Reflection: Beam 1.0 501B A23B

{"about":"### Reflection 的 Beam 1.0\n\nBeam 1.0 501B A23B 是 Reflection 用於程式設計、推理及多個相關步驟任務的文字模型。其稀疏 MoE 架構總計含 5013 億參數，每個 token 啟用約 234 億參數。模型主要針對英語開發，選擇原始碼、技術資料及詳細指示的語言時，可將此點納入考量。\n\n架構結合局部與全域注意力，模型訓練包含原始碼、技術文件、科學及教學資料。另有訓練階段透過結構化儲存庫、長任務及大型文件培養長上下文能力。強化學習時，開發商結合了程式設計、終端機操作及 STEM 任務。\n\n### 模型著重哪些工作\n\nReflection 強調軟體工程、數學與科學推理，以及連續解題。這些方向可用於有內容的文字對話：解釋程式元件間的關係、分析邏輯錯誤、提出演算法修改、規劃假設驗證，或整理技術資料摘要。\n\nBeam 也特別著重節省推理 token。訓練會獎勵在減少多餘 token 的情況下成功解題。這描述的是開發商的方法；具體請求仍宜明確指定答案長度，例如簡短結論、詳細證明，或附說明的修補內容。\n\n### Ailoot 的大型文字上下文\n\n此連線的上下文視窗為 1,048,576 個 token，包含提供資料、指示、歷史與生成中的答案。模型中繼資料標示的答案容量最多為 131,072 個 token。這個容量可傳入數份相關文字：規格、實作片段、驗證結果與相關問題。請為資料命名，並要求標示每項結論依據哪個片段，方便核對大型分析。\n\n例如，程式碼工作可先要求依傳入檔案建立相依關係圖，再指出修改位置，最後準備實作。科學問題則可提供初始條件、符號定義及所需嚴謹程度。這些請求安排能讓實用結果與原始資料逐一比對。\n\n此連線透過 Messages、Responses、Chat Completions 及文字 generateContent 接受文字並傳回文字。程式碼與建議以模型答案形式提供；命令執行及外部資料處理由應用程式能力決定。","alias_of":null,"aliases":[],"author":"Reflection","canonical_model":"reflection/beam-1.0-501b-a23b","ctx":null,"formats":["chat","gemini","messages","responses"],"id":"reflection/beam-1.0-501b-a23b","input":["text"],"kind":"text","max_out":null,"methods":[{"cache_read":0.1,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"anthropic","fast_multiplier":1,"flex_multiplier":1,"fmt":"messages","http":"POST","img_in":null,"img_out":null,"in":0.5,"input":["text"],"label":"Anthropic Messages","max_out":null,"max_tool_calls":null,"out":2.0,"output":["text"],"path":"/v1/messages","request_model_id":"reflection/beam-1.0-501b-a23b","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.1,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"responses","http":"POST","img_in":null,"img_out":null,"in":0.5,"input":["text"],"label":"OpenAI Responses","max_out":null,"max_tool_calls":null,"out":2.0,"output":["text"],"path":"/v1/responses","request_model_id":"reflection/beam-1.0-501b-a23b","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.1,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"chat","http":"POST","img_in":null,"img_out":null,"in":0.5,"input":["text"],"label":"OpenAI Chat","max_out":null,"max_tool_calls":null,"out":2.0,"output":["text"],"path":"/v1/chat/completions","request_model_id":"reflection/beam-1.0-501b-a23b","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.1,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"gemini","fast_multiplier":1,"flex_multiplier":1,"fmt":"gemini","http":"POST","img_in":null,"img_out":null,"in":0.5,"input":["text"],"label":"Gemini generateContent","max_out":null,"max_tool_calls":null,"out":2.0,"output":["text"],"path":"/v1beta/models/reflection/beam-1.0-501b-a23b:generateContent","request_model_id":"reflection/beam-1.0-501b-a23b","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]}],"model_aliases":[{"canonical_model_id":"reflection/beam-1.0-501b-a23b","request_model_id":"reflection/beam-1.0-501b-a23b","route_label":"Ultra"}],"name":"Reflection: Beam 1.0 501B A23B","price":{"in":0.5,"in_label":null,"out":2.0,"out_label":null,"sort_in":0.5,"sort_out":2.0,"varies":false},"public_id":"reflection/beam-1.0-501b-a23b","short":"Reflection 的大型上下文文字 MoE 模型，用於軟體工程、數學推理與逐步分析問題。"}