DeepSeek's latest price hike has left many users questioning whether they will continue to pay for its services. On the morning of August 6, a new announcement from DeepSeek surprised many users, stating plans for a significant overall increase in API service pricing, expected to be substantial. A well-known AI blogger, "Digital Life Katzke," shared on social media, noting that over the past few years, domestic models have been fiercely competing on price, with DeepSeek being the most aggressive. Its low costs allowed developers to deploy AI agents at scale, burning through tokens for product development.

While logically, given the high costs of training and reasoning, a price increase is reasonable, emotionally, many feel betrayed. This post quickly resonated among developers, sparking hundreds of comments within hours. Key questions include where the price increase boundary lies, whether DeepSeek remains viable, and what alternatives exist. Some fans even joked about Liang Wenfeng's "AI for all" image being shattered, humorously demoting him from a "saint" to a "prisoner."

Over the past two years, DeepSeek's pricing has been a key indicator of the "price war" in the domestic large model industry. In April 2024, it set a floor price of 1 yuan per million input tokens and 2 yuan per million output tokens, driving competition to the bottom. After a promotional period ended in February 2025, the first price adjustment occurred. By July, the V4-Flash official version was launched at similar rates. DeepSeek repeatedly pushed prices below cost, until both peers and users grew accustomed to these "fracture-level prices." Now, the scale is being recalibrated.

The real issue lies in the phrase "substantial increase" in the announcement. How large is substantial? This not only concerns DeepSeek's pricing strategy but also the direction of the entire large model industry. The phase of "subsidizing for scale" may be ending. DeepSeek's ultimate test will be whether its core competitiveness lies in extreme cost-effectiveness or model strength, and the market will provide new answers.

From 'Floor Price' to 'Substantial Increase': A Shortage of Computing Power

According to a senior executive at a vertical large model product company, the core reason for the API price increase is a widespread shortage of computing power. On August 4, the overseas open-source AI coding platform OpenCode reported that DeepSeek-V4-Flash's API had frequent response timeouts, rendering it nearly unusable, attributed to unprecedented access volume. Public data from OpenRouter shows DeepSeek-V4-Flash topped global model calls in the week from July 27 to August 2, with 7.22 trillion tokens processed, surpassing the total of over 400 models on the platform.

On OpenCode, a platform for developers, token processing on August 1 reached 8 trillion tokens, with 5 trillion from free trials. One model's daily consumption exceeded the total volume of platforms hosting hundreds of models. This scale of calls places immense pressure on computing infrastructure, with reasoning costs including hardware depreciation, electricity, and maintenance. With token processing in the trillions, even low marginal costs per call add up to a huge expense. More importantly, DeepSeek's call structure is changing. The V4 series supports 1 million token context windows and deeper chain-of-thought reasoning, meaning calls involve complex tasks with deep agent loops, not simple Q&A. Token consumption per task has surged from under 2,000 in the conversational era to 500,000 to 1 million tokens.

Industry insiders estimate that DeepSeek-V4-Flash's previous pricing of 1 yuan per million input tokens and 2 yuan per million output tokens likely fell below actual costs, resulting in negative gross margins. According to benchmarks from Artificial Analysis in late July, the cost per call for major models varies significantly: DeepSeek-V4-Flash at $0.03, Kimi K3 at $0.86, GPT-5.6 Sol at $1.86, and Claude Fable 5 at $3.15. This means DeepSeek's price is about 1/105th of Claude Fable 5. However, some industry experts question the reference framework for these comparisons, noting that V4-Flash should be compared to Claude Opus 4.8, not Kimi K3 or Fable 5. The pricing for Opus 4.8 per million output tokens is roughly 85 times that of V4-Flash.

How Much Will Prices Rise, and Who Is Calculating?

The price increase is now a given, but the market is anxious about the exact magnitude. A senior executive at a top five domestic cloud vendor speculated that even after a price hike, DeepSeek will remain competitive, with a maximum possible increase of three times. Different increase ranges lead to different business outcomes. An increase of 100% to 200% is seen as a moderate range, unlikely to cause significant user churn. Reports suggest that Liang Wenfeng previously stated in investor meetings that within very low price ranges, developers show almost zero price elasticity, meaning even a doubling of prices would not significantly reduce token consumption. A 300% increase would shift logic, potentially raising gross margins from negative to 30% to 40%, achieving independent profitability, while also filtering out low-value crawler scripts and "free-loading" traffic, improving system stability.

However, a 400% increase could be a turning point. DeepSeek's input price would rise to 5 yuan per million tokens and output to 10 yuan. While still below major domestic competitors, which typically charge 5 to 10 yuan per million input tokens and 10 to 30 yuan per million output tokens, the price advantage would shrink from a 10-fold gap to a 2-fold gap. When this gap no longer covers switching costs, frequent users may evaluate alternative models. User feedback confirms this threshold. A researcher noted that while a doubling of API prices is acceptable, any further increase would prompt a switch. An agent developer was more lenient, saying a doubling is acceptable, given DeepSeek's "fracture-level" pricing, but highlighted its lack of multimodal capabilities as a weakness. For enterprise clients, the price increase has minimal impact, as they use self-deployed models on private networks, with costs limited to computing and electricity. The API price increase primarily affects small and medium-sized developers, startup teams, and personal projects.

After the 'Price War' Pause

The price war initiated by DeepSeek has been paused by the company itself, with timing that is noteworthy. The day before DeepSeek's announcement, Meta founder Zuckerberg announced the open beta of its first AI coding agent, Muse Code, priced at $4.25 per million output tokens, with contributor subscriptions as low as $0.20. One is lowering prices to attract users, while the other is raising prices to prepare for the future, illustrating diverging paths in global large model commercialization. Industry insiders believe this price adjustment for DeepSeek goes beyond pricing strategy. Reports indicate that DeepSeek recently completed its first round of financing worth 51 billion yuan, with Tencent, CATL, NetEase, JD.com, and IDG Capital among the participants, along with strategic investment from the National AI Industry Investment Fund. A second round of financing has also begun, aiming to raise 50 billion yuan, with a pre-money valuation of around 500 billion yuan, expected to be finalized by late August. If both rounds succeed, DeepSeek will have raised over 100 billion yuan in less than five months.

However, financing is not unique to DeepSeek. A product head at a leading large model company noted that all model companies are losing money, at an unprecedented scale. Despite the price increase wave, losses are still widening, albeit at a slower pace. Yet, no one wants to abandon this visible future, leading even reluctant companies to consider IPOs for survival. The improved cash flow from the price increase will directly address investor concerns about business sustainability, especially since DeepSeek's API business has long operated at a loss, relying on financing. Deeper changes involve a re-anchoring of industry pricing systems. Over the past two years, DeepSeek's "price slasher" strategy significantly compressed profit margins for domestic competitors. As long as it maintained floor prices, others dared not raise prices. Now, DeepSeek's exit from the price war frees up pricing space for the entire industry. Goldman Sachs noted in a recent report that robust demand for domestic AI models, coupled with tightening computing resources, is shifting industry competition from aggressive price wars toward a more rational pricing framework. However, DeepSeek's core competitiveness relies on extreme cost-effectiveness. If price advantages are no longer extreme, the question remains whether developers will pay higher prices for the same technology. Long-term challenges include computing power shortages, high-end chip bans, and cost pressures, which remain difficult equations in DeepSeek's commercialization path. Reports from July suggest DeepSeek has secretly launched a self-developed AI chip project focused on reasoning chips to reduce reliance on external suppliers and significantly lower computing costs, adding another option to its procurement negotiations.