DeepSeek v3.1 在 2025 年的最新更新和行业影响
- Aisha Washington
- 6月7日
- 讀畢需時 10 分鐘
DeepSeek v3.1 是 2025 年人工智能领域的一次重大更新。这个开源版本拥有 6710 亿参数。它还拥有稳定的 128K 上下文窗口。这些特性为规模和性能树立了新纪录。改进的 Mixture-of-Experts 设计帮助 DeepSeek 超越旧版本。它还能与最好的 AI 模型竞争。下表显示了 DeepSeek v3.1 的参数数量和上下文窗口与早期版本的比较：
模型版本 | 总参数量 | 上下文窗口大小 |
DeepSeek V2 | N/A | 4K tokens |
DeepSeek V3 | N/A | 最多 128K tokens |
DeepSeek V3.1 (0324) | 6710 亿 | 128K tokens |
DeepSeek v3.1 的开源许可和强大功能帮助它快速传播。这次更新改变了人工智能行业。它影响了开发者、研究人员和公司。DeepSeek 现已成为人工智能创新的顶级领导者。
关键要点
DeepSeek v3.1 是一个强大的 开源 AI 模型。它拥有 6710 亿参数和 128K token 上下文窗口。这有助于它处理超长文档和复杂任务。
该模型在推理、编码和语言能力方面得到提升。它提供更准确、更流畅的结果。用户因无需支付许可费用而节省成本。
DeepSeek v3.1 使用更少的 GPU 和更少的能源进行训练。这使其比其他大型 AI 模型更便宜且更环保。
其开源许可允许公司和开发者在自己的服务器上使用 DeepSeek。他们可以控制自己的数据并根据需求修改模型。
DeepSeek v3.1 通过降低成本和提高易用性改变了人工智能市场。现在全球许多人都在使用它。它还影响了全球人工智能竞争和规则。
DeepSeek v3.1 更新
改变游戏规则的功能
DeepSeek v3.1 使用 6850 亿参数系统。这为大型语言模型设立了新标准。更新提供了更大的 128K 上下文窗口。现在，用户可以轻松处理更长的文档和更难的任务。open-source MIT license 非常重要。它允许团体控制如何使用 DeepSeek 并保护数据隐私。公司可以在自己的云或服务器上使用 DeepSeek。这有助于他们遵守严格的法规。
DeepSeek v3.1 的最佳功能包括：
推理能力更强，尤其在逻辑、数学和编码方面。
帮助前端工作，生成更干净的代码和更美观的界面。
写作和翻译更流畅、更准确。
支持多轮重写和报告分析，提供更好的体验。
由于开源，它可以扩展而不会被单一供应商锁定。
通过取消许可费用节省成本，让更多人可以使用它。
注意：DeepSeek v3.1 使用 Mixture-of-Experts design。它注重速度、智能思考和灵活性。它不是从头改变一切。
下表显示了 DeepSeek v3.1 相比 v3.0 的改进：
基准类别 | DeepSeek v3.0 性能 | DeepSeek v3.1 性能 | 改进 |
MMLU-Pro（专业知识） | 75.9 | 81.2 | +5.3 分 |
GPQA（通用问题解决） | 59.1 | 68.4 | +9.3 分 |
AIME（数学考试） | 39.6 | 59.4 | +19.8 分 |
LiveCodeBench（编码熟练度） | 39.2 | 49.2 | +10.0 分 |
性能提升
DeepSeek v3.1 在编码、推理和多语言方面表现出色。它在中文和其他语言编码测试中表现优异。在 CLUEWSC（90.9%）和 C-Eval（86.5%）中得分很高。Qwen2.5 在某些编码测试中更强。但 DeepSeek v3.1 非常灵活，是最好的全能模型。
基准 / 指标 | DeepSeek v3.1 得分 | 比较模型 / 版本 | 比较得分 | 意义 |
Codeforces 百分位 | 51.6 | GPT-4-0513 | 35.6 | 更强的编码竞赛技能 |
SWE-bench Verified (%) | 42.0 | GPT-4-0513 | 50.8 | 有竞争力的软件工程问题解决能力 |
AIME 2024 (%) | 39.2 | DeepSeek v2.5 | 23.3 | 显著的推理改进 |
Arena-Hard 得分 | 85.5 | DeepSeek v2.5 | 76.2 | 在困难任务中更好的上下文感知生成 |
AlpacaEval 2.0 得分 | 70.0 | Claude-Sonnet-3.5 | 52.0 | 改进的用户偏好和输出质量 |
Aider Polyglot Benchmark | 40-50% 准确率 | N/A | N/A | 在 diff-like 和完整格式中强大的编码任务完成能力 |
DeepSeek v3.1 发布后不久就有很多人开始使用。短短几周内，每日活跃用户达 3000 万。到 2025 年 1 月，每月活跃用户达 3370 万。约 7% 使用自有 AI 的团体现在使用 DeepSeek。这是 1 月前的两倍多。开源风格和低成本促进了这种增长。API 成本为每百万 token 2.19 美元，比西方 LLM API 便宜三到四倍。
下图显示了 2025 年 1 月 DeepSeek v3.1 用户的分布：
DeepSeek 的 GitHub 页面显示了大量社区工作。有超过 5000 个 fork 和许多他人制作的新版本。MIT 许可允许人们参与并创造新事物。这支持了全球开源人工智能的推动。团体获得灵活性、可扩展性和对模型的信任。开发者可以根据需求修改和改进 DeepSeek。
DeepSeek v3.1 与竞争对手
基准比较
DeepSeek v3.1 在与其他 AI 模型的对比中表现优异。它的得分接近 GPT-4 和 Gemini Ultra 等顶级模型。DeepSeek 因在知识、编码和推理方面的出色表现而受欢迎。下表显示了 DeepSeek v3.1 与其他领先模型的比较：
基准 | 描述 | DeepSeek v3.1 得分 | GPT-4o 得分 | Llama 3.3 70B 得分 |
MMLU | 测试 57 个科目的知识 | ~88.5% | 88.7% | 88.5% |
MMLU-Pro | 复杂推理 | 75.9% | 74.68% | 75.9% |
HumanEval | Python 编码能力 | 82.6% | 90.2% | 88.4% |
MATH | 高级数学问题解决 | 61.6% | 75.9% | 77% |
GPQA | 博士级科学知识 | 59.1% | 53.6% | 50.5% |
IFEval | 指令遵循 | 86.1% | N/A | 92.1% |
DeepSeek 在某些领域优于其他模型。它在 GPQA 和 MMLU-Pro 中表现强劲。这意味着它擅长科学和推理。DeepSeek 还拥有更大的上下文窗口。这有助于人们处理更长的文档和更难的任务。
成本效率
DeepSeek v3.1 为用户节省资金。它训练大约需要 2048 个 Nvidia H800 GPU。其他模型如 Meta 的 Llama 3.1 405B 需要超过 16000 个 GPU。DeepSeek 团队努力减少计算能力和时间。training cost is about $5.576 million。其他一些模型训练成本达 6000 万美元。DeepSeek 使用多头潜在注意力和部分 8 位训练等智能想法。这些有助于减少能源消耗和 GPU 小时数。
团体使用 DeepSeek 的开源模型节省资金。下表显示了与其他模型相比每月节省的金额：
用例 | 每月 token 数 | 专有成本（Claude） | DeepSeek 成本 | 大致节省 |
初创 MVP | 1000 万 | $180 | $14 | ~92% |
内容生成 | 5000 万 | $900 | $69 | ~92% |
企业客户服务 | 2 亿 | $3600 | $274 | ~92% |
代码生成 | 1 亿 | $1800 | $137 | ~92% |
研究/学术 | 10 亿 | $18000 | $1370 | ~92% |
DeepSeek 的低成本帮助更多人使用它。Startups and research teams can use advanced AI。他们无需支付高额费用或被单一供应商锁定。
可及性
DeepSeek v3.1 使高级 AI 更容易获取。其开源发布是行业的一大步。小公司和独立研究人员现在可以使用强大模型。他们不需要大量计算机。DeepSeek 需要更少的 GPU，因此更多人可以尝试。它的高效性和开放访问帮助人工智能在全球传播。随着更多开发者加入并根据需求修改 DeepSeek，社区快速增长。
DeepSeek 的开源风格允许团体在自己的服务器或私有云上运行 AI。这使他们完全控制自己的数据和系统。
DeepSeek 的增长改变了 AI 模型的竞争方式。其易用性、低成本和强大结果帮助它快速增长并被广泛使用。
技术创新
高效训练
DeepSeek v3.1 带来了更快训练的新方法。团队使用 DualPipe algorithm。这有助于计算机同时工作和通信。它减少了浪费的时间并降低了额外工作。该模型在 Mixture-of-Experts 层中使用特殊平衡。它使用基于偏差的更改来保持负载均衡。这保持了高准确性。FP8 混合精度训练节省内存和计算能力。它还保持数字稳定。多 token 预测让模型一次猜测多个 token。这使训练更快并加速响应。当 DeepSeek 运行时，它将预填充和解码分开。这有助于 GPU 更好地工作并保持等待时间短。额外的专家托管和智能路由使运行更顺畅。
指标 | DeepSeek V3.1 | Meta Llama 3.1 |
GPU Hours | ~30.8 million | |
GPU Type | NVIDIA H800 | NVIDIA H100 |
Training Cost (USD) | ~$5.576 million | N/A |
This table shows DeepSeek uses much less computer power than other models. This helps make ai more earth-friendly.
Model Architecture
DeepSeek v3.1 uses Mixture-of-Experts architecture. It sends tokens to special expert modules. This helps balance speed and power. The model only turns on some of its 685 billion parameters for each token. This saves computer resources. Multi-head Latent Attention squeezes key-value data. This lowers memory use and lets the model handle longer context windows. The model keeps a 128K token context window. This helps study long content in detail. Byte-level Byte Pair Encoding with 128,000 tokens helps compress text in many languages. The architecture picks expert modules for each task. This makes things faster and more exact.
DeepSeek’s new design solves old problems with scaling. Now, it can handle huge models and long context windows with less memory and computer power.
Practical Impact
DeepSeek v3.1 helps many industries in real life. The model can work with up to 128,000 tokens in a row. This is important for legal papers and science research. Its FP8 inference works on many types of hardware. It runs on big gpu clusters and small edge devices. This lets people make choices quickly and use ai in smart ways. For coding, DeepSeek mixes chat, thinking, and coding skills. It helps engineers build web apps and fix problems fast. Big companies like Tencent, Baidu, and Huawei use DeepSeek in their products. AMD uses DeepSeek v3 to make ai work better on its Instinct MI300X GPUs. The model’s low API price shakes up the market. It lets startups and research teams use strong ai. These real-world uses show DeepSeek’s big benefits in coding and long-context jobs.
Industry Impact
Market Disruption
DeepSeek v3.1 changed the AI industry in big ways. Its training cost is about $6 million. This is much less than OpenAI’s $100 million for GPT-4. Because DeepSeek is cheaper, other companies had to rethink prices. DeepSeek’s bold move made the market react fast. Nvidia lost $589 billion in value in one day after DeepSeek came out. The Nasdaq went down 3%. The S&P 500 dropped 1.5%. Investors worried about spending too much on AI hardware.
Date | Event | Nvidia Market Cap Change | Nasdaq Change | S&P 500 Change |
Jan 27, 2025 | DeepSeek v3.1 release, market reacts | -$589 billion | -3% | -1.5% |
Jan 28, 2025 | Partial rebound in Nvidia stock | +$260 billion | N/A | N/A |
DeepSeek’s growth made companies lower their prices. The company set very low prices for its R1 model. This made AI more like a basic product, just like computer chips. More people wanted computer power, so AWS H100 GPU prices went up. It also became harder to get these GPUs. When AI got cheaper, people used more hardware. Weiss Ratings said the market reacted too much at first. But DeepSeek made other companies and investors change their plans.
Experts at Stanford HAI said DeepSeek’s new ideas and open-source style made other companies work faster. Now, the AI industry is changing quickly and competition is strong.
Adoption Trends
DeepSeek became popular very fast in the AI world. It was the top free app in US app stores. Over 700 open-source versions were made by the community and groups. Big tech companies like Microsoft, AWS, and Nvidia started using DeepSeek v3.1. This shows that businesses are changing how they use AI.
Bain’s report says DeepSeek’s cheap training and use caught the eye of business leaders.
Companies are now looking for AI models that cost less and work better.
Cloud companies are spending money to get ready for more AI use.
Even though some new models are slow to come out, DeepSeek keeps growing. Companies are changing how they build AI and spend money on it.
DeepSeek’s longer context window helps with better conversations. Users can ask bigger questions and get more info at once. DeepSeek is now a strong rival to big US AI companies. The AI industry now has more choices that are easy to get and not expensive.
Geopolitical Influence
DeepSeek made global AI competition stronger. DeepSeek-R1 is an open-source model from China. It can do what US models do but costs less. US security experts worry about losing their lead in AI. DeepSeek helps China share AI that fits its goals. This could change who has power in the world.
The race between the US and China is moving faster. DeepSeek uses fewer resources but still thinks well. This makes the AI race even tougher. The European Union sees DeepSeek as a way to use smaller models. They do not want to spend money on huge computer systems. DeepSeek’s smart design lets it work well even without the best Nvidia chips.
The US made strict rules to stop sending chips and AI tech to China.
China is making its own chips and wants to control its digital future.
Italy started checking DeepSeek for privacy problems.
DeepSeek is banned on Canadian government devices and in Apple stores for safety reasons.
Italy, Australia, and Taiwan also banned DeepSeek.
OpenAI said DeepSeek stole ideas and used secret methods without permission.
Cybersecurity experts say developers and rule-makers need to work together. They warn that open-source AI like DeepSeek lets more people use AI but can be risky if used wrong.
Now, the world has many different AI rules. The US, China, and other places have their own ways. Big companies must follow lots of rules and watch out for risks. DeepSeek’s rise showed US tech companies why AI leadership matters. It also showed how important AI will be in the future.
DeepSeek v3.1 changes ai by making strong models easy to get. It costs less and is open-source, so more people can use it. Groups save money and get tools that match big companies.
Teams can test, change, and use the model on their own computers. This helps them try new ideas and follow good rules.
Working together and sharing ideas helps DeepSeek grow fast. But users should be careful with data and use ai in the right way.
In the future, more people will use ai. New ideas will come faster, but there will be new problems as open-source models change how countries compete.
FAQ
What makes DeepSeek v3.1 different from previous versions?
DeepSeek v3.1 has more parameters than before. It can handle much longer text at once. The MIT license lets people use it in many ways. The Mixture-of-Experts design makes it faster and more accurate.
How can organizations deploy DeepSeek v3.1 securely?
Groups can set up DeepSeek v3.1 on their own servers. They can also use cloud services to run it. The open-source license gives teams control over their data. Teams can change security settings to fit their needs.
Tip: Always check your security rules before using any AI model.
Is DeepSeek v3.1 suitable for coding tasks?
DeepSeek v3.1 does well in coding tests. It works with many languages. Developers can use it to write, fix, and translate code easily.
What hardware does DeepSeek v3.1 require for training?
DeepSeek v3.1 needs about 2,048 NVIDIA H800 GPUs to train. This is fewer GPUs than other big models need. Research teams can use it more easily because of this.
Model | GPUs Needed | Training Cost |
DeepSeek v3.1 | 2,048 | $5.6M |
Llama 3.1 405B | 16,000+ | $60M |
Can DeepSeek v3.1 be used for long documents?
Yes. DeepSeek v3.1 can work with 128K tokens at once. People can read, sum up, and look at long documents. They do not lose important information.
