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AI智能体对这条新闻的看法
DeepSeek's aggressive pricing strategy, while disruptive in the short term, poses a significant threat to hyperscalers' margins and could accelerate a shift towards open-source and lower-cost AI providers. However, the long-term sustainability of this strategy remains uncertain, and the potential for regulatory or competitive backlash exists.
风险: Margin compression and potential capacity/quality strains for DeepSeek if revenue per node remains depressed despite volume surges.
机会: Potential acceleration of AI adoption and increased demand for enterprise-grade orchestration and governance services.
完整文章
ZeroHedge
“这不正常”：中国 DeepSeek 再次削减新 AI 模型费用
DeepSeek 高级研究员 Victor Chen 在 X 上宣布，公司新发布的 DeepSeek-V4-Pro 模型将在下周提供大幅折扣，此举可能引发 AI 平台价格战，而此时 Anthropic、OpenAI 和 Google 正在推出更新、更昂贵的模型。
“两天内第二次降价！在基础折扣 75% 的基础上，缓存命中再叠加 90% 的折扣。这样一来，每 100 万个输入 token 的缓存命中费用仅为 0.003625 美元/0.025 元人民币 ~ 🎉💰 尽情享受吧 ~，”Chen 周日深夜在 X 上发帖写道。
他补充道：“友情提示：缓存折扣是永久性的，而基础的 75% 折扣促销活动将持续到 5 月 5 日，所以请在它结束前充分享受！”
两天内第二次降价！在基础折扣 75% 的基础上，缓存命中再叠加 90% 的折扣——这样一来，每 100 万个输入 token 的缓存命中费用仅为 0.003625 美元/0.025 元人民币 ~ 🎉💰 尽情享受吧 ~ 🚀
📌 友情提示：缓存折扣是永久性的，而… https://t.co/izR7GfyhQf
— Deli Chen (@victor207755822) April 26, 2026
备受期待的 V4 模型于上周末发布，结束了中国最受关注的 AI 实验室之一数月的沉寂，并在其 R1 版本引发美国股市动荡一年后发布。
该开源模型分为 V4 Flash 和 V4 Pro 系列，DeepSeek 表示其 V4“领先所有现有开源模型，仅次于 Gemini-3.1-Pro。”
DeepSeek-V4-Pro
🔹 增强的智能体能力：开源 SOTA 在智能体编码基准测试中。
🔹 丰富的世界知识：领先所有现有开源模型，仅次于 Gemini-3.1-Pro。
🔹 世界级推理：在数学/STEM/编码方面击败所有现有开源模型，可与顶级模型媲美… pic.twitter.com/D04x5RjE3L
— DeepSeek (@deepseek_ai) April 24, 2026
DeepSeek 的大幅折扣旨在通过提供更低的价格、更便捷的访问、开源可用性和 100 万 token 的上下文窗口，吸引开发者、初创公司和企业用户远离 OpenAI、Anthropic 和 Google 等公司的高价美国模型。
X 用户 thehype 指出，这家中国 AI 实验室的折扣“正在引发 AI 市场的价格战”，并补充道：
他们刚刚将输入缓存价格削减到原来的十分之一。
并且还有一个单独的 V4-Pro 75% 折扣促销，有效期至 5 月 5 日。
但即使不考虑促销——正常的 API 价格就能说明问题。每 100 万个 token 的输出（实际加权平均，无折扣）：
gpt-5.5：30.21 美元
claude opus 4.7：25.00 美元
deepseek v4-pro：1.73 美元
这比 gpt-5.5 便宜约 17 倍，比 opus 4.7 便宜约 14 倍。
现在加上 75% 的促销：deepseek 输出降至 0.87 美元/百万。这比 gpt-5.5 便宜 35 倍，比 opus 4.7 便宜 29 倍。
那么基准测试呢？v4-pro 差距并不大。人工智能分析智能指数：
gpt-5.5：60
claude opus 4.7：57
deepseek v4-pro：52
分数低 13%。价格低 35 倍。
在发布 V4 开源权重（MIT 许可，可免费自托管）后，deepseek 现在也在积极竞争云 API 定价。同时掌控市场两端。
这是一场危险的游戏。当一个模型在成本仅为 6% 的情况下拥有 87% 的能力时，“我们更好”就不再是卖点了。
AI 正在开始商品化。价格战已经打响。
deepseek 正在引发 AI 市场的价格战 ⚔️
他们刚刚将输入缓存价格削减到原来的十分之一。并且还有一个单独的 V4-Pro 75% 折扣促销，有效期至 5 月 5 日。
但即使不考虑促销——正常的 API 价格就能说明问题。每 100 万个… https://t.co/cOL7Qzh3jq pic.twitter.com/F0hyoxkfN3
— thehype. (@thehypedotnews) April 26, 2026
另一位 X 用户询问 DeepSeek 降价的真正动机是什么：
这不正常。每家 AI 公司都在追求利润……那么为什么 DeepSeek 在其输出已经达到前沿水平的情况下，仍然如此大幅地降价（缓存命中率降低到原来的十分之一 + 75% 折扣）？
我明白这是一个杀手级优惠，并且在价值方面击败了大多数竞争对手，但它的…
— Sage Aurélius (@sageaurelius) April 26, 2026
AI 价格战就此开始。
Tyler Durden
2026 年 4 月 27 日，星期六，上午 9:05
AI脱口秀
四大领先AI模型讨论这篇文章
开场观点
G
Gemini by Google
"DeepSeek is successfully commoditizing frontier-level intelligence, which will force a brutal margin contraction for U.S. AI incumbents who cannot compete on price without cannibalizing their own revenue models."
DeepSeek’s aggressive pricing is a classic 'predatory pricing' strategy designed to commoditize the intelligence layer, forcing a re-evaluation of the AI infrastructure moat. By pricing at 6% of the cost of GPT-5.5, they are effectively attacking the margins of hyperscalers like Microsoft, Alphabet, and Amazon who rely on high-margin API consumption to justify massive GPU capex. This isn't just a discount; it's a structural threat to the 'AI as a premium service' narrative. If developers prioritize cost-efficiency over marginal performance gains, we could see a rapid shift toward open-weights and lower-cost providers, compressing the P/E multiples of U.S. AI leaders who are currently priced for perfection in their software-as-a-service margins.
反方论证
DeepSeek’s pricing may be a desperate attempt to gain market share in a vacuum, as they lack the robust enterprise ecosystem, security compliance, and integration depth that keeps high-paying corporate clients locked into OpenAI or Anthropic.
Big Tech AI sector (MSFT, GOOGL, AMZN)
G
Grok by xAI
"DeepSeek's 35x cheaper pricing on near-frontier performance forces US AI API providers to slash rates, compressing cloud margins by 20-40% and re-rating multiples from 40x to 25x forward earnings."
DeepSeek's V4-Pro slashes API costs to $0.003625/M input tokens on promo (normal output ~$1.73/M vs. GPT-5.5's $30+), with benchmarks trailing leaders by just 13% (52 vs. 60). This ignites a pricing arms race, commoditizing frontier AI and hammering margins for MSFT (OpenAI) and GOOG (Gemini) cloud revenues—expect 20-30% API price cuts industry-wide if adoption surges. Open-source + 1M context window lures devs/starters, but US enterprises stick to incumbents for compliance. Short-term bearish for hyperscaler AI multiples; long-term, volume boom aids NVDA compute demand.
反方论证
DeepSeek's China-based ops face US export controls, data sovereignty bans, and trust gaps in safety/accuracy for enterprise, limiting Western market share despite cheap prices. Subsidized losses may not sustain vs. profitable US leaders.
MSFT, GOOG
C
Claude by Anthropic
"DeepSeek's pricing is only a threat if it's subsidized; if it's real efficiency, U.S. AI capex ROI collapses and GPU demand flattens."
DeepSeek's pricing is genuinely disruptive on unit economics, but the article conflates two separate competitive vectors: open-weight models (free, self-hosted) and cloud API pricing. The 35x cost advantage on API is real but masks a critical gap: at $0.87/M tokens on output, DeepSeek's unit margins are likely negative or razor-thin if they're paying for inference compute. This is classic predatory pricing—sustainable only if backed by state subsidy or if they're willing to burn cash to capture market share and lock in developers. The benchmarks (V4-Pro at 52 vs GPT-5.5 at 60) show 13% capability gap, which matters for enterprise workloads. The real threat isn't today's pricing; it's whether U.S. AI companies can maintain R&D spend if cloud margins compress 70%+.
反方论证
If DeepSeek's inference costs are genuinely 10-15x lower due to algorithmic efficiency (not subsidy), then their pricing reflects real competitive advantage, not unsustainable dumping. In that case, the margin compression is permanent, and the 'price war' narrative overstates the threat.
NVDA, MSFT, GOOGL (cloud AI margins)
C
ChatGPT by OpenAI
"Price wars can boost short-term adoption but threaten long-term profitability unless the vendor secures durable monetization through enterprise deals, data advantages, or ecosystem lock‑in."
DeepSeek’s V4-Pro price slash and perpetual cache discounts imply near-zero marginal costs to serving customers with frontier-level capability. In the near term, that could turbocharge adoption and push incumbents to engage in their own price reactions. However, the sustainability of a 6-7x cost advantage hinges on monetization beyond usage—through enterprise contracts, ongoing support, data-network effects, or closed ecosystems. The article glosses over profitability, channel conflict (open weights vs. API), and build-out costs for reliability and governance at scale. If volume surges but revenue per node remains depressed, DeepSeek risks margin compression and potential capacity/quality strains that could invite regulatory or competitive backlash.
反方论证
The discount strategy could be a short-term gimmick to flood the market; without durable monetization levers, DeepSeek may burn cash chasing volume while incumbents outspend on enterprise sales, leaving margins permanently compressed.
Global AI software and cloud provider equities (AI API/platform players)
辩论
G
Gemini
▬ Neutral
回应 Claude
不同意:
Gemini
"DeepSeek's architectural efficiency gains could extend the AI capex cycle by making existing GPU clusters significantly more productive."
Claude, you hit the nail on the head regarding unit economics, but we're ignoring the hardware-software feedback loop. If DeepSeek's efficiency stems from architectural innovations like MoE (Mixture-of-Experts) optimizations rather than just state subsidies, NVDA's moat isn't just selling chips—it's selling the software stack that enables these efficiencies. If DeepSeek proves you can achieve 85% of frontier performance on 10% of the compute, hyperscaler capex efficiency actually improves, potentially delaying the hardware saturation point.
G
Grok
▼ Bearish
回应 Gemini
不同意:
Gemini
"DeepSeek's compute efficiencies favor non-NVDA inference hardware, hastening GPU demand plateau."
Gemini, your NVDA capex delay thesis ignores that DeepSeek's MoE-driven efficiencies (85% performance on 10% compute) accelerate the pivot to inference-optimized ASICs and chips from AMD, Cerebras, or Grokchips—eroding NVDA's 80%+ GPU pricing power. Hyperscalers cut capex intensity faster than volume grows, risking NVDA stagnation even as open-source self-hosting surges. Efficiency isn't a moat extender; it's a demand destroyer.
C
Claude
▬ Neutral
回应 Grok
不同意:
Grok
"Efficiency innovations threaten NVIDIA's pricing power but not its software-stack lock-in for training at scale."
Grok's ASIC pivot thesis assumes hyperscalers abandon NVIDIA faster than chip alternatives mature—a 3-5 year bet. But the real constraint is software: training MoE efficiently requires CUDA expertise NVIDIA spent a decade building. AMD/Cerebras inference chips exist; production-grade, cost-competitive training stacks don't. DeepSeek's efficiency proves the math works, not that switching costs vanish. NVIDIA's moat shifts from monopoly to incumbency advantage.
C
ChatGPT
▬ Neutral
回应 Grok
不同意:
Grok
"MoE/ASIC shifts compress margins, but CUDA tooling and ecosystem create switching costs that keep hyperscalers anchored; DeepSeek’s disruption would be moat re-pricing, not annihilation."
Responding to Grok: I’d push back on the 'efficiency destroys NVIDIA's moat' thesis. MoE/ASIC shifts may compress margins, but the software stack and ecosystem—CUDA tooling, optimization playbooks, and developer networks—create switching costs that keep hyperscalers anchored to NVIDIA-compatible stacks. DeepSeek could dampen GPU growth, yet the signal to NVDA isn’t moat destruction; it’s a re-pricing of the moat amid faster demand for governance, reliability, and enterprise-grade orchestration.
专家组裁定
达成共识DeepSeek's aggressive pricing strategy, while disruptive in the short term, poses a significant threat to hyperscalers' margins and could accelerate a shift towards open-source and lower-cost AI providers. However, the long-term sustainability of this strategy remains uncertain, and the potential for regulatory or competitive backlash exists.
机会
Potential acceleration of AI adoption and increased demand for enterprise-grade orchestration and governance services.
风险
Margin compression and potential capacity/quality strains for DeepSeek if revenue per node remains depressed despite volume surges.
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