": 0.858, # \u8de8\u670d\u52a1\u5668\u591a\u6b65\u7f16\u6392
\n}<\/p>\n def pick(steps: int) -> str:
\n floor = 0.85 if steps >= 4 else 0.78
\n ok = [(m, s) for m, s in TIER.items() if s >= floor]
\n if not ok:
\n raise RuntimeError("\u6ca1\u6709\u578b\u53f7\u6ee1\u8db3\u8be5\u4efb\u52a1\u7684\u5de5\u5177\u8c03\u7528\u6210\u529f\u7387\u4e0b\u9650")
\n return min(ok, key=lambda kv: kv[1])[0] # \u8fbe\u6807\u91cc\u6311\u6700\u4fbf\u5b9c\u7684<\/p>\n # 3. \u628a\u5931\u8d25\u5f53\u5e38\u6001：\u91cd\u8bd5\u4e0e\u964d\u7ea7\u5199\u5728\u8c03\u7528\u91cc，\u4e0d\u5199\u5728\u7948\u7977\u91cc
\ndef call_with_tools(prompt: str, tools: list, steps: int) -> str:
\n model = pick(steps)
\n messages = [{"role": "user", "content": prompt}]
\n for _ in range(steps + 2): # \u9884\u7559\u4e24\u6b21\u7ea0\u9519\u989d\u5ea6
\n resp = client.chat.completions.create(
\n model=model, messages=messages, tools=tools, tool_choice="auto",
\n )
\n msg = resp.choices[0].message
\n if not msg.tool_calls:
\n return msg.content or ""
\n messages.append(msg)
\n for tc in msg.tool_calls:
\n messages.append({
\n "role": "tool",
\n "tool_call_id": tc.id,
\n "content": dispatch(tc.function.name, tc.function.arguments),
\n })
\n return ""<\/p>\n \u843d\u5730：\u4e00\u4e2a\u7edf\u4e00\u7684\u6a21\u578b\u5165\u53e3\u8ba9\u4e0a\u9762\u4e09\u4ef6\u4e8b\u53d8\u5f97\u53ef\u884c<\/h3>\n\u8fd9\u4e09\u4ef6\u4e8b\u5355\u72ec\u505a\u90fd\u4e0d\u96be，\u96be\u5728\u5b83\u4eec\u8981\u6c42\u4f60\u540c\u65f6\u63e1\u4f4f\u4e09\u6837\u4e1c\u897f：\u8db3\u591f\u5bbd\u7684\u578b\u53f7\u6c60\u3001\u7a33\u5b9a\u7684\u8c03\u7528\u901a\u9053\u3001\u80fd\u5bf9\u9f50\u7684\u8d26\u5355\u3002<\/p>\n \u578b\u53f7\u6c60\u4e0d\u591f\u5bbd，\u7b2c\u4e00\u4ef6\u4e8b\u5c31\u505a\u4e0d\u6210\u3002\u80fd\u8fbe\u6807\u7684\u6863\u4f4d\u5206\u6563\u5728\u81f3\u5c11\u56db\u5bb6\u5382\u5546\u4e4b\u95f4：Hy3 \u5728 0.66 \u7f8e\u5143\u7684\u4ef7\u4f4d\u62ff\u5230 79.1%，Seed 2.1 Pro \u7528 5.34 \u7f8e\u5143\u62ff\u5230 83.8%，Claude Opus 5 \u7528 30 \u7f8e\u5143\u6362\u5230\u9876\u90e8\u90a3 2 \u4e2a\u70b9\u3002\u53ea\u5728\u5355\u4e00\u5382\u5546\u5185\u90e8\u9009，\u7b49\u4e8e\u628a\u201c\u6027\u4ef7\u6bd4\u201d\u76f4\u63a5\u780d\u6389\u3002<\/p>\n \u8c03\u7528\u901a\u9053\u4e0d\u7a33，\u7b2c\u4e8c\u4ef6\u4e8b\u5c31\u5168\u662f\u566a\u58f0\u3002MCP \u751f\u6001\u7684\u957f\u5c3e\u53ea\u6709\u4e00\u534a\u662f\u6d3b\u7684；\u5982\u679c\u8fde\u6a21\u578b\u5165\u53e3\u672c\u8eab\u90fd\u5728\u6296\u52a8，\u4f60\u5206\u4e0d\u6e05\u5931\u8d25\u662f\u6a21\u578b\u9009\u9519\u4e86\u5de5\u5177，\u8fd8\u662f\u94fe\u8def\u65ad\u4e86\u3002\u7edf\u4e00\u5165\u53e3\u7684\u4ef7\u503c\u5728\u4e8e\u628a\u8fd9\u4e00\u5c42\u56fa\u5b9a\u4e0b\u6765：\u4e00\u6b21\u63a5\u5165\u3001\u957f\u671f\u53ef\u7528，\u4e0d\u5fc5\u4e3a\u6bcf\u4e2a\u5382\u5546\u5355\u72ec\u7ef4\u62a4\u9274\u6743\u3001\u91cd\u8bd5\u548c\u9650\u6d41\u3002<\/p>\n \u8d26\u5355\u4e0d\u80fd\u5bf9\u9f50，\u7b2c\u4e09\u4ef6\u4e8b\u5c31\u6c38\u8fdc\u7b97\u4e0d\u6e05\u3002\u8de8\u670d\u52a1\u5668\u591a\u6b65\u4efb\u52a1\u7684\u771f\u5b9e\u6210\u672c\u662f\u201c\u8c03\u7528\u6b21\u6570 \u00d7 \u6bcf\u6b65\u4e0a\u4e0b\u6587\u4f53\u91cf \u00d7 \u8be5\u578b\u53f7\u5355\u4ef7\u201d，\u4e09\u4e2a\u56e0\u5b50\u6563\u5728\u4e0d\u540c\u5382\u5546\u7684\u8d26\u5355\u91cc，\u5f52\u56e0\u65e0\u4ece\u8c08\u8d77\u3002\u7edf\u4e00\u8ba1\u8d39\u628a\u8fd9\u4e2a\u4e58\u6cd5\u6536\u655b\u5230\u4e00\u5f20\u8868\u4e0a，\u4f60\u624d\u53ef\u80fd\u53d1\u73b0\u201c\u6362\u4e2a\u4fbf\u5b9c\u6863\u4f4d\u6210\u672c\u780d\u534a\u3001\u6210\u529f\u7387\u4ecd\u6709 96%\u201d\u8fd9\u79cd\u7ed3\u8bba\u3002<\/p>\n router.accels.tech \u5c31\u662f\u7167\u8fd9\u4e2a\u601d\u8def\u505a\u7684：Accels \u662f\u4e00\u5bb6\u65b0\u52a0\u5761\u7684\u516c\u53f8，\u63a5\u53e3\u662f\u6807\u51c6 OpenAI \u517c\u5bb9\u683c\u5f0f，base_url \u6362\u6210 https:\/\/router.accels.tech\/v1 \u5c31\u80fd\u7528，\u6a21\u578b\u6c60\u8986\u76d6\u4e3b\u6d41\u5382\u5546\u7684\u5b8c\u6574\u6863\u4f4d，\u5355\u4e00\u5bc6\u94a5\u3001\u5355\u4e00\u8d26\u5355\u3002\u4e0a\u9762\u4ee3\u7801\u91cc\u7684 client.models.list() \u80fd\u5217\u51fa\u4f60\u53ef\u7528\u7684\u5168\u90e8\u578b\u53f7\u2014\u2014\u6309\u53ef\u9760\u6027\u5206\u6863\u9009\u8def，\u7b2c\u4e00\u6b65\u5c31\u662f\u628a\u5907\u9009\u96c6\u94fa\u5f00\u3002<\/p>\n \u7ed3\u8bed<\/h3>\n\u4eca\u5929\u8fd9\u4efd\u6570\u636e\u7684\u610f\u4e49\u4e0d\u5728\u4e8e\u201c\u67d0\u5bb6\u6a21\u578b\u53c8\u8d62\u4e86\u51e0\u4e2a\u70b9\u201d\u3002\u5b83\u628a\u4e00\u4ef6\u957f\u671f\u88ab\u542b\u7cca\u5904\u7406\u7684\u4e8b\u62ac\u5230\u4e86\u53f0\u9762\u4e0a：\u5de5\u5177\u8c03\u7528\u53ef\u9760\u6027\u662f\u4e00\u4e2a\u53ef\u4ee5\u6d4b\u91cf\u3001\u53ef\u4ee5\u6392\u5e8f\u3001\u53ef\u4ee5\u5728\u8def\u7531\u5c42\u51b3\u7b56\u7684\u5de5\u7a0b\u6307\u6807，\u5b83\u548c\u63a8\u7406\u80fd\u529b\u4e0d\u662f\u4e00\u56de\u4e8b，\u548c\u4ef7\u683c\u66f4\u4e0d\u662f\u4e00\u56de\u4e8b\u3002<\/p>\n \u4e0b\u6b21\u6709\u4eba\u62ff\u4e00\u4e2a MCP-Atlas \u5206\u6570\u6765\u5bf9\u6bd4\u6a21\u578b，\u95ee\u6e05\u53e3\u5f84；\u4e0b\u6b21\u8981\u4e0a\u591a\u6b65 Agent，\u5148\u6570\u6e05\u4e00\u6b21\u4efb\u52a1\u5230\u5e95\u8981\u51e0\u6b21\u5de5\u5177\u8c03\u7528\u3002\u628a\u8fd9\u4e24\u4ef6\u4e8b\u505a\u624e\u5b9e，\u6bd4\u8ffd\u4e00\u4e2a\u65b0\u53d1\u5e03\u7684\u6a21\u578b\u6709\u7528\u5f97\u591a\u3002<\/p>\n
\n\u6765\u6e90<\/h3>\n\n- \n
MCP-Atlas \u8bba\u6587（Scale Labs）：https:\/\/labs.scale.com\/papers<\/p>\n<\/li>\n - \n
MCP-Atlas \u6392\u884c\u699c\u4e0e\u5b9e\u65f6\u4ef7\u683c\u5feb\u7167（2026-09-15）：https:\/\/anotheraiwrapper.com\/tools\/llm-pricing\/evals\/mcp-atlas<\/p>\n<\/li>\n - \n
MCP-Atlas \u4e24\u4e2a\u5206\u6570\u53e3\u5f84\u7684\u62c6\u89e3：https:\/\/ai-agent-engineering.org\/news\/mcp-atlas-says-445-and-822-ask-which-number<\/p>\n<\/li>\n - \n
MCP Registry \u968f\u673a\u62bd\u6837\u63a2\u6d4b\u62a5\u544a（arXiv:2609.10962v1）：https:\/\/blog.pebblous.ai\/report\/mcp-registry-random-sample-probe\/en<\/p>\n<\/li>\n - \n
AI Daily 2026-09-15：https:\/\/quidproquo.cc\/posts\/daily\/2026-09-15-ai-agent-daily-en<\/p>\n<\/li>\n - \n
AGI HUNT \u65e5\u62a5 2026-09-15：https:\/\/agihunt.info\/en\/daily\/2026-09-15<\/p>\n<\/li>\n - \n
DataLearnerAI MCP-Atlas \u699c\u5355：https:\/\/www.datalearner.com\/benchmarks\/mcp-atlas<\/p>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":" \u5f15\u5b50：\u4eca\u5929\u6ca1\u6709\u65b0\u6a21\u578b，\u53ea\u6709\u4e00\u4efd\u96be\u770b\u7684\u5206\u65709 \u6708 15 \u65e5\u6ca1\u6709\u65d7\u8230\u6a21\u578b\u53d1\u5e03\u3002\u771f\u6b63\u8be5\u5148\u770b\u7684，\u662f\u4e24\u4ef6\u53e0\u5728\u4e00\u8d77\u7684\u4e8b：Scale AI \u628a MCP-Atlas \u5f00\u653e\u6210\u53ef\u590d\u73b0\u7684\u5de5\u5177\u8c03\u7528\u57fa\u51c6；\u540c\u4e00\u5929，\u4e00\u4efd\u72ec\u7acb\u7814\u7a76\u4ece MCP Registry \u91cc\u968f\u673a\u62bd\u4e86 400 \u4e2a\u670d\u52a1\u5668，\u6328\u4e2a\u542f\u52a8\u4e00\u6b21，\u4e0d\u505a\u4efb\u4f55\u4fee\u590d\u3002195 \u4e2a\u8d77\u6765\u4e86\u3002\u4e00\u534a\u591a\u4e00\u70b9\u3002\u800c\u5982\u679c\u53ea\u6d4b\u4f5c\u8005\u624b\u5de5\u6311\u7684\u90a3 24 \u4e2a，\u8fd9<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50],"topic":[],"class_list":["post-110753","post","type-post","status-publish","format-standard","hentry","category-server","tag-50"],"yoast_head":"\n \u53ea\u6709 48.8% \u7684\u5de5\u5177\u670d\u52a1\u5668\u80fd\u8d77\u6765\uff1aMCP-Atlas \u628a\u201c\u5de5\u5177\u8c03\u7528\u53ef\u9760\u6027\u201d\u63a8\u5230\u4e86\u8def\u7531\u5c42 - 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