What Is DeepSeek V4?DeepSeek V4 是什么？
DeepSeek V4 is DeepSeek's open-weights model family, released April 24, 2026 — the successor to the V3.2 generation, jumping its context ceiling from 128K to 1M tokens on both tiers. The family ships in two tiers: V4-Pro (1.6 trillion total parameters, 49B active per token) for top capability, and V4-Flash (284 billion total, 13B active) for low cost. Both are sparse Mixture-of-Experts designs, both are released under the MIT license with downloadable weights, and V4-Pro exposes three reasoning-effort modes — non-thinking, Think High and Think Max — with Think Max posting the headline benchmarks.DeepSeek V4 是 DeepSeek 2026 年 4 月 24 日发布的开权重模型家族，是 V3.2 世代的继任者，双档上下文上限从 128K 一跃到 100 万 token。家族分两档：V4-Pro（1.6T 总参数、每 token 激活 49B）主打能力上限，V4-Flash（284B 总参数、激活 13B）主打低成本。两者均为稀疏 MoE 架构，均采用 MIT 许可、权重可下载；V4-Pro 提供非思考/Think High/Think Max 三档推理努力度，头条基准由 Think Max 档打出。
The headline benchmark is 80.6% on SWE-bench Verified for V4-Pro-Max — the highest open-weight coding result at launch, within a few points of the closed leaders at the time — plus vendor-reported strength on competitive coding (LiveCodeBench 93.5, Codeforces 3206). Treat all of those as vendor-reported: independent harnesses since have reported both higher and lower numbers depending on setup, so re-run on your own tasks before committing. Prices per the official site — see the pricing warning below.头条基准是 V4-Pro-Max 的 SWE-bench Verified 80.6%——发布时开权重编程最高分，离当时闭源领头羊只差几个点；另有厂商自报的竞赛编程成绩（LiveCodeBench 93.5、Codeforces 3206）。这些一律按厂商自报处理：此后独立测试因设置不同报出过更高也更低的数字，拍板前请在自己的任务上重测。价格以官网为准——见下文定价提醒。
DeepSeek V4 is the cost-disruption play done properly: a 1.6T open-weights model whose headline coding score sits within striking distance of closed flagships, at a serving cost that makes 1M-token agent loops economically sane for the first time. The two-tier split is the smart part — Flash for volume, Pro-Max for the hard tasks — and MIT licensing removes the legal friction that slows enterprise adoption of other open weights. The honest costs: Pro-class self-hosting is a serious GPU commitment, reasoning-heavy answers are slow (third-party harnesses measured both tiers among the slowest they ran), and the API price has moved enough in 2026 that any budget built from a blog post will be wrong. Verdict: if your workload is patch-shaped coding at volume, start on Flash and escalate only the tasks that fail to Pro-Max; if you need data sovereignty, download the weights and stop renting tokens altogether — but price the GPU cluster first.
DeepSeek V4 是把成本颠覆做对了的打法：1.6T 开权重模型的头条编程分逼近闭源旗舰，服务成本却让 100 万 token 的 Agent 链路第一次在经济上成立。双档分工是聪明之处——Flash 跑量、Pro-Max 啃硬任务；MIT 许可则去掉了其他开权重在企业落地时的法律摩擦。真实代价：Pro 级自托管是实打实的 GPU 投入；推理重的回答偏慢（第三方测试里两档都排在最慢之列）；API 价在 2026 年内动得足够多，照博客文章做的预算一定会错。结论：如果你的活是批量补丁式编程，先上 Flash、只有失败的任务再升 Pro-Max；如果要数据主权，直接下载权重、别再租 token——但先把 GPU 集群的账算清楚。
— 中国吧 AI Tools Hub Editorial Team
Who Should Use DeepSeek V4?谁适合使用 DeepSeek V4？
✓ Good Fit For适合以下场景
- High-volume coding workloads where per-token cost dominates单 token 成本占大头的批量编程工作
- Teams that need MIT weights for self-hosting or fine-tuning需要 MIT 权重做自托管或微调的团队
- Long-context agent loops up to 1M tokens100 万 token 以内的长上下文 Agent 链路
✕ Not Ideal For不适合以下场景
- Anyone budgeting from a stale third-party price list — check the official rate card照过期第三方价目表做预算的人——请查官网价目表
- Latency-critical interactive chat — reasoning-heavy tiers are slow时延敏感的交互对话——推理重的档偏慢
- Small teams expecting to self-host Pro on modest hardware想在普通硬件上自托管 Pro 的小团队
Getting Started with DeepSeek V4DeepSeek V4 快速开始
1. Pick the tier first: Flash for volume, Pro-Max for the hardest coding tasks — do not default everything to Pro先选档：跑量用 Flash、最难的编程任务用 Pro-Max，别默认全走 Pro2. Check DeepSeek's official pricing page on the day you budget — 2026 rates changed repeatedly; prices per official site做预算当天查 DeepSeek 官网价目——2026 年价目多次变动；价格以官网为准3. Self-hosting: pull the MIT weights from Hugging Face / ModelScope and size the cluster for the tier you chose自托管：去 Hugging Face / ModelScope 拉 MIT 权重，按所选档位配 GPU 集群Key Features核心功能
- V4-Pro: 1.6T MoE, 49B ActiveV4-Pro：1.6T MoE、49B 激活 — 1.6 trillion total parameters with 49B active per token — the largest open-weights model of 2026 at launch.1.6T 总参数、每 token 激活 49B——发布时 2026 年最大的开权重模型。
- V4-Flash: 284B, 13B ActiveV4-Flash：284B、13B 激活 — 284B total / 13B active cost tier for high-volume work, with a much smaller download (~160 GB reported).284B 总参数/13B 激活的成本档，面向大批量工作，下载包小得多（据报约 160GB）。
- 1M-Token Context, Both Tiers双档均为 100 万上下文 — A million-token context window on both Pro and Flash — a 10x jump over the V3.2 generation's 128K ceiling.Pro 与 Flash 均为 100 万上下文——比 V3.2 世代的 128K 上限翻了约 10 倍。
- MIT License, Downloadable WeightsMIT 许可、权重可下载 — MIT-licensed weights on Hugging Face and ModelScope — self-host, fine-tune and embed with no per-token fee.MIT 许可权重见 Hugging Face 与 ModelScope——自托管、微调、嵌入均不收 per-token 费用。
- Three Reasoning-Effort Modes (Pro)Pro 三档推理努力度 — V4-Pro offers non-thinking, Think High and Think Max modes; headline benchmarks come from Think Max.V4-Pro 提供非思考/Think High/Think Max 三档；头条基准出自 Think Max 档。
Pros & Cons优缺点
✓ Pros优点
- Frontier-adjacent coding (80.6% SWE-bench Verified, vendor-reported) at open-weights cost接近前沿的编程能力（SWE-bench Verified 80.6%，厂商自报），开权重成本
- MIT weights: self-host, fine-tune, no per-token feeMIT 权重：自托管、微调、不收 per-token 费
- 1M context on both tiers for long agent loops双档 100 万上下文，撑得起长 Agent 链路
- Two-tier split matches cost to task difficulty双档分工：成本按任务难度匹配
✕ Cons缺点
- API pricing changed repeatedly in 2026 — stale quotes are everywhereAPI 价 2026 年内多次变动——过期报价到处都是
- Reasoning-heavy tiers are slow in third-party harnesses推理重的档在第三方测试里偏慢
- Pro self-hosting needs serious GPU capacityPro 自托管需要相当规模的 GPU
Use Cases应用场景
Popular ways people use DeepSeek V4.人们使用 DeepSeek V4 的常见场景：
💻 Patch-Shaped Coding at Volume批量补丁式编程
Fixing real GitHub-style issues in bulk — the SWE-bench-shaped workload where V4-Pro-Max shines, priced per the official site.批量修 GitHub 风格的真实 issue——正是 V4-Pro-Max 擅长的 SWE-bench 型工作；价格以官网为准。
🏆 Competitive Programming竞赛编程
Vendor-reported LiveCodeBench 93.5 and Codeforces 3206 make it a strong contest-coding candidate — verify on your own problem set.厂商自报 LiveCodeBench 93.5、Codeforces 3206，是竞赛编程的有力候选——请在自己的题集上验证。
🔧 Private / On-Premise AI私有化/本地部署 AI
MIT weights run inside your own infrastructure for regulated or data-sensitive work — no data leaves your network.MIT 权重跑在自己的基础设施里，适合受监管或数据敏感场景——数据不出内网。
🤖 Low-Cost Agent Loops低成本 Agent 链路
1M context makes whole-repo agent loops affordable on Flash — the "cost-effective 1M context" pitch, checked against the official rate card.100 万上下文让整库 Agent 链路在 Flash 上也负担得起——这就是“高性价比 100 万上下文”的卖点，仍需对照官网价目表核对。
Similar AI Tools相似 AI 工具
If DeepSeek V4 doesn't fit your needs, here are other popular AI tools you might consider — the earlier DeepSeek overview at DeepSeek, the closed-model coding reference at Claude, and Google's flagship entry at Gemini.如果 DeepSeek V4 不符合你的需求，可以看看这些热门 AI 工具——早期概览见 DeepSeek，闭源编程参照见 Claude，谷歌旗舰入口见 Gemini。