DeepSeek
DeepSeek V4 Flash Vision Exp
deepseek-v4-flash-vision-exp
最大上下文
1M
最大輸出
384K
發布日期
暫無
輸入$0.2857 / 百萬 Token
輸出$1.1429 / 百萬 Token
DeepSeek V4.1 Flash
deepseek-v4.1-flashDeepSeek-V4.1-Flash 是一款快速且高效的多模態 AI 模型，具備先進的推理、編碼和代理能力。支援高達 100 萬 tokens 的上下文與原生視覺理解，以更低的推論成本提供前沿級智慧，非常適合 AI 代理、自動化、編碼助理和可擴展應用。
上下文視窗
1M Token
最大輸出
384K Token
發布日期
2026年9月10日
模態
DeepSeek V4.1 Flash 價格
| 輸入價格 | 輸出價格 | 快取讀取 |
|---|---|---|
| $0.2857/M | $1.1429/M | $0.0057/M |
DeepSeek V4.1 Flash API 能力
推理
支援
工具呼叫
支援
Temperature 參數
支援
附件
支援
知識庫
2025-05-01
Endpoint 協議
Completions APIResponses APIMessages API
DeepSeek V4.1 Flash 模型亮點
DeepSeek V4.1 Flash 結合原生視覺理解、100 萬 Token 內容脈絡、可調整的推理強度，以及針對大量輸入 Agent 工作最佳化的模型架構。
原生視覺理解
模型會整合處理圖片與文字表徵，讓視覺資訊直接參與推理並反映於文字回應。
百萬 Token 內容脈絡
最高 100 萬 Token 的內容脈絡視窗適合分析大型程式碼儲存庫、長篇文件及長時間累積的 Agent 紀錄。
可調整推理強度
推理強度可在 1 至 100 之間連續調整，方便開發者依工作需求平衡思考深度與執行時間。
高效率非對稱架構
Causal Encoder-Decoder 在處理輸入時啟用 8B 參數、產生內容時啟用 16B 參數，並以壓縮注意力降低持久化 KV Cache 需求。
DeepSeek V4.1 Flash 使用情境
DeepSeek V4.1 Flash 適合儲存庫規模的軟體開發、視覺文件分析、長內容脈絡研究及多步驟自動化工作。
儲存庫規模開發
檢查大型程式碼庫並追蹤相依關係，透過終端機程式開發流程完成跨檔案修改並驗證修正結果。
視覺文件分析
解讀畫面截圖、圖表與文件圖片，回答相關問題、擷取所需資料並產生文字摘要。
長內容脈絡研究
在同一工作內容脈絡中檢閱大量技術資料，串聯不同章節的依據並產出結構化結論。
多步驟 Agent 自動化
規劃並執行工具輔助工作流程，整合資料蒐集、推理與程式碼執行，以完成具體操作任務。
如何透過 TokenHub API 使用 DeepSeek V4.1 Flash
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)DeepSeek V4.1 Flash 基準測試
DeepSeek V4.1 Flash (Reasoning, Max Effort)
| 指數分數 | ||
|---|---|---|
| Artificial Analysis 綜合能力指數 | Artificial Analysis 綜合能力評分 | 39.5 |
指標來源 Artificial Analysis
DeepSeek V4.1 Flash 常見問題
關於在 TokenHub 使用 DeepSeek V4.1 Flash 的常見問題。
DeepSeek V4.1 Flash 是什麼？+
它是 DeepSeek 的多模態混合專家模型，擁有 552B 參數主幹、非對稱因果編碼器—解碼器架構，並支援文字與圖像輸入。
DeepSeek V4.1 Flash 最適合哪些情境？+
它適合程式設計代理、長上下文分析、視覺文件理解，以及反覆處理大型輸入的高吞吐工作流程。
它的主要優勢是什麼？+
其優勢包括高效的非對稱運算、顯著縮小的持久化 KV 快取、百萬 Token 上下文、原生圖像理解以及對代理式工作負載的有力支援。
需要考慮哪些限制或取捨？+
其開放權重體量很大，自行部署需要大量資源；視覺輸入輸出的是文字而非圖像；重要結果仍需驗證。請用實際工作負載測試品質、延遲與總成本。
如何在 TokenHub 使用 DeepSeek V4.1 Flash？+
在 TokenHub API 請求中選擇 deepseek-v4.1-flash，並使用帳戶中顯示的介面位址與憑證。請在 TokenHub 模型文件中確認目前的輸入格式與限制。
價格與可用性如何確定？+
DeepSeek 已透過其 API 提供該模型，並採用峰谷定價。TokenHub 的費率與地區可用性可能不同，部署前請查看即時模型頁面。
什麼時候應選擇其他模型？+
如果需要圖像生成、體量小得多的本機部署，或特定供應商生態功能，應選擇其他模型。決定前請用代表性提示詞比較候選模型。
準備呼叫 DeepSeek V4.1 Flash？
透過一個 API Key 接入 DeepSeek V4.1 Flash 與更多 AI 模型。
DeepSeek V4.1 Flash 媒體與示範
經驗證的 DeepSeek V4.1 Flash 公開公告、技術解析與社群測試。
X (Twitter)
DeepSeek ·
🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient. 🔹 Introducing the smallest model in our new architecture family, with native visual understanding. 🔹 Designed for greater capability, faster inference, higher throughput, and scaling to larger models. 1/6 https://t.co/wxJGiyX56o
https://pbs.twimg.com/media/HR1UyHiaAAAtpqw.jpg查看原文DeepSeek ·
💾 Smaller KV cache. Bigger savings. Compared with the previous generation, V4.1-Flash’s KV cache needs just: 🔹 1/4 the HBM 🔹 1/8 the SSD storage Cache-hit charges often account for a large share of agent costs. Compressing the cache cuts those costs significantly. 3/6 https://t.co/rLzo9cTJzR
https://pbs.twimg.com/media/HR1VAiCaUAA4jnX.jpg查看原文DeepSeek ·
⚡ V4.1-Flash is now live on the DeepSeek API with native multimodal support. Set your model to deepseek-flash. 🔹 V4-Flash & V4-Flash-Vision-Exp are retired. For compatibility, deepseek-v4-flash and deepseek-v4-flash-vision-exp temporarily route to V4.1-Flash. 🔹 Tests by
查看原文Reddit
DeepSeek-V4.1-Flash Release (official)
dnohrdk ·
展開全文
It’s officially out and the prices have been updated. /// Today, we officially release the DeepSeek-V4.1-Flash model. It is the smallest model in our new architecture family, with native multimodal visual understanding. The new architecture is designed for a higher capability ceiling, faster inference, higher throughput, and scaling to larger models. GPQA Diamond: 90.9 HLE: 36.8 (39.1*) Codeforces (Rating): 3471 MathArena Apex: 65.6 Terminal-Bench 2.1: 90.6 Terminal-Bench 3.0: 30.0 Terminal-Bench 4.0: 31.2 DeepSWE v1.1: 74.2 ProgramBench: 20.3 NL2Repo-Bench: 65.4 CyberGym: 88.1 SEC-Bench Pro: 62.8 ExploitGym: 15.3 HLE (w/tools): 63.9 Automation-Bench: 54.8 Agents' Last Exam: 31.8 Chartography (w/tools): 78.9 BabyVision (w/tools): 89.6 ZeroBench-main (w/tools): 49.0 * Tested only on the pure-text subset of the HLE benchmark set. API changes DeepSeek V4.1 Flash is now available on the DeepSeek API with native multimodal support. Change the model name to deepseek-flash to call the latest V4.1 Flash model. The previous-generation models V4 Flash and V4 Flash Vision Exp have been retired; for compatibility, the model names deepseek-v4-flash and deepseek-v4-flash-vision-exp are temporarily routed to V4.1 Flash. Meanwhile, extensive testing shows that V4.1 Flash now outperforms DeepSeek V4 Pro across performance, cost, speed, and total time, so we plan to retire V4 Pro in an orderly manner. After 12:00 Beijing Time on September 14, 2026, and until the future release of V4.1 Pro, all requests to deepseek-v4-pro will be routed to V4.1 Flash and billed at the V4.1 Flash price. API apricing adjustment With the release of DeepSeek-V4.1-Flash, API prices have been reduced accordingly. For details, please refer to Models & Pricing. /// Source: https://api-docs.deepseek.com/updates/#deepseek-v41-flash-release
DeepSeek V4.1 Flash: Stronger, Faster, More Accessible
Top_Power5877 ·
展開全文
Original Source from DeepSeek WeChat Official Account: https://mp.weixin.qq.com/s/qg0NU3NNUbp1co2PdkAPAg Today we're officially releasing the DeepSeek V4.1 Flash model. It is the smallest model in our brand-new model architecture series, with native multimodal visual understanding. The new architecture was designed with these goals in mind: a higher capability ceiling, faster inference, greater throughput, and scalability to larger-parameter models. Asymmetric architecture: big intelligence at low cost DeepSeek V4.1 Flash is a 552B-parameter MoE model built on a brand-new Causal-Encoder-Decoder architecture. Input and output are asymmetric: only 8B parameters are activated on the input side and 16B on the output side, making it significantly cheaper than known models of the same size. V4.1 Flash also uses a new pre-training approach and has gone through larger-scale reinforcement learning post-training. In benchmark testing, it surpasses the intelligence level of a range of flagship models, including DeepSeek V4 Pro. r/LocalLLaMA - DeepSeek V4.1 Flash: Stronger, Faster, More Accessible (https://preview.redd.it/deepseek-v4-1-flash-stronger-faster-more-accessible-v0-qq5p9q5qymoh1.png?width=1080&format=png&auto=webp&s=3ae8cc6dbdd62a2b2ec7f59dc207d147c9165c0f) r/LocalLLaMA - DeepSeek V4.1 Flash: Stronger, Faster, More Accessible (https://preview.redd.it/deepseek-v4-1-flash-stronger-faster-more-accessible-v0-2tvpysuvymoh1.png?width=1080&format=png&auto=webp&s=84ed35f530b3c0a4534036eaa4fc83cfce641b7d) Less cache, lower cost The new generation of models dramatically reduces the size of the KV cache. Compared with the previous generation, HBM requirements drop to 1/4 and SSD requirements to 1/8. In agent scenarios, cache-hit charges often make up a large share of the bill, so compressing the KV cache substantially lowers the cost of agent-style tasks. r/LocalLLaMA - Figure: DeepSeek's continued progress in reducing context storage. Relative to the first-generation model, the KV cache has shrunk 437×. (https://preview.redd.it/deepseek-v4-1-flash-stronger-faster-more-accessible-v0-p47kb004zmoh1.png?width=1080&format=png&auto=webp&s=e67d215932f53fa7fb9987c31a0ee55490811bad) Figure: DeepSeek's continued progress in reducing context storage. Relative to the first-generation model, the KV cache has shrunk 437×. API support DeepSeek V4.1 Flash is now live on the DeepSeek API with native multimodal support. Simply change the model name to deepseek-flash to call the latest V4.1 Flash. The older V4 Flash and V4 Flash Vision Exp models have been retired; for compatibility, the model names deepseek-v4-flash and deepseek-v4-flash-vision-exp will temporarily be routed to V4.1 Flash. In addition, extensive testing shows that V4.1 Flash comprehensively outperforms V4 Pro on performance, cost, speed, and total time-to-completion, so we plan to phase out the V4 Pro model in an orderly fashion. After 12:00 Beijing time on September 14, 2026, and until V4.1 Pro launches, all requests to deepseek-v4-pro will be routed to V4.1 Flash and billed at V4.1 Flash's unit price. Tencent (WorkBuddy, CodeBuddy) and OpenCode, as official partners, have now fully integrated DeepSeek V4.1 Flash — give it a try! API pricing adjustment Thanks to the architectural innovations, DeepSeek V4.1 Flash can serve more users at lower cost, so we have cut V4.1 Flash's pricing accordingly. To allocate resources more sensibly, we continue to use peak/off-peak pricing, with off-peak prices at half the peak rate, and encourage users to schedule tasks around their actual usage patterns. The new prices take effect at 12:00 on September 10, 2026. r/LocalLLaMA - DeepSeek V4.1 Flash: Stronger, Faster, More Accessible (https://preview.redd.it/deepseek-v4-1-flash-stronger-faster-more-accessible-v0-qkhui0kjzmoh1.png?width=1690&format=png&auto=webp&s=0d6ce254d63e29bfabea7c65638342717a5d1e54) Open-source release We will fully support the open-source community in adapting inference for the new model, and will explore various ways to broaden deployment. If you have large-scale deployment needs and the corresponding resources (a 2k-GPU cluster with storage cluster), please get in touch. Model weights: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash Paper: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/DeepSeek_V41_Tech_Report.pdf
DeepSeek V4.1 Flash vs V4 Flash Vision Exp: 38% faster and 43% fewer tokens in my quick coding test
Diru14 ·
查看原文YouTube
DeepSeek V4.1 Flash Is INSANE – Is THIS the Best Open Model Yet?
Bijan Bowen
查看原文Deepseek V4.1 Flash (Fully Tested): 200 TPS & Beats Astra!? (+New Architecture Overview)
AICodeKing
查看原文DeepSeek V4.1 Flash 有多快？架构简析 + Pi Agent 演示
01Coder
查看原文