Zhipu and MiniMax burn $10 for every $1 in revenue
Two Chinese AI companies going public reveal stark differences in monetization, offering first hard data on what works in the world’s most competitive AI market.
Zhipu derives 85% revenue from enterprise on-premise deployment at 59% margins, MiniMax gets 71% from consumer apps
Both companies lose over $10 for every $1 earned, burning through capital faster than revenue grows
DeepSeek triggered 92% API price collapse in 2024, forcing both to pivot business models before IPO
Market valuations at less than 10% of US peers
Zhipu AI and MiniMax listed on the Hong Kong Stock Exchange on January 8 and 9, 2026, becoming the first foundation model companies to go public globally. The simultaneous debuts beat OpenAI and Anthropic to market by months. More importantly, their prospectus filings expose financial realities that Chinese AI startups have kept private for years.
The two companies represent opposite bets on AI monetization.
Zhipu generated 85% of H1 2025 revenue from selling on-premise model deployments to state-owned enterprises and financial institutions, according to its prospectus.
MiniMax derived 71% of revenue from consumer applications, with its Talkie AI companion app and Hailuo video generator driving growth in overseas markets.
Neither path shows clear signs of sustainability. But the data reveals which model comes closer to working.
Two divergent bets on AI monetization
Zhipu AI, officially Knowledge Atlas Technology, emerged from Tsinghua University’s computer science department in 2019. Co-founders Tang Jie and Li Juanzi built the company around their academic research in knowledge graphs and large language models.
The business model reflects its origins. Zhipu’s on-premise deployment revenue reached RMB 162M in H1 2025, representing 84.8% of total revenue of RMB 191M ($27M), according to prospectus data.
These deployments serve clients requiring strict data sovereignty—central state-owned enterprises, banks, and government agencies that cannot use cloud-based AI services.
The gross margin on this business is substantial. On-premise deployment achieved 59.1% gross margins in H1 2025, well above traditional enterprise software. The model packages AI capabilities as licensed software plus technical services, commanding premium pricing.
Zhipu’s cloud API business tells a different story. The segment generated just RMB 29M in H1 2025, representing 15.2% of revenue. More concerning, gross margins turned negative at -0.4% after operating at a thin 3.4% in 2024, according to industry analysis.
This reflects China’s brutal API pricing environment where DeepSeek offers inference at $0.14 per million tokens.
MiniMax chose consumer applications over enterprise services. Founded in December 2021 by former SenseTime vice president Yan Junjie, the company initially launched Glow, an AI character roleplay app, in October 2022. Four months later, the app had 5M users.
Regulatory pressure forced a pivot. MiniMax repackaged Glow as Talkie for international markets, targeting the US and Singapore.
The strategy worked. Talkie ranked 5th among free entertainment apps in the US by June 2024, surpassing Character.AI in downloads, according to company filings.
Consumer applications generated 71% of MiniMax’s 9-month 2025 revenue of $53.4M. Talkie contributed 35% of total revenue, while Hailuo AI video generator added another 33%. The remaining 29% came from B2B API access, according to prospectus data.
The geographic split matters. MiniMax derived 73.1% of revenue from overseas markets as of September 2025, with the US and Singapore as top markets. This international focus insulates the company from domestic price competition but exposes it to regulatory risk.
Disney, Universal, and Warner Bros. filed a $75M copyright lawsuit against MiniMax in September over Hailuo-generated content allegedly violating intellectual property.
The profitability mirage behind impressive margins
Both companies present gross margin figures that suggest viable unit economics. The reality is more complex.
Zhipu reported overall gross margins of 50% for H1 2025, driven primarily by its on-premise business. But revenue growth cannot keep pace with R&D spending. The company posted revenue of RMB 191M against R&D expenses of RMB 1.595B in H1 2025, producing an R&D expense ratio of 835%, according to financial analysis.
Approximately 70% of R&D spending goes directly to computing power costs. Zhipu burned RMB 1.145B on GPU clusters in H1 2025 alone.
The net loss reached RMB 2.36B ($330M) on revenue of RMB 191M ($27M), a loss ratio exceeding 12:1.
Annual trajectory shows the problem. Revenue grew from RMB 57M in 2022 to RMB 312M in 2024, a 130% CAGR. But 2024 R&D spending hit RMB 2.2B ($315M), representing 703% of revenue, according to prospectus disclosures.
For every yuan earned, Zhipu reinvested over eight yuan into model development.
MiniMax shows better trajectory but remains deeply unprofitable. The company achieved overall gross margins of 23.3% in the first nine months of 2025, up from 12.2% in 2024 and turning positive after -24.7% in 2023, according to financial filings.
The B2B segment drives margin improvement. API and enterprise services achieved 69.4% gross margins in 9M 2025, far exceeding industry averages.
This suggests MiniMax achieved significant technical advantages in inference cost control, likely through its Mixture of Experts architecture and custom attention mechanisms.
But losses remain staggering. MiniMax reported $512M in net losses on $53.4M revenue in 9M 2025, a loss ratio approaching 10:1. R&D spending reached $180M, representing 337% of revenue.
The company spent over $150M on cloud computing bills in 2025, with total infrastructure and R&D consuming approximately $250M annually, according to industry reports.
Revenue growth provides some optimism. MiniMax grew from $3.5M in 2023 to $30.5M in 2024, reaching $53.4M in 9M 2025, a 130% YoY growth rate.
Monthly active users expanded from 3.1M in 2023 to 27.6M by September 2025, demonstrating rapid global adoption, according to company disclosures.
The critical question is whether revenue can scale faster than compute costs. MiniMax reduced inference costs by 45% over the past year, but gross margins remain at 23% even after these improvements, according to financial data. At current burn rates, neither company approaches breakeven.
Why they went public now and what it reveals
The IPO timing reflects strategic urgency rather than financial readiness. Both companies raced to list before OpenAI and Anthropic begin their public offerings, expected in 2026.
OpenAI is projected to reach a $830B valuation if it completes a $100B fundraising round. Anthropic is valued at approximately $350B following major investments from Amazon and Google, according to market data. These mega-offerings will dominate global AI investment attention and liquidity.
Smaller companies face a closing window. Once US AI giants list publicly, Chinese startups will struggle to secure funding at favorable valuations. The market can absorb multiple billion-dollar IPOs but has limits on total capital allocation to unprofitable AI companies.
Zhipu raised HK$4.35B ($560M) at a HK$116.20 offer price, implying a market valuation of approximately $6.7B. Retail investors oversubscribed the offering 1,159 times, signaling strong Hong Kong appetite for AI exposure, according to exchange data. The stock closed 13.2% higher at HK$131.50 on debut.
MiniMax raised HK$4.8B ($620M) at the top of its HK$151-165 range, pricing at HK$165 per share. Institutional demand forced the company to halt bookbuilding a day early. The stock surged 109% on debut, closing at HK$345 and valuing the company at approximately $13.7B, according to market reports.
Market reception diverged significantly. MiniMax achieved 3x higher first-day gains than Zhipu despite Zhipu’s technically superior models. Zhipu’s GLM-4.7 currently ranks among the top global open-source models, ahead of MiniMax’s best offering, according to benchmark data.
The valuation gap reflects two factors. First, investors prefer consumer growth stories over enterprise deployment models in AI. Second, Zhipu faces regulatory headwinds that MiniMax avoids.
The US Commerce Department added Zhipu to its Entity List in January 2025, restricting access to American semiconductor technology and effectively banning the company from US markets, according to government filings.
The designation cites alleged support for China’s military modernization through research collaborations. MiniMax operates without similar restrictions and actively courts US developers through events in New York, Miami, and San Francisco.
Both companies plan similar capital deployment. Zhipu will allocate 70% of IPO proceeds to R&D for general-purpose AI models.
MiniMax will invest heavily in product development and infrastructure scaling, according to use-of-proceeds statements. Neither prioritizes near-term profitability. The capital provides runway to continue the compute arms race.
The DeepSeek disruption that reshaped everything
DeepSeek fundamentally altered Chinese AI economics in 2024, forcing both IPO companies to pivot their business models months before listing.
On May 6, 2024, DeepSeek released V2 with API pricing at $0.14 per million input tokens, representing a 5-10x reduction from prevailing market rates, according to market analysis. The launch triggered immediate price warfare.
Alibaba slashed its Qwen model pricing from $1.10 to $0.07 per million tokens within days. ByteDance followed with its Doubao model at $0.04 for the lite version. By January 2025, when DeepSeek’s R1 model gained global attention, average Chinese LLM prices had fallen 92% compared to May 2024 levels, according to pricing data.
The impact on API-focused revenue was severe. Companies relying on cloud inference margins saw profitability collapse overnight.
Zhipu’s cloud API gross margins fell from 3.4% in 2024 to -0.4% in H1 2025, making the business economically unviable at scale, according to financial disclosures.
The disruption forced strategic shifts. Zhipu accelerated its enterprise deployment focus, leveraging advantages in custom training and industry-specific implementations where DeepSeek’s standardized API could not compete effectively.
The company’s co-founder Zhang Peng stated publicly that Zhipu aims to increase API business to 50% of revenue, signaling recognition that on-premise deployment alone cannot sustain growth, according to executive statements.
MiniMax pursued differentiation through consumer applications. Rather than competing on API pricing, the company focused on packaged experiences—Talkie for social interaction, Hailuo for video generation—where users pay for outcomes rather than tokens.
This strategy insulated MiniMax from direct price competition while building international brand recognition.
Both companies also accelerated model performance improvements. As of July 2025, 41% of employees at the Six Tigers (China’s elite AI startups including Zhipu and MiniMax) listed job-hunting status, reflecting workforce reductions across the sector, according to employment data. But the survivors emerged with stronger technical foundations.
Zhipu’s GLM-4.5, released July 28, 2025, topped Hugging Face’s global trending list within 48 hours. By September, GLM-4.5 ranked in the top five globally on ChatBot Arena and WebDev Arena, prestigious benchmarks where user votes determine rankings, according to performance data.
The December release of GLM-4.7 achieved coding abilities comparable to Anthropic’s Claude Sonnet 4.5 on mainstream tests.
MiniMax’s M2 model, launched October 2025, claims performance rivaling Claude Sonnet 4.5 while operating at just 8% of the cost, according to company benchmarks.
Earlier, MiniMax-M1 set industry standards with 4M token context windows, positioning the company at the technical frontier.
DeepSeek’s impact extended beyond pricing. The company demonstrated that model training costs could be reduced by an order of magnitude through architectural innovations.
DeepSeek’s V3 model reportedly cost $6M to train, compared to $100M+ for comparable US models, using approximately one-tenth the computing power of Meta’s Llama 3.1, according to technical papers.
This efficiency breakthrough challenged assumptions about AI economics. If DeepSeek’s approach scales, massive capital expenditure may not be necessary to maintain frontier performance.
That proposition threatens the core valuation thesis for capital-intensive US AI giants while potentially leveling the playing field for Chinese startups operating under US chip restrictions.
Compute costs define the real business model
GPU spending dominates AI company economics in ways traditional software never experienced. Understanding compute costs is essential to evaluating whether these business models can work.
Zhipu spent RMB 1.145B ($160M) on computing power services in H1 2025 alone, representing 71.8% of total R&D spending of RMB 1.595B, according to expense breakdowns. This does not include personnel costs for the research teams designing and training models.
Annualized, Zhipu burns approximately $320M on compute while generating $54M in annual revenue at current run rates. The company must grow revenue 6x just to cover GPU costs, before accounting for salaries, infrastructure, or other operating expenses.
US export controls compound the challenge. The Commerce Department’s October 2023 restrictions limit Chinese companies’ access to Nvidia’s H100 and A100 chips, forcing reliance on less powerful H800 alternatives or Chinese-made alternatives from Huawei and domestic manufacturers, according to regulatory analysis.
Companies work around restrictions by renting capacity from cloud providers like Microsoft Azure and Amazon Web Services in Southeast Asian and Middle Eastern regions where controls apply less stringently.
But US lawmakers are pushing to close this cloud loophole, which could substantially increase costs, according to policy reporting.
MiniMax faces similar economics. The company spent over $150M on cloud infrastructure in 2025 while generating $53M in revenue, an infrastructure cost ratio of approximately 280%, according to financial data. Total R&D including personnel reached $180M, or 337% of revenue.
The critical question is whether inference costs decline faster than training costs rise. As models grow larger and more capable, training expenses increase exponentially. GPT-4’s training reportedly cost over $100M. Next-generation models may require $1B+ in compute, according to industry projections.
But inference costs per query have dropped significantly. MiniMax reduced inference costs by 45% year-over-yearthrough architectural optimizations, according to company statements.
DeepSeek’s technical papers describe custom GEMM routines using 8-bit floating point arithmetic that dramatically lower memory requirements and computational load, according to research documentation.
If inference efficiency continues improving while training costs plateau, unit economics may eventually work. Models could reach quality thresholds where incremental performance gains require less additional compute.
Current evidence does not support this optimistic scenario. Companies continue racing to train larger, more expensive models to maintain competitive positioning.
The infrastructure dependency creates strategic vulnerability. Unlike software companies that achieve high gross margins as revenue scales, AI companies face compute costs that scale linearly with usage. Every additional customer, query, or interaction requires proportional GPU capacity. This fundamentally limits margin expansion potential.
Zhipu’s 59% gross margins on on-premise deployment appear sustainable because customers provide their own infrastructure. The company licenses software and provides services, but does not bear ongoing inference costs. This model resembles traditional enterprise software economics.
MiniMax’s 69% gross margins on B2B API services suggest the company achieved architectural efficiencies that reduce inference costs below revenue per query.
But overall margins remain at 23% when consumer applications are included, indicating that infrastructure costs consume the majority of consumer subscription and advertising revenue, according tosegment analysis.
Neither model has proven it can support the $400M+ annual R&D budgets required to remain competitive while also achieving profitability. The IPO capital provides 12-18 months of runway at current burn rates, after which both companies must either raise additional capital or dramatically reduce R&D spending.
What $9B versus $500B valuations actually reveal
Market capitalizations tell a story about investor confidence in business model viability and market positioning.
Zhipu’s ~$9B valuation and MiniMax’s ~$14B valuation combine for approximately $23B in market capitalization, according to trading data.
Together, they represent less than 5% of OpenAI’s reported $500B valuation or 7% of Anthropic’s ~$350B valuation, according to comparative data.
