JPMorgan Raises Zhipu Target Price to HK$1,800; DeepSeek Price Hike Reshapes China's AI Competitive Landscape
Nashnova编辑部
JPMorgan raised Zhipu's target price from HK$1,600 to HK$1,800 and lifted MiniMax to HK$260. This means → in China's AI model war, pricing power built on frontier intelligence is replacing low-cost strategies as the dividing line for investment value.
Why did JPMorgan adjust both companies at the same time?
In its August 16 note, JPMorgan raised Zhipu's target to HK$1,800 (maintaining Overweight) and MiniMax's target to HK$260 (maintaining Neutral).
The shared catalyst: Zhipu's release of GLM-5.3 and DeepSeek's API price hikes effective August 17.
This means → JPMorgan's core call is not "who is cheaper" but who holds pricing power at the frontier — only models strong enough to command prices can build sustainable businesses.
What is the "Pareto frontier" and why does JPMorgan use it here?
The Pareto frontier — a framework that asks "can anyone deliver the same capability at a lower price?" — is JPMorgan's primary tool for mapping China's AI model landscape.
In plain terms = if no rival can match a model's capability at equal or lower cost, that model sits on the Pareto frontier — safe from both performance comparisons and price wars.
This reflects JPMorgan's underlying investment logic: not who spends the most on training, but whose capability-price combination is hardest to displace.
What makes Zhipu's GLM-5.3 strong? How is the moat built?
GLM-5.3 uses the same base model as GLM-5.2. Gains in coding and agentic capability come primarily from reinforced post-training, not a new round of large-scale pre-training.
This means → the competitive edge shifts from "who has the biggest training set" to data quality, reinforcement learning, and engineering execution — capabilities far harder to replicate.
API pricing is essentially unchanged (input ¥8.00, cache-hit input ¥2.00, output ¥28.00 per million tokens). Stronger capability at the same price should lift adoption and retention. JPMorgan raised Zhipu's 2026–2030 revenue forecasts by 6%–10%.
How large are DeepSeek's price hikes? What do they signal?
V4 Pro peak hours: input rose from ¥3.00 to ¥9.00, output from ¥6.00 to ¥27.00. V4 Flash peak hours: input from ¥1.00 to ¥3.00, output from ¥2.00 to ¥9.00.
Short-term impact: cost-sensitive players — especially MiniMax — get breathing room as the cost gap narrows.
Yet JPMorgan argues DeepSeek's system-level efficiencies — its MoE architecture (a design that activates only part of the model's parameters to save compute), attention design, and KV-cache optimisation — remain its moat. In plain terms = DeepSeek raised prices, but it is still the industry's cost benchmark — rivals' cost structures have not fundamentally improved.
Where does MiniMax stand now? Why is M3.1 the make-or-break moment?
JPMorgan states plainly: MiniMax's current M3 model has not established a clear edge on either capability or cost-efficiency, leaving it squeezed between stronger models and DeepSeek's low-price strategy.
The breathing room from DeepSeek's price hike comes from a competitor's decision and could reverse at any time — it is not a self-built moat.
JPMorgan frames M3.1 as MiniMax's most important catalyst: it must either meaningfully improve capability or deliver standout cost-efficiency — claiming a position on either axis of the Pareto curve. A modest upgrade would have limited impact on the long-term thesis.
What drives the valuation gap between the two?
Zhipu's HK$1,800 target is based on a 20x 2030E P/E, discounted to December 2026 at a 15% WACC. The 20x premium reflects an expected 2026–2030 revenue CAGR of over 100%.
MiniMax's HK$260 target also uses a 20x 2030E P/E. JPMorgan raised its 2027–2030 revenue forecasts by 11%–21%, but remains cautious about MiniMax's ability to capture value as a standalone vendor — integrated platforms like ByteDance and Kuaishou can monetise across model, content, distribution, and advertising, while MiniMax relies more heavily on direct product and model-service revenue.
This reflects JPMorgan's overarching conclusion: endogenous capability improvement is worth more than passive gains from a friendlier environment. The valuation divergence between Zhipu and MiniMax is a direct expression of that judgment.
Content is for reference only, not financial advice.