MiniMax Goldman Sachs Conference Call: Confident in $1 Billion ARR This Year, Model Advantage in "Organizational Agility," Highly Integrated with Domestic Chips

On July 4, according to news from ZhuiFeng Trading Desk, Goldman Sachs released its latest research report on July 3, stating that MiniMax's conference call conveyed strong signals of commercialization and technological evolution: Management is confident in achieving an annual recurring revenue (ARR) target of $1 billion by the end of 2026.

The report indicates that the core catalyst lies in the inflection point of the "price war" in China's AI large model industry—as competitor DeepSeek announced price increases during peak periods, industry pricing is returning to rationality.

MiniMax, leveraging over 90% computing power utilization, deep integration with domestic chips, and unique "organizational agility," has achieved gross margins far exceeding peers while maintaining highly competitive pricing (M3 model blended price of $0.22 per million tokens). Additionally, the H3 video generation model, to be launched within weeks, will further open up the imagination space of the multimodal market.

Goldman Sachs maintains a Buy rating with a 12-month target price of HKD 860, implying a 141% upside from the current share price of HKD 356.80.

ARR Growth Trajectory: From $100 Million to $1 Billion, Management Provides a Clear Path

The report states that MiniMax management systematically laid out the growth milestones for ARR (annualized recurring revenue) during the conference call:

• End of December 2025: ARR reaches $100 million;

• February 2026: ARR rises to $150 million;

• April 2026: ARR doubles again compared to February;

• Before the official launch of the M3 model on June 1: ARR further accelerates.

Management clearly expressed full confidence in achieving the $1 billion ARR target by the end of 2026.

In terms of pricing strategy, the M3 maintains the same pricing as the previous generation M2.7. However, management emphasizes that this strategy is sustainable at the gross margin level—due to architecture upgrades in training and inference, which bring over 2x cost savings, essentially offsetting the cost increase from doubling the total parameter count.

The company also previewed that a larger M3 series model will be launched in the second half of 2026, aiming to further improve intelligence while maintaining strong cost performance.

This ARR growth curve is the core basis supporting Goldman Sachs' revenue projections—Goldman Sachs expects MiniMax's revenue to jump from $79 million in 2025 to $300 million in 2026, further reaching $880.1 million in 2027, and exceeding $2.4696 billion in 2028.

DeepSeek Price Increase: A Breakthrough Signal of Industry Pricing Rationalization, Directly Benefiting MiniMax

This is the most market-catalytic external event in the Goldman Sachs report.

DeepSeek announced this week that the official version of its V4 model will launch in mid-July, simultaneously introducing a peak/off-peak API differential pricing mechanism: Peak hours (9:00 AM to 12:00 PM and 2:00 PM to 6:00 PM Beijing time) will be charged at 2x the off-peak rate, with blended pricing of approximately $0.35 per million tokens (Pro version) / $0.12 per million tokens (Flash version).

Goldman Sachs interprets this as: An early signal that aggressive pricing by Chinese AI model companies since the end of April 2026 (with some players having zero or even negative gross margins) is transitioning to a more rational phase, essentially reflecting inference cost pressure on the pricing side.

In comparison, MiniMax's M3 blended pricing is $0.22 per million tokens, offering a significant competitive advantage in the performance/price dimension, with gross margins notably higher than peers—thanks to its higher proportion of self-built optimized computing power and an architecture that achieves efficient inference with smaller activated parameters.

MiniMax also specifically noted that its self-operated computing power can achieve over 90% utilization, using peak hours to serve knowledge workers and developers, and off-peak hours to allocate idle computing power for experiments and data sorting, thereby balancing computing power peaks and valleys and sustaining cost advantages for long-duration agent workflows.

H3 Video Model: Launch Within Weeks, Deeply Integrated with M3 Architecture

Meanwhile, MiniMax is about to launch its next-generation video generation model H3, with an official release expected "within the coming weeks."

The core upgrades of H3 are reflected in two dimensions:

• Comprehensive improvement in video generation quality and functional diversity, underpinned by major architecture upgrades (including optimization of annotation/classification/feedback loops);

• Deep integration with the M3 model architecture: Large language model capabilities are embedded into H3's DiT (Diffusion Transformer) architecture, such as enhanced understanding of human actions and basic physical relationships.

Additionally, MiniMax is introducing vertical domain experts to gradually enter the long-form film/TV series production market, expanding the commercialization boundaries of video generation.

Competitive Landscape: From "Hundred-Model War" to Concentration, "Organizational Agility" Becomes Core Barrier

Goldman Sachs believes that MiniMax's assessment of China's AI model competitive landscape is strategically significant: The market is rapidly concentrating from hundreds of players one or two years ago to the top tier.

During the conference call, facing competition from AI labs under domestic internet giants, MiniMax defined its own advantages as:

• Efficient corporate organizational structure;

• Higher infrastructure utilization;

• Rapid model iteration capability;

• Fast response to emerging agent opportunities—such as the rapid commercialization of MaxClaw following the rise of OpenClaw, and the rapid deployment of the MiniMax Code product.

Company management believes that as AI model competition shifts from "one-time benchmark rankings" to "continuous product iteration and real-world implementation," sustainable ROI will become the core evaluation criterion, and organizational agility will become increasingly valuable under this new competitive paradigm.

Global Infrastructure and Domestic Chip Integration: Accelerating Localization

At the computing power infrastructure level, MiniMax adopts a dual-track parallel strategy:

• Directly renting computing power from global cloud service providers (CSPs);

• Deep cooperation with emerging cloud service providers (neo-cloud).

Currently, MiniMax's localized inference infrastructure has covered more than 200 countries and regions globally, with a highly diversified customer base and no excessive concentration risk in any single country.

In the Chinese market, MiniMax has highly integrated domestic AI chips (ASICs) for inference tasks, and as domestic chip capabilities continue to improve, this localization process is accelerating. This deployment not only helps reduce reliance on overseas computing power but also builds a certain level of supply chain resilience in the context of the US-China technology competition.

In terms of talent strategy reserve, MiniMax supports high-intensity technological competition with an extremely lean team size:

• 400 to 500 employees company-wide, with over 80% engaged in R&D;

• 300 to 400 employees participate in an ESOP (Employee Stock Ownership Plan) covering approximately 7% of equity, using equity incentives to strengthen talent retention;

• Continuously recruit fresh graduates from top Chinese universities and overseas prestigious schools;

• Recruit senior experts in vertical fields through the "10X Talent Program," converting industry know-how into model training and real-world task optimization capabilities.

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