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A fascinating collection of AI related videos. Each more interesting than the other. If you do not have the time, just listen to the first one. And try to enjoy your "free" AI as much as you can while you still can. I won't last. It cannot!
00:00- Free ChatGPT Is Dead. The $200 Subscription PANIC.
A very complete overview of the Chinese AI models. But how did we got there?
Well, what about this:
When you know that AI at this stage is 95% mathematics. Maybe the above picture encapsulate what really happened.
In 2012, when my team was building statistical models, I was in the US once a month. Five years later, I was in Shanghai every week. (It's closer to Japan but communication was more difficult with many bright young engineers we needed to train.) So having seen the transformation of China over the last 20 years, I find the outcome rather obvious.
We
were lucky enough to read the tea leaves ahead of the "frontier", so to
speak, and conclude that various "open" models out of China would soon
be the biggest talking point - not to mention the market's fulcrum
catalyst. See for example:
More recently, last week we summarized virtually all of China's AI Models in "The Definitive LLM Primer"
(July 11), which we republished below for our readers' convenience, yet
in the fast-paced world of AI, even that article is now woefully out of
data due to the recent arrival of Moonshot's open-sourced Kimi K3 (July
16), which has not only taken the AI world by storm, but promptly
sparked a momentum meltdown amid growing fears that Chinese LLMs are
catching up too fast to frontier US models (something we warned about one month ago here).
The reason for the jarring market reaction is that Kimi K3 is viewed as
being on par, if not better, than most leading US frontier models.
Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5.
This is a 17-place jump from Kimi-k2.6 (#18 -> #1).
And
while there is rampant debate whether this performance is accurate or
gamed to beat specific benchmarks, the bigger issue is that China is now
clearly developing stunning(ly cheap) open-sourced models which are on
par with much more expensive US models, which in itself renders the
entire ROI calculus behind trillions in AI capex spending null and void,
because if China can achieve 98% what the US has done with a fraction
of the capex...
...
then all those trillions already allocated to AI capex are nothing more
than sunk costs, as end markets will quickly pivot to what is cheapest,
since in most cases it is also on par with what is best.
So what happened for those who went on vacation early last week and are stunning how everything has changed?
Well, as Ronald Keung, the Goldman strategist who wrote the original Chinese LLM primer said late on Friday, we have gone "from cost efficiency (DeepSeek), rise in model intelligence (GLM) to new frontier/pricing power at Kimi K3."
On
July 17, the Kimi K3 open-weight model was released with 2.8 trillion
parameters, and has set a new frontier in coding and certain agentic
capabilities globally, as per Arena.ai coding rank and Artificial
Analysis Intelligence score.
Accordingly,
Goldman notes K3’s pricing was set at a new high amongst Chinese
models, US$2.3 per 1M tokens (blended) vs. Qwen3.7 Max of US$1.4/Zhipu’s
GLM5.2 of US$0.9/MiniMax’s M3 of US$0.22/DeepSeek’s V4 Pro of US$0.18,
being still below global SOTA models.
As highlighted in our recent China LLM primer, China’s AI open-source/open-weight models are reaching a critical point of intelligence performance for global proliferation. Amongst
signposts into 2H 2026, we have anticipated that competition could
intensify in the high-end coding segment via coding data flywheel and
scaling (to larger models, up to 2-5 trillions parameter sizes). The
share price reaction of Knowledge Atlas/Zhipu (where Goldman recently
initiated at Neutral, -28% on July 17) and MiniMax (Buy-rated, -16% on
July 17) have been due to concerns around Chinese AI model competition
and who the potential long-term winners will be, given the competitive
landscape/sustainability of model company leadership remains highly
dynamic.
Here,
Goldman repeats that Independent AI model companies stand out in its
Competitive Positioning framework (see full report for more). The bank
also notes the positive read from Chinese President Xi’s comments at the opening ceremony of World Artificial Intelligence Conference (WAIC) in Shanghai held
last Friday, drawing parallels of AI’s societal impact to the invention
of electricity and steam engines, and in offering China’s technology
infrastructure to developing nations with its continued open approach,
yet cautioned on the need for human oversight/controls and risk
mitigation.
What to watch out for from here?
Harness/agentic applications: We
expect China AI model companies to increasingly focus on positioning
their harness/agentic applications as key entry points, especially in
coding (e.g. Zhipu’s ZCode, Tencent’s Workbuddy, and Alibaba’s Qoder
which are aggregate platforms that support the full suite of AI models)
as model companies attempt to close the loop in capturing more real-life
coding and agentic data for scaling. Co-work and industry expert
agentic products could be the next priorities.
Multiple
large parameter high-end coding model launches and potentially stricter
access to the most advanced Chinese AI models outside of China: After
Kimi K3 launch, we anticipate for further new Chinese AI model launches
over 2H26 (Zhipu GLM, Alibaba Qwen, MiniMax M3 Pro and more) with
significantly larger total parameter sizes of 2-5 trillion across
Chinese AI model players. Coding/programming segment competition will
remain intense. The addition of multi-modal/visual understanding will
also be the next upgrades amongst Chinese AI foundation models.
Continued suppressed API pricing for the lower end agentic-focused segment: Goldman
expects API pricing and therefore gross margins to remain under
pressure at the lower-end pricing segment (around US$0.1-0.2 per 1M
tokens) into the second half, as China’s AI model players have significant cash buffers post-fund raising to subsidize competitive pricing. As
a result, we see financial strength as one of the three most important
metrics (alongside pricing power and cost efficiencies) in assessing a
Chinese AI model company.
Multi-modal/video-generation models to see further ARR ramp-up from global adoption: Goldman
expects continued healthy industry pricing and gross margins within
video generation (unlike foundation text) where key players ByteDance’s
SeeDance (at reportedly 70% gross margins, latest US$2bn ARR run-rate),
Kuaishou’s (Buy) Kling and MiniMax’s (Buy) Hailuo/upcoming H3 models to
enjoy healthy growth over 2H 2026 amid new functionality breakthroughs
(combination of video-generation with LLM) and tight computing resources
where demand significantly outpaces capacity.
Expect increasing
domestic ASICs supply, any stricter access to China’s future most
frontier models for overseas markets, Western markets’ policies on China
models, and access to high-end computing in model training as key swing
factors/risks to our China AI model token/revenue growth trajectory.
Goldman's key ideas/stock picks
Within Cloud & Data Centers (the bank's top preferred sub-sector within China Internet), GS
continues to highlight key ideas (Alibaba, GDS, VNET, and Kingsoft
Cloud) on the back of higher AI hyperscaler capex spending into 2H 2026.
Within
AI models, Keung highlights MiniMax on upside skewed risk-reward. Key
swing factors for MiniMax to improve its overall competitive positioning
will hinge on its pricing power following its recent completed
fund-raising that has strengthened financial strength. Expect its H3
video generation model performance (imminent launch in a more favorable
industry landscape vs. text models) and time-to-market for its next M3
updates (focusing on further coding intelligence level uplift from
post-training/reinforcement learning, we estimate M3 update over
July-Aug, and a larger parameter size M3 Pro model later in 2H 2026)
will be the next key drivers.
Yet what may be the clearest signal
yet just how competitive with the US Chinese LLMs have become comes from
none other than OpenAI's "head of strategic futures" (presumable that
title refers to whoever can demand protectionism the loudest) who
essentially is begging for protectionism against open Chinese LLMs...
Some observations on Kimi:
1. It's a very good model! I don't think its performance can be
explained away by distillation or anything like that. In agentic coding
sessions, it seems pretty much on par with the best public models of Q1
2026. In my fairly limited use, it also…
... and the scathing retorts from Trump's (former?) AI Tsar David Sacks, who responds that "K3
just fixed 15 critical security bugs that Codex and Fable refused
because of “cyber guardrails.” There’s no reason to limit American
models on tasks that Chinese models handle without issue. We’re only
making ourselves less competitive." And then again, here to point
out that Chinese LLMs are already more useful to some programmers than
the best of the (very expensive) best Anthropic and OpenAI have the
offer, to wit: "Here’s
another example: Hugging Face tried using American frontier models to
analyze an AI-powered cyber attack. But the guardrails blocked requests
containing real exploit payloads so they switched to GLM 5.2 running
locally. The guardrails actually impaired defensive security."
Three weeks ago, we attempted a lengthy answer of the "trillion dollar question" namely are Chinese AI models a better value than US models and,
using extensive research from UBS, concluded that at almost 95% of the
capability (and rising) and just 10% of the cost, the answer was a
resounding yes.
Fast
forward to today when Goldman analyst Ronald Keung also addressed the
$64 trillion elephant in the room, and published a 50-page China AI models LLM primer (available to pro subs), in which he agrees with our conclusion, namely that "China's
AI open-source/open-weight models are reaching a critical point of
intelligence performance vs. global proprietary models, with a
significant ramp up in domestic enterprise & global SME adoption
that will enable a positive data flywheel of further model
improvement."
In the report, Goldman evaluates:
How these models achieve such performance at low costs/tight computing resources;
Why they pursue an open-source/open-weight approach and how they monetize;
What
the key addressable markets are, as global enterprises shift from
'token-maxxing' to ROI-first, where Goldman highlights two favored ARR
quadrants for Chinese models; and
Who the potential long-term winners will be under the bank's Competitive Positioning framework.
In
keeping with the recent newsflow, Ronald notes that ongoing
politicization of AI, namely any stricter access to China's future 'most
frontier' models for overseas markets, Western markets' policies on
China models, and access to high-end computing in model training, are
three key swing factors/risks to the bank's China AI model token/revenue
growth trajectory.
Additionally, the Goldman strategist
introduces his Chinese AI model Competitive Positioning framework based
on pricing power, cost advantage and financial strength, overlaying with
token scale and market share progressions, and identify Knowledge Atlas
(Zhipu, initiation) and DeepSeek (private) as the strongest positioned
in foundation models, and Bytedance (private) in multi-modal.
Before we get into the weeds, excerpted from the gull Goldman report, lets start with a visual summary of China's AI Model and Hyperscaler Ecosystem...
... and a Breakdown of China's open-source models unit economics today and path to profitability
Which
brings us to the core overarching theme of Chinese AI development, from
cheap to smart, or as Goldman's Ronald Keung puts it:
From DeepSeek’s moment last year (on cost efficiency) to Zhipu’s GLM moment this year (on model intelligence).
China’s
AI open-source/open-weight models are reaching a critical point of
intelligence performance vs. global proprietary models, with a
significant ramp up in domestic enterprise adoption and a proliferation
of global consumer and SME demand. In this report, Goldman evaluates:
How these models achieve such performance at low costs/tight computing resources;
Why they pursue an open source/open weight approach and how they monetize;
What the key addressable markets are, where the bank highlights two favored ARR quadrants and risk factors; and
Who the potential long-term winners will be under Goldman's Competitive Positioning framework.
Chinese models are reaching a critical ‘good enough’ stage for agentic tasks/specific coding scenarios, and rising fragmentation in China’s AI model landscape (but the strong will get stronger). While pricing power remains strong for models with frontier performance/multi-modal, the lower-end segment is in a price war; nevertheless,
Agentic AI is driving explosive demand for these value-for-money models
at the lower-end. Access to computing will be a swing factor, where
US/China regulations, balance sheet and inference efficiency are key.
Accordingly, the bank introduces its Chinese AI model Competitive
Positioning framework based on pricing power, cost advantage and financial strength, overlaying
with token scale and market share progressions, and identify several
companies its views as the strongest positioned in both foundation
models and in multi-modal.
How do Chinese models achieve competitive performance at low costs/tight computing resources?
By leveraging smaller-sized parameter models for equivalent benchmark
performance, and Mixture-of-Expert and new architectural innovations.
Chinese AI models are bifurcating into a two-tiered market where
performance and time to market are key to pricing power, thereby leading
to two ‘ARR maximizing’ quadrants based on token adoption and pricing. A
positive flywheel is taking effect for top Chinese AI models driven by
increasing actual real world coding adoption, and reducing their
reliance on model distillation practices.
Why are Chinese models pursuing an open source/open weight approach, and ways to monetize? Open
source allows for greater flexibility in model training/deployment, and
allows for the widest adoption and an open community. Open-source
models’ disclosed ARRs are likely understating total deployment and
revenue potentials, and Goldman expects more shifts to open weight (with
Community License, i.e., commercial terms if for commercial use) among
Chinese AI models down the road.
What are the key addressable markets, domestically and internationally, and key risks? The
bank highlights two favored ARR quadrants and estimate China/China AI
models market to see token growth of 25X by 2030E; the coding
landscape to consolidate while the agentic/low-end segment could remain
fragmented. International (going global) should be a key upside, with
the potential for higher pricing and global proliferation, especially in
non-US markets as global enterprises increasingly pivot from a
token-maxxing to a ROI-first model that prioritizes clear task
boundaries, number of agents per day, back-end process automation and
actual output over pure computational token volume.
Key risk factors are
market access/anti-distillation and regulations, including any stricter
access to China’s future most frontier models for overseas markets,
access to highest-end leased computing equipment (used for training),
market access to western markets, further restriction lists/entity list
designations (but this could be positive for China’s path to further AI
self-sufficiency across software/CPU/ASICs), and competition from
SLM/threat from AI architectures.
Who are best positioned to be the long term winners within China’s AI model companies?
Goldman expects players with the largest ARR scale with a gross margin
advantage + financial strength to be the long-term winners. The bank
highlights independent AI model companies mostly stand out in its
Competitive Positioning framework in pricing power + cost advantage (in
aggregate represent over US$200bn in implied valuations, based on latest
market cap/funding rounds), where Zhipu and DeepSeek are the most strongly positioned in text based foundation models, while ByteDance leads in multi-modal capabilities.
Signposts for 2H 2026
Harness/agentic applications: China
AI model companies will increasingly focus on positioning their
harness/agentic applications as key entry points, especially in coding
(e.g. Zhipu’s ZCode, Tencent’s Workbuddy, and Alibaba’s Qoder which are
aggregate platforms that support the full suite of AI models) as model
companies attempt to close the loop in capturing more real-life coding
and agentic data for scaling. Co-work and industry expert agentic products could be the next priorities. Enterprises
will increasingly focus on overall cost per task instead of headline
pricing per token, with an openness to using different models
(multiple-models approach).
Multiple large parameter
high-end coding model launches and potentially stricter access to the
most advanced Chinese AI models outside of China: Multiple new
Chinese AI model launches are expected over 2H26 with significantly
larger total parameter sizes of 2-5 trillion across Chinese AI model
players. Coding/programming segment competition will intensify as
Chinese models try to challenge Zhipu GLM’s leadership via training on
high-quality real-life coding data (where available) and scaling to
larger parameter model sizes. There may be a potential shift from an
open-source to an open-weight approach for best-performing models (i.e.
from free-for-all use cases to requiring revenue sharing/a take rate for
commercial use). The addition of multi-modal/visual understanding will
be the next upgrades amongst Chinese AI foundation models (e.g. for GLM,
DeepSeek), while MiniMax M3 already excels in these given the
multi-modal focus of MiniMax from the start. That said, press reports on
potential future restrictions on overseas access to China’s most
advanced AI models (both closed and open source if at the frontier
level) as cutting-edge artificial intelligence is increasingly being
seen as a critical national asset.
Continued suppressed API pricing for the lower end agentic-focused segment: While
DeepSeek recently announced an increase in peak-hour pricing from
mid-July, API pricing and therefore gross margins should remain under
pressure at the lower-end pricing segment (around US$0.1-0.2 per 1M
tokens) into the second half, as China’s AI model players have
significant cash buffers post-fund raising to subsidize competitive
pricing at zero/negative gross margins in the near term. As a result, financial
strength will be one of the three most important metrics (alongside
pricing power and cost efficiencies) in assessing a Chinese AI model
company, where cash on hand, net cash as % of assets and
valuation multiples will be the critical financial strength metrics for
long-term success. This said, Goldman is Buy-rated on MiniMax as the
company stands out on cost efficiency/cost advantage metrics under a
Competitive Positioning framework. With its M3 model well positioned in
the favored ARR maximizing quadrant (attractive pricing + high token
volumes), alongside its discounted valuation at 13X P/2026E year-end ARR
(vs. China/global peers which command multiples several times higher at
similar ARR stage), risk-reward is skewed to the upside (Goldmanb
reiterates its Buy rating of the stock). Key swing factors for MiniMax
to improve its overall competitive positioning will hinge on its pricing
power and financial strength. Its H3 video generation model performance
(imminent launch in a more favorable industry landscape vs. text
models) and time-to-market for its next M3 updates (focusing on further
coding intelligence level uplift from post-training/reinforcement
learning, estimated over July-Aug, and a larger parameter size M3 model
later in 2H 2026) will be the next key drivers.
Multi-modal/video-generation models to see further ARR ramp-up from global adoption: Goldman
expects continued healthy industry pricing and gross margins within
video generation (unlike foundation text) where key players ByteDance’s
SeeDance, Kuaishou’s (Buy) Kling and MiniMax’s Hailuo/upcoming H3 models
to enjoy healthy growth over 2H 2026 amid new functionality
breakthroughs (combination of video-generation with LLM) and tight
computing resources where demand significantly outpaces capacity.
According to China news reports like LatePost and 36Kr, ByteDance’s
Seedance gross margins have been at a healthy 70% at its latest US$2bn+
ARR run-rate.
The bank's strategists also expect increasing
domestic ASICs supply, tighter access to overseas computing resources
and potential market access limitations (could mirror TikTok’s
trajectory where rapid expansion in western markets was followed by more
regulations/focus on ensuring data security where computing will have
to be conducted within local jurisdictions).
Goldman's assessment of AI model companies: ARR scale x gross margin advantage + financial strength
Financial strength (Balance sheet, Access to computing)
Accordingly,
the bank lays out its Competitive Positioning framework for AI model
players based on quantifiable metrics, 1) Pricing Power (amongst which,
based on Time-to-market of model launch, Arena score based on actual
usage cases and pricing level), 2) Cost advantage (based on token volume
scale, throughput/cache hit rate, parameter sizes/activation ratio and
our estimate of inference gross margins), and 3) Financial strength
(based on cash on-hand, net cash as % of assets, and valuation
multiples).
Next, an overview of competitive analysis for key LLM labs’ positioning
The
bank identifies two favorable ARR quadrants (in maximizing ARR) which
is a combination of maximizing token volumes and/or pricing level
Comparing China’s key LLM players
Decoding China’s AI model tokens & our forecasts on token/revenue share
How do Chinese models achieve competitive performance at low costs/tight computing resources
Smaller-sized parameters models for equivalent benchmark performances, Mixture-of-Expert and new architectural innovations
As readers of our Chinese LLM primer may
recall, compared with a year ago, Chinese models’ coding and agentic
capabilities have reached a critical level in being able to complete
more coding and autonomous agentic tasks with higher success rates as
context windows have been expanded to 1 million tokens. The smaller
parameter model sizes of Chinese models (spanning from 200bn to 1.6T
parameters, at 2-10% of leading SOTA models, due to constrained access
to high-end computing), and highly efficient architectures (MoE, Sparse
Attention, OCR etc., at 3-5% activated parameters only vs. total
parameter sizes) all contribute to the much lower training and inference
costs for Chinese models vs. leading US models. Goldman attributes the
recent step improvement of Chinese models in coding according to
Arena.ai to data curation, distillation techniques and reinforcement
learning post training, despite their relatively small parameter model
sizes (1.6T for DeepSeek V4 Pro, 0.7T for Zhipu’s GLM5.2 and 0.4T
for MiniMax’s M3). On June 27, DeepSeek introduced DSpark, a speculative
decoding framework that makes existing DeepSeek-V4 models serve faster.
DSpark has already been deployed in DeepSeek-V4 Flash / Pro online
serving, improving per-user DeepSeek-V4 generation 60-85% faster on
V4-Flash and 57-78% faster on V4 Pro without changing the model’s
weights or output quality.
Chinese AI models bifurcating
into two-tiered market (where performance and time to market are key to
pricing power), with two ‘ARR maximising’ quadrants based on token
adoption and pricing
Goldman is seeing pricing power for
the highest performing Chinese AI models, e.g. Zhipu’s GLM5.2 model and
Alibaba’s Qwen3.7 Max models at around US$1 per blended 1M tokens, at
5X that of low-end Chinese AI models. The reported tighter US processes
in allowing most SOTA model access has also opened new arenas for
China’s top performing coding models for enterprises and SMEs. Smaller
parameter and activated ratios allow China’s top performing models to be
priced at US$1, 10-25% vs. US SOTA models at US$4-8 per blended 1M
tokens, while generating double digit 10-20% gross margins (GSe) that
are lower than global SOTA models due to relatively lower pricing
power. In the lower-end segment, agentic focused models are priced at
US$0.06-0.2 per blended 1M tokens, which are enabling these models to
tap into new global TAMs for price sensitive SMEs and one-man companies.
MiniMax generates 60-70% revenues from overseas.
Note that
DeepSeek announced that its V4 official version is set for launch in
mid-July, alongside the introduction of peak/off-peak API pricing to
better allocate resources and improve service stability. V4 Pro/Flash
non-peak pricing remains unchanged, while peak hour (9am-12pm/2pm-6pm
China time) will be charged at 2X non-peak rates, implying
blended pricing of US$0.35/US$0.12 per 1M tokens due to strong Chinese
AI model demand that is increasingly causing significant compute
tightness in work/productivity scenarios.
Positive
flywheel is taking effect for top Chinese AI models from increasing
actual real world coding adoption, with less reliance on model
distillation ahead
Compared with learning and
distillation tactics from global SOTA models in the past, top Chinese
models like GLM5 are reaching a critical stage of adoption by China’s
major enterprises and global users. As per LatePost, AI-generated code
has increased to as high as 90% at some China mega-cap companies, up
from 20-30% in 2H25, which will enable a positive data flywheel effect
of further improvements via reinforcement learning with actual user data
(both successful and unsuccessful coding cases) and post training.
These have underpinned the step improvements in GLM5.2 from GLM5.1 in
just over the course of a few months in 2026, and expect to see further
step improvements to Chinese AI models over the next 6-12 months.
In coding/agentic tasks, Chinese players are reaching global top-tier positions
Compared
with global SOTA leading models of several tens of trillions, China
open source models are mostly around or below 1 trillion parameter in
size, and adopt a MoE structure, with low activated to total parameter
ratios for higher inference efficiency.
Blended
LLM token price (SDLLMTK) has been declining since early June,
potentially driven by rising adoption of cost effective China models
Historical and projected capex for major US & China cloud service providers
Capex to operating cash flow ratio is still healthy for China hyperscalers
Case study on Meituan’s LongCat 2.0: A milestone for China’s domestic AI infrastructure
Released
on June 30, 2026, Meituan’s LongCat 2.0 marks a major milestone as
China’s first official 1.6 trillion-parameter open-source
Mixture-of-Experts (MoE) model trained and deployed entirely on a
50,000-card domestic compute cluster. Purpose-built for agentic coding
and complex software engineering workflows, the model features a native
1-million-token context window enabled by LongCat Sparse Attention (LSA)
and dynamically activates an average of 48 billion parameters per token
to optimize inference costs. Implications for a more
self-sustainable China AI model outlook that is less reliant on foreign
high-end chips for model training: The successful end-to-end
pre-training and inference of a trillion-parameter class model on
Chinese silicon (reportedly utilizing Huawei Atlas-950 SuperPods)
fundamentally drives a more sustainable China’s AI model development
outlook, in our view. While previous Chinese flagship models, such as
DeepSeek V4-pro, mentioned domestic chips for inference, LongCat 2.0’s
ability to overcome critical memory bottlenecks and distributed
stability challenges during the compute-heavy pre-training phase proves
the viability of a wider and more localized hardware stack for AI model
training in the future.
Why are Chinese models pursuing an open source/open weight approach, and ways to monetize?
Open
source allows for higher flexibility in model training/deployment, and
allows for the widest adoption and an open community
Alibaba’s
Qwen model family has long pursued an open source approach (before
adopting a closed source for its largest and highest performing Qwen-Max
models for better monetization), while other key Chinese AI model
players have mostly pursued an open source/open weight approach
including DeepSeek, Zhipu’s GLM and MiniMax M3 series models with the
exception of ByteDance’s full closed proprietary approach for its Seed
model. The open source approach allows for the highest flexibility in
terms of the locations of where models are trained (and thus where the
models can be deployed both inside and outside of Mainland China after
training). An open source approach also allows for the highest adoption
amongst the AI community with full transparency of the model
parameters/architecture for trust, and an open community in driving more
user feedback and thus model iterations/improvements. The open source
approach vs. world’s leading closed/proprietary approach also provides
an alternative choice for worldwide AI users when the best performing
closed models have stricter user access and higher costs from their
premium pricing, especially at a time when ‘token-maxxing’ has become a
key cost consideration for many corporates.
Open source models’ disclosed ARRs are likely understating total deployment and revenue potentials
While
open source model companies offer their own coding plans and a
chargeable open platform API channel (where the model companies conduct
their own model inference), the majority of open source models also
allow individuals/third-party hyperscalers/neocloud providers to deploy
the models without a charge even for commercial use (e.g. Alibaba
Cloud’s Bailian MaaS platform can house GLM5.2 open source model without
needing to pay a fee/take rate to Zhipu). As a result, while Zhipu’s
last stated ARR target for year-end 2026 is at US$1bn, the actual
deployment of GLM5.2 model worldwide is and will be multiple-fold higher
vs. Zhipu’s own API channel token volumes and revenue. There is also
increasing post training of Chinese AI models that are re-branded by
global companies (e.g. Composer 2 etc.) where the Chinese AI models do
not necessarily receive any revenues given the open source spirit.
Expect more shifts to open weight (with Community License) approach among Chinese AI models down the road
While
Zhipu’s open source GLM model’s MIT license allows for free for all use
cases (regardless of revenue), MiniMax M series models have pursed a
restricted license (where the industry terms it as an open weight with
Community License model), which requires MiniMax’s agreement and
commercial terms (e.g. revenue sharing/a take rate) on commercial use.
This will be the likely next path for other open source models in the
Chinese AI model industry, in order for eventual gross profits of
inference tokens to cover training costs and for each AI model company
to achieve a sustainable path to returns.
What are the key addressable markets, domestically and internationally, and key risks?
According
to Goldman estimates, China AI models’ aggregate API+subscription
revenue to increase from Rmb35bn in 2026E to Rmb879bn by 2030E from
rising model intelligence, in particular with recent models like GLM5.2
reaching a critical point for global adoption and attractive pricing.
The bank's revenue pool estimates for China AI models imply total
Chinese AI model daily token consumption of 350T in 2026E to increase to
4,600T by 2030E.
Goldman estimates domestic market to see
token growth of 25X by 2030E; coding landscape to consolidate while
agentic/low-end segment could remain fragmented.
At 140tn
daily token volumes for the country in March 2026 and several hundred
trillions by June 2026 as per the National Bureau of Statistics, open
source/open weight models have roughly a 30% token share (vs. 70% token
market share by ByteDance alone, which is closed source and mainly
driven by its Doubao app enterprise and individual user base, as the #1
used AI chatbot in China). Similar to the US, the coding segment (at a
premium pricing level) will continue to be dominated by SOTA best
performing models. Meanwhile, the lower-end segment focused on agentic
AI will remain fragmented with multiple players due to the financial
strength of AI model companies/mega-caps which would sustain the
lower-end segment price war for longer.
International
(going global) to be the key upside; with potential for higher pricing
and global proliferation, especially in non-US markets
Goldman's
US research team estimates agentic AI will drive 24X growth in token
consumption by 2030 (from 2026) to 120 quadrillion tokens per month (or 4
quadrillion tokens per day, from their estimate of 170 trillion daily
tokens today), with the biggest driver at 55X from enterprise agents and
12X from consumer agents. The global (ex. China) landscape has seen
significant token share gains from Chinese AI models, as rising model
intelligence and attractive token costs have driven higher adoption
across 24/7 Hermes/Claw/Productivity agents, and shifting global SME
mindset on using Chinese models in managing token costs given Chinese
models have reached a ‘good enough’ stage in terms of
intelligence/performance.
Pivoting from ‘token-maxxing’ to ROI-focused metrics, e.g. Daily Active Agents/Agentic Work Units
The
AI token proliferation is undergoing a paradigm shift from
‘token-maxxing’ (an initial focus from late 2025 to early 2026 where
enterprises equated high AI token consumption directly with organization
productivity) towards an ‘ROI-first’ model that prioritizes clear task
boundaries and output over raw computational volume.
‘Token-maxxing’ has been attributed to corporate inefficiencies and cost overruns: Data
from a Jellyfish AI Engineering trends study indicated heavy AI users
at enterprises consumed 10X more tokens but only had a 2X increase in
output. Meanwhile, multiple US Internet companies have commented back in
April 2026 that either their engineering teams utilized a full year’s
AI budget in just four months using agentic products (promoting stricter
monthly caps per tool) or changed their internal token utilization
leaderboards that previously wrongly incentivized staff to launch
inefficient/low value autonomous agent tasks.
Enterprise
framework is pivoting towards an ROI-first model that prioritizes clear
task boundaries, number of agents per day, backend process automation
and actual output over pure computational token volume. Besides
a marked increase in adoption of Chinese AI models, global enterprises
have been downgrading default models to cheaper flash models for more
typical tasks (while reserving SOTA models for only the top most value
creating tasks like coding/programming). There is a transition from just
token tracking to metrics like Daily Active Agents (DAA) and Agentic
Work Units (AWU), and the overall cost per task will become more
relevant than price per token metrics.
Open
source/open weight approach allows for the option for U.S. hyperscalers
to host Chinese models, operated within U.S. cloud ecosystem
Alphabet
and Amazon’s respective cloud services Gemini Enterprise Agent Platform
(via. its Model Garden) and AWS Bedrock already offer a broad selection
of Chinese AI models including DeepSeek, MiniMax, Moonshot, GLM and
Qwen which are fully managed by the US hyperscalers. Besides the model
layer, into applications, worthy of note is Microsoft (covered by
Gabriela Borges) CEO’s recent remarks at a Wall Street Journal interview
(link) where he noted Microsoft is considering hosting versions of
DeepSeek on Copilot as an optional, cost-effective model which could
give its customers access to cheaper choices alongside US proprietary
models. Microsoft indicated that if it hosts DeepSeek, the model would
operate within its cloud ecosystem, ensuring customer data stays inside
Azure.
Noting key risks around any potential tighter geopolitical policies given Chinese AI models inroads into western markets.
Key
risks to the ‘going global’ opportunity will hinge on end market access
(especially in western countries, and with a focus on where computing
is done/data is stored), access to high-end computing for AI model
training (that could impact iteration pace and cost structure of Chinese
models), and restrictions on Chinese AI model companies could impact
supplier relationships/access to US companies and/or access to US
capital.
Who are best positioned to be the long term winners? Introducing our Competitive Positioning framework
Players with the largest ARR scale with a gross margin advantage + financial strength are expected to be the long-term winners.
ARR scale x gross margin advantage + financial strength
Financial strength (Balance sheet, Access to computing)
Accordingly, Goldman introduces a Chinese AI model Competitive Positioning framework based on pricing power, cost advantage and finance strength, overlaying
with token scale and market share progressions, and identify Knowledge
Atlas (Zhipu initiation) and DeepSeek (private) as the most strongly
positioned in foundation models, and Bytedance (private) in multi-modal
capabilities
Foundation models
Goldman assesses each key
player’s competitive positioning from its flagship foundation model,
scored across 3 aspects: pricing power, cost advantage and financial
strength (of the company), with each aspect further built upon granular
quantitative metrics.
Pricing Power
Time to market: Goldman
assesses this in terms of how quickly and effectively a player delivers
frontier competitive models. From comparing the launch date of the
company’s flagship models with its prior generation & other models
at similar performance level, Goldman assesses whether the release
delivers a meaningful capability step-up over its past generation, and
whether it narrows the gap to the current global SOTA models.
Arena score (overall text): Model
intelligence is viewed as the primary determinant of pricing power,
since models capable of handling higher-value tasks can deliver clearer
ROI and therefore sustain their pricing premium. Here, the LMArena’s
score is used instead of any static benchmark because it reflects a
large scale of blind user reviews, making it a more objective read on
the real-world capability.
Pricing (blended, US$ per 1M tokens): The
bank refer to the realized headline price (across input/output/cached
input) for flagship models, where sustained higher pricing with
iteration signals stronger pricing power, whereas pricing cuts may
indicate a more volume-prioritized strategy.
Cost advantage: the structural cost-to-serve (i.e. inference costs) that sets the floor for price and margin
The below metrics are referred to as proxies for inference efficiency:
Throughput (tokens/second): Measured
by the number of tokens a single GPU generates per second. The more
tokens a GPU outputs per second, the more fixed hourly compute cost is
spread out. Therefore, throughput is a strong indicator of inference
efficiency, which depends on model architecture (sparsity &
attention design) and serving efficiency (batching & utilization).
Cache hit rate (%): The
share of input tokens served from cache rather than recomputed. In
conversations and agentic workflows, much of the input repeats across
calls, so models can read those tokens from cache memory instead
of recomputing from new inputs. A higher rate cuts compute, and since
cached tokens are near-free to serve despite being billed at a discount
(10X-100X cheaper than input cost), it is also margin accretive.
Parameter size/activation ratio: The
share of total parameters activated per token (available for
open-source MoE models), where fewer active parameters mean fewer FLOPs
per token and therefore lower inference cost, at any given level of
performance.
Inference GPM (where disclosed, or GS estimates based on total parameter size/activated parameters): The realized gross margin on model API, where ~90% of COGS is inference costs, as a direct indication on cost efficiency
Financial strength: capacity to keep funding frontier R&D and training before a profitability turnaround
Cash on hand and net cash/debt as % of assets: Goldman
uses total cash on hand as a measure of balance sheet strength, with
net cash as % of assets to normalize across players of different scale.
Mega-caps (compared with individual players) are seen as having greater
resources to accumulate compute, fund multi-modal exploration and push
faster product distribution.
Valuation multiples: For
independents, P/ARR 2026E multiples are used as they are not yet
profitable and P/ARR is a more comparable measure of business scale and
future monetization potential, and for mega-caps the P/E 2026E is used.
Appendix
China's Key Players at a Glance: Mega-caps
China's Key Players at a Glance: Key Independent Players
