DeepSeek's permanent 75% price cut and the industry's new cost floor

Core findings

On 24 May 2026 a post on r/OpenAI took 1290 points and 251 comments. Its title was "DeepSeek just popped the American AI bubble". That claim is a user's view, not this article's conclusion — but it is worth taking apart, because what the user was actually comparing, under the talk of popped bubbles, was the price per million tokens.

The same day, another post on r/Anthropic, "I think I know why deepseek is so good", took 1360 points and 159 comments, labelled Negative. Both posts point at the same core issue: pricing.

The price butcher: from "the Pinduoduo of AI" to a permanent cut

Within the Reddit monitoring window of 28 April to 29 May 2026, DeepSeek's highest-engagement topic was Pricing_and_Cost, with post_count=375, total_score=20414 and nss=20.8 (C1). That figure sits far above the others — second-placed Model_Capabilities totalled 13259, a clear gap.

External research confirms the trigger behind the Reddit discussion. On 22 May 2026, DeepSeek announced a permanent 75% reduction in V4-Pro API pricing: cache-hit input fell to RMB 0.025 per million tokens, cache-miss input to RMB 3 per million, and output to RMB 6 per million. According to Jiemian News citing DeepSeek's own disclosure, if all tokens were priced as R1, theoretical daily profit would reach $475,000 at a cost-profit margin of 545% (C1 Jiemian: DeepSeek financing and pricing analysis). That figure comes from Jiemian citing DeepSeek's own disclosure and has not been independently verified.

The cut was not a temporary promotion. MarsBit reported that DeepSeek took what had been a short-term promotion at 25% of list price (a 75% cut) and made it long-term official pricing, effective worldwide at the same time (C1 MarsBit: DeepSeek permanent price cut analysis). The backdrop to that decision: AI companies globally were raising prices, and OpenAI released GPT-5.5 on 24 April with API pricing doubled, to $5 per million input tokens and $30 per million output. DeepSeek shipped the V4 series 12 hours later, fully open source under MIT, with V4-Flash output at just $0.28 per million tokens.

Cost structure: not charity, technical overwhelm

The ability to cut price comes from breakthroughs at three levels — the same cost discipline later shaped the execution framework it open-sourced.

The first is the MoE architecture. DeepSeek V4 Pro has 1.6 trillion total parameters but activates only around 49B per inference — roughly 3%. According to an article on Eastmoney's Caifuhao self-media platform citing Artificial Analysis benchmarks, training cost is thereby compressed to 1/14 of comparable models (C1 Eastmoney: DeepSeek cost structure analysis). That source is Caifuhao, a self-media platform, and its data comes from third-party benchmarks it cites; it has not been independently verified.

The second is the hybrid attention mechanism. In a 1M-token context, V4 Pro's per-token inference compute is only 27% of V3.2's, and KV cache footprint only 10% of what it was.

The third is end-to-end adaptation to the Ascend 950, Huawei's AI accelerator line. The MarsBit article argues that DeepSeek's low-price capability comes from systematic cost control from the physical layer up through software, including the price of green power in western China (around RMB 0.2–0.3 per kWh, a fifth to a quarter of US and European rates) and a cost-performance advantage in AI talent (C1 MarsBit: systematic cost control). The claim that prices will fall further once Ascend 950 superpods ship in volume comes from the MarsBit article citing a DeepSeek technical report; the factor is not mentioned in the Reddit discussion.

A 51CTO blog post citing Reuters says: "DeepSeek's permanent price cut has broken the pricing structure of high-end large models globally, and by being first to compress cost through technical optimisation, has opened a new round of global AI price competition." (C1 51CTO: Reuters report cited) That source is a 51CTO blog, an industry outlet, and the Reuters original it cites is not directly verified here.

The flashpoint: that negative post on r/Anthropic

Back to the post that took 1360 points on r/Anthropic, "I think I know why deepseek is so good". It is labelled Negative, which means the author was not praising DeepSeek. And it was posted on r/Anthropic — Anthropic's own community.

A note on the post's contents: external research could not obtain the post's full text. What follows is inference from contemporaneous Reddit discussion, not confirmation of that post's specific claims: a post on r/DeepSeek at 951 points, "The Great Leader Liang Wenfeng Brought Down the Price of DeepSeek V4 with a Single Line of Code", hints at adulatory community discussion of founder Liang Wenfeng's personal decisions, while a 742-point post on r/LocalLLaMA, "DeepSeek is pushing forward with $10.29 billion financing round", points at the relationship between financing and pricing strategy. These contemporaneous posts have no citation or content link to that post and cannot serve as evidence of what it discussed.

On the C2 verification: external search found no independent source directly supporting the post's specific claims. It is a single high-traffic post and cannot by itself prove a broad trend or a knock-on effect from another event. Its high engagement proves only that the post drew attention within the r/Anthropic community; it does not support an inference about consensus across Reddit or the AI industry.

The financing–pricing paradox

The financing structure is worth taking apart. A Sohu article detailed the shareholder list: founder Liang Wenfeng contributed RMB 20 billion personally, Tencent RMB 10 billion, CATL RMB 5 billion, and JD.com, NetEase and IDG Capital RMB 3 billion each. The national AI industry investment fund put in RMB 1 billion and is the only institution holding voting rights and exempt from the five-year lock-up (C1 Sohu: financing structure analysis).

Liang Wenfeng used personal funds to secure absolute control of the company, and the outside capital is essentially financial investment. But the moment capital enters, it defines a path from here to the future — a company valued at $50 billion cannot only burn money and never collect it.

Five weeks later, on 29 June 2026, DeepSeek announced peak-and-trough pricing: API calls cost double during weekday daytime hours. A Juejin article recorded the timeline in detail: the V4 preview went live on 24 April, emergency repricing followed on 26 April, the permanent cut was announced on 22 May and took effect on 1 June, peak pricing was announced on 29 June, and the full release landed in mid-July (C1 Juejin: pricing timeline).

The "permanent cut" is real — off-peak prices are unchanged. "Double at peak" is also real — weekdays 9:00–12:00 and 14:00–18:00 cost twice as much. The two do not contradict each other, but read together, DeepSeek's pricing strategy has moved from floor pricing to win market share toward peak-and-trough pricing to optimise utilisation.

Industry impact: the community's "kill line" framing

The Eastmoney Caifuhao article quotes a line circulating in the community: "Codex and Claude set the ceiling, DeepSeek sets the kill line — match it and you have no premium, fall behind it and you are out." (C1 Eastmoney: the kill-line concept) That framing comes from community opinion quoted in a Caifuhao article and is not this article's industry conclusion. No independent data or case study was found to show that DeepSeek's pricing has in fact produced a kill-line effect.

A Sina Finance article analysed the industry's response: consumer products raised prices against the trend (Doubao, ByteDance's assistant, launched paid tiers at RMB 68 a month for standard and RMB 500 for professional), while B2B vendors were forced to react (Tencent Cloud announced cuts to its whole DeepSeek-V4 interface price range, close to halving them). The article argues DeepSeek's low-price strategy is not destructive dumping but a push toward more mature tiered pricing across the industry (C1 Sina Finance: industry pricing analysis).

An article from the DeepSeek technical community notes that V4-Pro-Max edges past the leading open-source models but still trails GPT-5.4 and Gemini-3.1-Pro, by roughly three to six months (C1 DeepSeek technical community: performance gap). Which means DeepSeek's competitiveness comes mainly from the cost side, not from an absolute performance lead.

Closing

DeepSeek's pricing strategy has already moved the global AI industry's cost baseline. From a permanent cut to peak-and-trough pricing, from "the Pinduoduo of AI" — a reference to the Chinese discount e-commerce platform — to a $50 billion valuation, the core question facing this company is whether Liang Wenfeng's narrative of staying open source and pursuing AGI can hold up under capital pressure. User-behaviour data after peak pricing takes effect, actual cost changes once Ascend 950 superpods ship, and competitors' pricing adjustments are the indicators most worth watching next.

FAQ

Is DeepSeek's 75% price cut permanent or a promotion? Permanent, in the sense that it has no expiry date. On 22 May 2026 DeepSeek converted what had been a short-term promotion at 25% of list price into long-term official pricing, effective worldwide simultaneously: cache-hit input at RMB 0.025 per million tokens, cache-miss input at RMB 3 per million, output at RMB 6 per million.

Does the permanent cut mean DeepSeek is always cheaper now? Not at every hour. Five weeks later, on 29 June 2026, DeepSeek announced peak-and-trough pricing that doubles API costs on weekdays from 9:00 to 12:00 and 14:00 to 18:00. Off-peak prices are unchanged, so both statements are true at once — the strategy shifted from floor pricing for market share toward pricing that manages utilisation.

How can DeepSeek price this low? Three technical levels, per the sources cited here. The MoE architecture holds 1.6 trillion total parameters but activates only about 49B per inference, roughly 3%, which Artificial Analysis benchmarks cited by Eastmoney put at 1/14 the training cost of comparable models. Hybrid attention brings per-token inference compute in a 1M-token context to 27% of V3.2's, with KV cache at 10% of its former footprint. And end-to-end adaptation to Huawei's Ascend 950 sits alongside input-cost advantages such as green power in western China at around RMB 0.2–0.3 per kWh. Note that the cost figures come from self-media and industry outlets citing third parties, not from independent verification.