古尚集團是一家以「AI業務化改造 + 國際品牌落地」為雙核心驅動的精品商業諮詢機構，為中小企業、初創團隊及個人品牌提供從戰略到落地的一站式服務。
Gushang Group is a boutique consultancy driven by two core engines — AI business transformation and international brand localisation — delivering end-to-end services for SMEs, startups and personal brands.
戰略層、執行層、流量層深度協同，為客戶提供「技術 + 商業」的複合價值。Strategy, execution and audience layers working in synergy to deliver combined technology and business value.
從AI就緒度診斷到業務系統設計、流程改造及持續運維的全週期服務，讓AI真正融入業務流程。
Full-cycle services from AI readiness assessment to system design, process re-engineering and ongoing operations.
了解詳情 →Learn more →輕量化、模組化的AI數位轉型方案，涵蓋個人品牌策略、社交媒體運營、官網建設與技術支持。
Lightweight, modular AI solutions covering personal branding, social media, website building and IT support.
了解詳情 →Learn more →品牌戰略、市場進入、商業模式設計、運營診斷與IP商業化，為客戶釐清方向、設計路徑。
Brand strategy, market entry, business model design, operational diagnosis and IP monetisation.
了解詳情 →Learn more →模型迭代以月為單位。我們持續追蹤全球頂尖實驗室的最新進展，並依據您的業務場景、預算與合規要求，在「能力、成本、可控性」三者之間取得最佳平衡。Frontier models now iterate monthly. We track the world's leading labs continuously, balancing capability, cost and controllability against your business scenario, budget and compliance needs.
OpenAI 新一代旗艦，官方稱迄今最智能、對齊最充分的模型：原生操作電腦與工具、直接交付辦公產出，並成為首個達到「關鍵級」網絡安全能力閾值、在更強防護機制下發布的 OpenAI 模型；Astra Pro 提供更高推理強度檔。
OpenAI's new flagship — its smartest, most aligned model yet, operating computers and tools natively to deliver finished work, and the first OpenAI model to cross the "critical" cybersecurity capability threshold, released under stronger safeguards. Astra Pro adds a higher-effort reasoning tier.
2026 年 9 月初發布的新旗艦，面世時被普遍評價為公開可用模型中最強，主攻高難度推理與長程智能體工作；同步推出編碼與知識工作特化的 Mythos 5.1，Opus 5 則以更高性價比承接近前沿場景。
The new flagship released in early September 2026, widely assessed as the strongest publicly available model at launch, built for demanding reasoning and long-horizon agentic work. Coding-focused Mythos 5.1 shipped alongside it, with Opus 5 covering near-frontier needs cost-effectively.
六週內推出的第三款 Flash，定位「最智能 Flash」：登頂 DeepSWE 軟件工程榜、Terminal-Bench 2.1 達 90.8%，並同場發布網絡安全特化版 3.8 Flash Cyber，兼具高性價比與長程軟件工程能力。
The third Flash in six weeks and billed as the smartest yet: it tops the DeepSWE leaderboard, scores 90.8% on Terminal-Bench 2.1, and shipped with a cybersecurity-specialised 3.8 Flash Cyber — pairing cost-efficiency with long-horizon software engineering.
2026 年 8 月 12 日發布的前沿模型，專注長時間運行的智能體任務與編程場景，已上線 GitHub Copilot 等主流企業開發平台；上下文最高 50 萬 token，逾 20 萬後價格階梯上調，適合深度代碼工程與多步驟自動化。
A frontier model released 12 Aug 2026, focused on long-running agentic tasks and coding; available in GitHub Copilot and enterprise agent platforms. A 500K-token window (pricing steps up past 200K) suits deep code engineering and multi-step automation.
Meta 超級智能實驗室 Muse 系列的最新迭代（2026 年 9 月），維持百萬 token 上下文與約每百萬 token 1.25 美元的入門定價，並提供大幅降價的 Contributor 檔，把前沿能力壓向大規模商用量產場景。
The latest iteration of Meta Superintelligence Labs' Muse series (September 2026), keeping a 1M-token context at about $1.25 per million tokens and adding a much cheaper Contributor tier — pushing frontier capability into mass commercial workloads.
繼近萬億參數的 Inkling 之後推出的輕量版，保留百萬級上下文與開源權重，把調用成本壓至每百萬 token 約 0.45 美元，適合高頻調用與可自部署的企業生產環境。
A lightweight follow-up to the near-trillion-parameter Inkling, retaining a 1M-token window and open weights while cutting cost to roughly $0.45 per million tokens — ideal for high-frequency, self-hostable production use.
2026 年 7 月發布，採用自研 KDA 混合線性注意力機制，原生支援視覺理解，具備 100 萬詞元上下文，面向軟件工程、深度研究與多模態理解優化，綜合智能水平接近全球前沿閉源模型。
Released July 2026 with a proprietary KDA hybrid linear attention mechanism. Native vision understanding, a 1M-token context, optimised for software engineering, deep research and multimodal tasks — approaching frontier closed-source models.
8 月無預告上線的 V4 Pro 正式版，配 100 萬 token 上下文；系列同時提供超低價的 V4 Flash（約每百萬 token 0.05 美元、131 萬上下文）與視覺實驗版，形成「正式版＋低成本檔」的完整梯度，私有化部署性價比突出。
The V4 Pro stable build shipped unannounced in mid-August with a 1M-token context; the family also offers a ultra-low-cost V4 Flash (~$0.05/1M, 1.3M context) and a vision experiment tier — a full price ladder with standout self-hosting value.
8 月中旬發布的開源旗艦：與 5.2 同底座，官方稱提升全部來自「極致後訓練」，專攻編程與網絡安全場景，以一半尺寸對標一線閉源模型；隨後以 MIT 許可開源的 GLM-5.3-Flash（3,200 億總參/180 億激活）是系列首個原生多模態版本。
The open-source flagship released mid-August: same base as 5.2, with gains attributed entirely to intensive post-training, specialising in coding and cybersecurity at half the size of frontier rivals. GLM-5.3-Flash (320B total / 18B active, MIT-licensed) followed as the family's first natively multimodal model.
8 月迭代出完整梯隊：Qwen3.8-Max 以 2.4 萬億參數、原生多模態與 100 萬 token 上下文衝擊前沿；Qwen3.8-27B 與 1,250 億參數的 Qwen3.8-Flash 相繼開放權重，兼顧雲端旗艦調用與本地高性價比部署。
A full ladder iterated through August: Qwen3.8-Max pushes the frontier with 2.4T parameters, native multimodality and a 1M-token context, while Qwen3.8-27B and the 125B Qwen3.8-Flash released open weights — covering cloud flagship calls and cost-efficient local deployment alike.
8 月底開源發布並同步提供輕量版：在代碼、辦公、科學等真實生產力任務上位居開源第一梯隊；輕量版把權重從近 1.5TB 壓縮至約 214GB，支援異構設備聯合推理，顯著降低私有化算力門檻。
Open-sourced in late August alongside a lite edition: first-tier open-model performance on real productivity tasks across code, office and science. The lite build compresses weights from ~1.5TB to ~214GB with heterogeneous-device joint inference, lowering the bar for private deployment.
WAIC 2026 發布的旗艦，打通「理解、生成、行動」三大能力，突破傳統 AI 僅能單次生成內容的限制；8 月再以開源授權釋出 8B 參數的 U1.5 Lite 原生統一多模態模型，單卡即可部署，覆蓋從旗艦到輕量的完整場景。
The WAIC 2026 flagship unifying understanding, generation and action — beyond single-shot content generation to task delivery. In August it was joined by the open-sourced 8B SenseNova U1.5 Lite, a natively unified multimodal model deployable on a single GPU, spanning flagship to lightweight needs.
模型從「生成內容」轉向「交付任務」——自主規劃、調用工具、長時間執行已成為旗艦標配。Models shifted from generating content to delivering tasks — planning, tool use and long execution are now standard.
總參數與激活參數分離（如 Qwen3.8-Max 總參 2.4 萬億僅激活 950 億），在不犧牲能力的前提下大幅降低推理成本。Separating total from active parameters (Qwen3.8-Max: 95B active of 2.4T) cuts inference cost without sacrificing capability.
1M token 上下文從亮點變成旗艦標配，長文檔分析、代碼庫理解與長期記憶成為基礎能力。A 1M-token window moved from headline feature to baseline, making long-document and codebase reasoning routine.
Hugging Face 報告顯示，過去一年平台上約 41% 的大模型下載量來自中國研發的模型，開源陣營影響力持續擴大。Hugging Face reports roughly 41% of model downloads on its platform over the past year came from China-developed models — open source keeps gaining ground.
我們區別於傳統大型諮詢公司的高成本模式，也不同於純技術外包的單一交付，精准切入兩者之間的市場空白地帶。
We fill the gap between costly traditional consultancies and single-delivery tech outsourcing teams.
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