Gemini 3.6 Flash
最大上下文
1M
最大輸出
65.5K
發布日期
暫無
gemini-4-argonGemini 4 Argon 是 Google 的旗艦推理模型,專為複雜軟體工程、深度數學與科學問題求解,以及自主多步驟代理而打造。Google 訓練它能在冗長的多步驟任務中維持上下文,並將混合文字、表格、圖表和圖片的長文件轉化為單一有據可依的輸出。它在金融、法律、程式設計和稅務工作中領先 Vals Index,並在單次回應中支援最長 100 萬輸出 token 的長解碼。
上下文視窗
1M Token
最大輸出
65.5K Token
發布日期
2026年10月13日
模態
| 輸入價格 | 輸出價格 | 快取讀取 | 快取建立 5m |
|---|---|---|---|
| $4/M | $20/M | $0.2/M | $0.375/M |
推理
工具呼叫
Temperature 參數
附件
知識庫
Endpoint 協議
面向複雜推理、持續工程任務與多模態理解。
面向高難度程式設計、數學與科學問題,可調整推理深度。
旨在跨複雜、多步驟的軟體工程與自主代理流程持續保持上下文。
輸入、推理和輸出共享 1,048,576 Token;Google 的長輸出流程可突破標準 64K 輸出上限。
融合文字、圖像、音訊、影片和 PDF 輸入,產生文字形式的分析與綜合結果。
適用於複雜開發、研究與文件分析的實際任務。
規劃並實施跨檔案重構或舊程式碼遷移,再依據專案要求審查修改。
將包含文字、表格、圖表和圖像的長文件整理為依據所提供資料撰寫的連貫報告。
探索高難度技術問題、推導數學論證,並起草供獨立驗證的研究分析。
建立跨多個步驟完成規劃、建立、檢查和修訂的軟體或 3D 原型流程。
import OpenAI from "openai"
const client = new OpenAI({
apiKey: process.env.TOKENHUB_API_KEY,
baseURL: "https://us-api.tokenhub.com/v1",
})
const result = await client.chat.completions.create({})
console.log(result.choices[0]?.message?.content)Gemini 4 Argon (High)
| 指數分數 | ||
|---|---|---|
| Artificial Analysis 綜合能力指數 | Artificial Analysis 綜合能力評分 | 52.6 |
指標來源 Artificial Analysis
了解模型能力、實際取捨與 TokenHub 串接方式。
它是 Google DeepMind 面向複雜程式設計、自主代理、數學與科學任務的旗艦推理模型。
適用於需要持續推理的跨檔案工程、多模態文件綜合和技術研究。
Google 文件列出文字、圖像、音訊、影片和 PDF 輸入,輸出為文字。圖像與音訊生成需要其他模型。
模型可能產生幻覺,複雜請求也可能較慢。百萬 Token 預算由輸入、推理和輸出共享;超過 64K 的輸出需要 Google 長輸出流程及路由支援。
若帳戶中已上架,請選擇 Gemini 4 Argon,使用 TokenHub 顯示的模型 ID、端點位址和 API 金鑰;串接前確認所選路由的功能。
Google 已提供付費 API 存取。TokenHub 的價格、路由和帳戶可用性請查看目前模型頁,可能與 Google 直連定價不同。
當任務重視推理深度、大規模混合模態輸入或持續工程流程時可選用它。請用代表性任務比較準確性、延遲和總 Token 成本。
透過一個 API Key 接入 Gemini 4 Argon 與更多 AI 模型。
Gemini 4 Argon 媒體與示範
已核驗的官方貼文與示範,以及社群早期使用體驗。
X (Twitter)
Gemini 4 Argon 應用上線公告
Google Gemini ·
官方公告介紹 Gemini 4 Argon 在 Gemini 應用中的推出。
The moment you've been waiting 4… Our new frontier model, Gemini 4 Argon, is rolling out now in the Gemini app to bring your most complex ideas into reality. See how Gemini 4 Argon and our latest updates across the Gemini app help you get the most out of every prompt. 🧵
查看原文Gemini 4 Argon 從規劃到成果
Google Gemini ·
展示 Gemini 應用使用 Gemini 4 Argon 製作網頁應用、3D 檔案、投影片和報告的流程。
Gemini 4 Argon breaks down your tasks step-by-step and instead of stopping at a text outline, Argon helps you build the real thing, whether that means: ➡️ Generating a shareable web app ➡️ Giving you ready-to-print 3D files ➡️ Producing slides and reports that are ready to go https://t.co/V4RPXun8I9
https://pbs.twimg.com/amplify_video_thumb/2110039533853212673/img/r4OrZZO4Lsf8FhNo.jpg查看原文Gemini 4 Argon 在 Gemini 應用中編程
Google Gemini ·
介紹程式設計能力改進,以及 macOS 版 Gemini 應用中的 Antigravity 程式設計介面。
Gemini 4 Argon also brings major improvements in coding, so we’re making it easier to switch between chatting and coding with Google Antigravity's agentic coding interface now available directly in the Gemini app for macOS. https://t.co/waB3VmjmDI
https://pbs.twimg.com/amplify_video_thumb/2110039768579080193/img/q_yj8_IP7nVoDQHf.jpg查看原文Reddit
Gemini 4 Argon:程式設計初步評測
Gemini 4 Argon Initial review
this-is-hilarours ·
社群使用者比較後端實作方案,留言討論測試方法與不同的程式設計體驗。
展開全文
As an ultra user just got the access to Gemini 4 Argon. i made an initial test in my python backend project . the project is already in production and there was a real feature integration that i have started working today . so i was curious how it would compare against Opus 5.5 . I have a claude code subscription and i also use a factory droid subscription . so i created an implementation plan based on the spec with opus 5.5 with extra high effort level . similarly created an implementation plan with Argon high . Both of the plan was fed to Gpt 6 astra who worked as judge as i believe gpt model always works as strong review model . i asked it to carefully judge and give both of them score out of 100 . here is the score astra give Opus 5.5 Score - 77/100 Gemini 4 Argon Scores - 59/100 I gave the original score and review to Gemini in antigravity it agreed with all of that . i think more testing would be needed from my side to test it properly . but it needs at least one more reasoning level like extra high One thing surprised me It compacted the conversation relatively faster . i don't know what is the context window set here but previously i never got the context compacted this fast . pretty sure the context is not 1 million current set in antigravity app
Gemini 4 Argon:發布討論
Gemini 4 Argon Release Blog
elemental-mind ·
社群貼文分享 Google 關於 Gemini 4 Argon 擴大開放範圍的公告。
查看原文Gemini 4 Argon:早期使用者回饋
Thanks for Gemini 4 Argon
Liberation2020 ·
一位 Ultra 訂閱者分享正面的任務體驗,討論涉及速度、用量限制和個人工作流程。
展開全文
I’m making this for the Google team that may be checking the thread. I’m an Ultra user and have patiently been waiting for this model. It is everything I need and more. I’ve been seeing others getting great results from it. As well. It’s ability to turn some of my things into skills that actually work is next level compared to what was happening before. It’s work rate is also impressive in my opinion detailed heavy task that I feel should maybe take a little while are done quite quickly. I’m still fine running my request style with it but I am quite happy with it this past couple of hours. It even did better on a task I had set to Astra to complete. (Not saying it is overall better just did that one task better) Bottom line I’m happy and I’m super excited to see nano banana pro update and also the video models!!!
YouTube
Gemini 4 Argon:十個實用示範
Gemini 4 Argon: 10 incredible AI demos (1M token output in action)
Google Cloud Tech
官方示範涵蓋程式碼遷移、互動式 3D 專案和多模態成果。
查看原文Gemini 4 Argon:從概念圖到 3D 大教堂
Gemini 4 Argon and Google Antigravity: From 2D concept to 3D cathedral
Google for Developers and Google Antigravity
代理結合 Gemini 4 Argon、Antigravity 和 Blender,根據二維參考圖重建大教堂。
查看原文Gemini 4 Argon:舊程式碼遷移至 Rust
Gemini 4 Argon: Modernizing 40 year code
Google Antigravity and Google for Developers
官方示範使用 Antigravity 和 Gemini 4 Argon,將數十年前的科學運算程式碼遷移至 Rust。
查看原文