(Article 3(1)e and Article 7 of MA)
Original title: (Article 3(1)e and Article 7 of MA)
Summary
(Article 3(1)e and Article 7 of MA)
Full Modality Model Lifts Intelligence; Multiple Drivers on ARR
Highlights: (1) full modality model is different from standalone ones as former achieve higher intelligence ceiling; (2) peak TPM has achieved consistent week-over-week growth of 10-20%; (3) it pursues asset light model with flexible and rapid compute orchestration; (4) ARR benefits from tokens growth, compute, and revenue per card. Rev per minute is higher than industry average; (5) M3.0 and Hailuo 3.0 are expected to be released midyear.
Full modality model has unique viral effects and lifts model intelligence. MiniMax focuses on both intelligence improvement (e.g., LLM, coding) and creative enhancements (e.g., video, speech, music), with the end goal to achieve AGI and economy improvement (% of GDP). Its full modality approach creates a unique viral effect and lifts model intelligence vs standalone models. Video generation is treated as a post-training feedback loop for its LLM. The precise understanding of light, motion, and space gained during video training (via advanced Captioning and VLM) is a recursive data source for LLM. The self-evolving loop drives better RL (reinforcement learning) with better video and LLM. As a result, MiniMax achieves higher intelligence ceiling, which becomes a fundamental driver for global economy.
Body
Full Modality Model Lifts Intelligence; Multiple Drivers on ARR
Highlights: (1) full modality model is different from standalone ones as former achieve higher intelligence ceiling; (2) peak TPM has achieved consistent week-over-week growth of 10-20%; (3) it pursues asset light model with flexible and rapid compute orchestration; (4) ARR benefits from tokens growth, compute, and revenue per card. Rev per minute is higher than industry average; (5) M3.0 and Hailuo 3.0 are expected to be released midyear.
Full modality model has unique viral effects and lifts model intelligence. MiniMax focuses on both intelligence improvement (e.g., LLM, coding) and creative enhancements (e.g., video, speech, music), with the end goal to achieve AGI and economy improvement (% of GDP). Its full modality approach creates a unique viral effect and lifts model intelligence vs standalone models. Video generation is treated as a post-training feedback loop for its LLM. The precise understanding of light, motion, and space gained during video training (via advanced Captioning and VLM) is a recursive data source for LLM. The self-evolving loop drives better RL (reinforcement learning) with better video and LLM. As a result, MiniMax achieves higher intelligence ceiling, which becomes a fundamental driver for global economy.
Multiple drivers on ARR. On inferencing, peak TPM (Tokens Per Minute) has achieved consistent week-over-week growth of 10-20%, thanks to surging demand for coding and agents. The double-digit WoW growth in TPM, coupled with industry-leading monetization efficiency per unit of compute, serves as the primary driver of ARR expansion. We view speed (output tokens per second) as important in Agentic AI, due to amount of token consumptions vs chatbot. MiniMax pursues asset-light model and sources compute across multiple markets on global basis. We expect MiniMax ARR benefits from token growth, compute, and revenue per card. We estimate its revenue per minute is about USD1 (vs industry at USD0.5) while cost per minute is about USD0.3 based on assessment of one H800 server (8 cards). Despite this, it does not raise input/output price; it revised up prompt cache read price for M2.7 vs M2.5, with overall blended price increased by 30%.
Upcoming new models anticipated. We expect M3.0 and Hailuo 3.0 will be released around midyear. Model intelligence determines pricing. We believe MiniMax demonstrates strong execution and provides better user experience through a synergistic closed-loop "model + harness". The integration serves as another acceleration engine as harness captures high-value, real-world interaction data and granular user feedback. This proprietary interaction refines models reasoning, decision-making, and execution capabilities with unprecedented precision and speed.
| RATING | BUY |
| PRICE | HK898.50 $ ^{a} $ |
| PRICE TARGET | % TO PT | HK1,118.00 | +24% |
| 52W HIGH-LOW | HK1,330.00 - HK220.00 |
| FLOAT (%) | ADV MM (USD) | 33.9% | 1,559.17 |
| MARKET CAP | HK281.8B | 36.0B |
| TICKER | 100 HK |
$ ^{a} $Prior trading day's closing price unless otherwise noted.
We would like to thank Fiona Fan, employee of Evalueserve Inc., for providing research support services to our preparation of this report. We would like to thank Han Wang, employee of Evalueserve Inc., for providing research support services to our preparation of this report. We would like to thank Erica Qiu, employee of Evalueserve Inc., for providing research support services to our preparation of this report.
MiniMax is a global AI foundation model company. Founded by a group of forward-thinking engineers, it is committed to driving AI innovation towards performing the full range of human intellectual tasks, from learning and reasoning to planning and generalizing knowledge across diverse domains.
We have a Buy rating and PT of HKD1,118 based on (1) 49x 2028E price-to-sales on the back of fast revenue growth at 123% CAGR between 2027 and 2030. We apply 60% valuation discount due to its scale vs overseas peers. (2) Our PT is based on WACC 13% and discounted back to 2026. Risks include (1) slower-than-expected pace in innovations; (2) AI-native products are not well received by users and enterprises; (3) geopolitical tension on chip export controls on advanced chips.
I, Thomas Chong, certify that all of the views expressed in this research report accurately reflect my personal views about the subject security(ies) and subject company(ies). I also certify that no part of my compensation was, is, or will be, directly or indirectly, related to the specific recommendations or views expressed in this research report.
I, Zoey Zong, certify that all of the views expressed in this research report accurately reflect my personal views about the subject security(ies) and subject company(ies). I also certify that no part of my compensation was, is, or will be, directly or indirectly, related to the specific recommendations or views expressed in this research report.
(Article 3(1)e and Article 7 of MA)
Recommendation Published Recommendation Distributed
April 22, 2026 9:20 A.M.
April 22, 2026 9:20 A.M.
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MiniMax Group Inc. (100 HK)
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Other Companies Mentioned in This Report • MiniMax Group Inc. (100 HK: HK$898.50, BUY)
Notes: Each box in the Rating and Price Target History chart above represents actions over the past three years in which an analyst initiated on a company, made a change to a rating or price target of a company or discontinued coverage of a company.
| Distribution of Ratings | IB Serv./Past12 Mos. | JIL Mkt Serv./Past12 Mos. | ||||
| Count | Percent | Count | Percent | Count | Percent | |
| BUY | 2173 | 61.82% | 364 | 16.75% | 109 | 5.02% |
| HOLD | 1182 | 33.63% | 102 | 8.63% | 16 | 1.35% |
| UNDERPERFORM | 160 | 4.55% | 1 | 0.62% | 1 | 0.62% |
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MiniMax Group Inc. (100 HK)
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MiniMax Group Inc. (100 HK)
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