OpenAI: GPT-6.1 Terra Pro

{"about":"### テキスト資料の処理\n\nAiloot APIのGPT-6.1 Terra Proは、テキスト要求を受け取り、テキスト回答を返します。文書の準備、元資料の分析、対話を通じた結果の継続的な調整に組み込めます。一つの要求や会話のメッセージに、課題の内容、必要な情報、期待する回答形式を渡してください。\n\n### 文書の準備\n\nまず、必要な文書、対象読者、文書が支援する行動という目的を示します。要点、資料の抜粋、下書きを添え、導入、主な結論、論拠、次の手順などの構成を指定します。編集では長さ、文体、用語、説明順序など、変える点を具体化します。「文章をよくして」という曖昧な依頼ではなく、検証できる成果を定める方法です。\n\n### 分析と比較\n\n複数資料の分析では名前で区切り、比較基準を示します。各文章の短い要約、共通の質問一覧、項目ごとの要件比較を求められます。正確な根拠との対応が重要なら、結論の横に文書名と該当箇所を示すよう求めます。項目一覧と記入例を添えると、後の手作業で扱いやすい形式になります。\n\n### 接続と分量\n\nAiloot APIでは、このモデルカードにMessages、Responses、Chat Completions、Gemini互換のgenerateContentのテキストインターフェースが設定されています。クライアントが対応するメッセージ形式を使用します。文書などの元資料はテキストで渡し、結果も保存、編集、次の工程への引き渡しが可能なテキストです。\n\n大きな課題では、元資料、指示、期待する回答の間で分量を配分します。短い結論が必要なら長さを要求に明示し、網羅性を重視するなら必須の節と確認基準を列挙します。\n\n### 要求の順序\n\n1. 最終課題を説明し、元の文章を添えます。\n2. 制約と期待する構成を指定します。\n3. 回答を元資料と照合します。\n4. 全体の目的と要件を保ちながら、個別の節を調整します。","alias_of":null,"aliases":[],"author":"OpenAI","canonical_model":"openai/gpt-6.1-terra-pro","ctx":null,"formats":["chat","gemini","messages","responses"],"id":"openai/gpt-6.1-terra-pro","input":["text"],"kind":"text","max_out":null,"methods":[{"cache_read":0.1,"cache_write":2.5,"cache_write_1h":2.5,"cache_write_5m":2.5,"conditional_meter":null,"ctx":null,"family":"anthropic","fast_multiplier":1,"flex_multiplier":1,"fmt":"messages","http":"POST","img_in":null,"img_out":null,"in":2.0,"input":["text"],"label":"Anthropic 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Responses","max_out":null,"max_tool_calls":null,"out":10.0,"output":["text"],"path":"/v1/responses","request_model_id":"openai/gpt-6.1-terra-pro","route_label":"Ultra","status_reads":[],"tiers":[{"cache_read":0.1,"cache_write":2.5,"cache_write_1h":2.5,"cache_write_5m":2.5,"in":2.0,"interval_boundary":"min_exclusive_max_inclusive","label":"<=272K","max":272000,"min":0,"null_rate_policy":"inherit_base_rate","out":10.0},{"cache_read":0.2,"cache_write":5.0,"cache_write_1h":5.0,"cache_write_5m":5.0,"in":4.0,"interval_boundary":"min_exclusive_max_inclusive","label":">272K","max":null,"min":272000,"null_rate_policy":"inherit_base_rate","out":15.0}],"tool_prices":{"web_search_call":0.02},"unit":"token","units":[]},{"cache_read":0.1,"cache_write":2.5,"cache_write_1h":2.5,"cache_write_5m":2.5,"conditional_meter":null,"ctx":null,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"chat","http":"POST","img_in":null,"img_out":null,"in":2.0,"input":["text"],"label":"OpenAI Chat","max_out":null,"max_tool_calls":null,"out":10.0,"output":["text"],"path":"/v1/chat/completions","request_model_id":"openai/gpt-6.1-terra-pro","route_label":"Ultra","status_reads":[],"tiers":[{"cache_read":0.1,"cache_write":2.5,"cache_write_1h":2.5,"cache_write_5m":2.5,"in":2.0,"interval_boundary":"min_exclusive_max_inclusive","label":"<=272K","max":272000,"min":0,"null_rate_policy":"inherit_base_rate","out":10.0},{"cache_read":0.2,"cache_write":5.0,"cache_write_1h":5.0,"cache_write_5m":5.0,"in":4.0,"interval_boundary":"min_exclusive_max_inclusive","label":">272K","max":null,"min":272000,"null_rate_policy":"inherit_base_rate","out":15.0}],"tool_prices":{"web_search_call":0.02},"unit":"token","units":[]},{"cache_read":0.1,"cache_write":2.5,"cache_write_1h":2.5,"cache_write_5m":2.5,"conditional_meter":null,"ctx":null,"family":"gemini","fast_multiplier":1,"flex_multiplier":1,"fmt":"gemini","http":"POST","img_in":null,"img_out":null,"in":2.0,"input":["text"],"label":"Gemini generateContent","max_out":null,"max_tool_calls":null,"out":10.0,"output":["text"],"path":"/v1beta/models/openai/gpt-6.1-terra-pro:generateContent","request_model_id":"openai/gpt-6.1-terra-pro","route_label":"Ultra","status_reads":[],"tiers":[{"cache_read":0.1,"cache_write":2.5,"cache_write_1h":2.5,"cache_write_5m":2.5,"in":2.0,"interval_boundary":"min_exclusive_max_inclusive","label":"<=272K","max":272000,"min":0,"null_rate_policy":"inherit_base_rate","out":10.0},{"cache_read":0.2,"cache_write":5.0,"cache_write_1h":5.0,"cache_write_5m":5.0,"in":4.0,"interval_boundary":"min_exclusive_max_inclusive","label":">272K","max":null,"min":272000,"null_rate_policy":"inherit_base_rate","out":15.0}],"tool_prices":{"web_search_call":0.02},"unit":"token","units":[]}],"model_aliases":[{"canonical_model_id":"openai/gpt-6.1-terra-pro","request_model_id":"openai/gpt-6.1-terra-pro","route_label":"Ultra"}],"name":"OpenAI: GPT-6.1 Terra Pro","price":{"in":2.0,"in_label":null,"out":10.0,"out_label":null,"sort_in":2.0,"sort_out":10.0,"varies":false},"public_id":"openai/gpt-6.1-terra-pro","short":"文書処理、資料の比較、構造化された回答の準備に向けたGPT-6.1 Terra Proのテキストモデルカードです。"}