China's AI company Moonshot unveiled its new flagship model "Kimi K3" —and in independent tests, it’s nearly on par with the best models from Anthropic and OpenAIin independent tests, which may have contributed to the sharp decline in AI stock prices.

What happened?

Moonshot AI, a Chinese AI company with a valuation of approximately $31.5 billion, has unveiled its new model, Kimi K3. By comparison, Anthropic and OpenAI are each projected to be valued at over $1 trillion —more than 30 times that amount.

Key figures:

Size: 2.8 trillion parameters (= the “adjustment screws” that store the model’s knowledge—roughly speaking: the more there are, the more powerful the model, but also the more expensive it is to run). This makes K3 the largest model ever to come out of China.

Open-Weight: Moonshot plans to make the model available for free download by July 27—companies could then run it on their own hardware without paying licensing fees to Moonshot. As of now, however, it’s only available through Moonshot’s paid interface.

Context: Capable of processing 1 million tokens at a time (tokens = text units; 1 million tokens correspond roughly to 750,000 words).

Unknown: The actual training costs. Anthropic also published evidence in February 2026 that Chinese labs had copied U.S. models via “distillation” (= a model learns by querying and imitating vast amounts of responses from another model). Whether and to what extent this plays a role in K3 remains unclear.

The comparison: How much do the models cost—and how smart are they?

Pricing is per 1 million tokens, broken down by input (what you send to the model) and output (what it responds with):

Kimi K3 (Moonshot): $3 input / $15 output – Intelligence Index: 57

Claude Fable 5 (Anthropic): $10 / $50 – Intelligence Index: 60 (1st place)

GPT-5.6 Sol (OpenAI): $5 / $30 – Intelligence Index: 59

Claude Opus 4.8 (Anthropic): $5 / $25 – Intelligence Index: 56

Grok 4.5 (xAI): $2 / $6 – the most affordable model among the top performers

DeepSeek V4 Pro (China): approx. $0.44 / $0.87 – low-cost segment, but index only 44

The Intelligence Index comes from Artificial Analysis, an independent testing provider that compares models across nine task areas (programming, logic, knowledge work) on a scale of up to 100.

Strengths – K3 trails the leader, Fable 5, by only 3 points, but costs only one-third as much per output token ($15 instead of $50) and half as much as GPT-5.6 Sol. In Artificial Analysis’s practical test, completing a task with K3 cost an average of $0.94—compared to $1.04 for GPT-5.6 Sol and $1.80 for Opus 4.8. When it comes to building web interfaces, K3 even took first place in blind tests with real developers.

Weakness – K3 isn’t quite as cheap as the headline suggests: It costs three to four times as much as its predecessor, is the most expensive model among its Chinese competitors, and, according to tests, “burns” up to 1.9 times as many tokens as GPT-5.6 Sol for the same task—so the cost advantage per token effectively disappears in practice. And the promised free model weights have not yet been released.

What could this mean for the market?

The AI stock boom hangs by a thread: U.S. labs like Anthropic, OpenAI, and xAI have committed to spending hundreds of billions of dollars—on chips, data centers, and electricity. Chip manufacturers, data center operators, energy providers, and construction companies have seen their stock valuations rise because this money is expected to flow to them. The plan is to refinance this through high prices for cutting-edge AI.

If a model now delivers nearly the same performance for a fraction of the price—and is soon expected to be available for free download—it could create the impression that the U.S. labs’ pricing power is crumbling (“commoditization of intelligence”: cutting-edge AI is becoming an interchangeable mass-market commodity). This wouldn’t immediately cost revenue, but it sows doubt about the business case behind the massive investments—and in overheated market phases, even a single doubt can trigger a chain reaction.

Opportunities – Cheaper AI could accelerate AI adoption overall: More adoption means greater demand for computing power for ongoing operations (inference)—and K3 is just as hardware-intensive in operation as U.S. models. Chip and infrastructure providers would benefit from this, regardless of which model is running.

The latest quarterly figures support this interpretation: On Tuesday, ASML raised its annual forecast for the second time (to €43–45 billion in revenue) and reported that its capacity is nearly fully booked through 2027. On Wednesday, TSMC increased its capital expenditure budget to a record-high $60–64 billion and stated that investments over the next three years would once again be significantly higher than those of the past three years—and that it is monitoring the construction progress of data centers to ensure its own chips do not end up in inventory. This can be interpreted as a sign that the demand side remains strong.

Risks – If price compression were to actually hit U.S. labs, training investments could be stretched out or cut back—which would eventually affect the recipients of this spending (chips, data centers, energy). Furthermore, those who train using distillation save precisely the billions in GPU costs that make the U.S. approach so expensive. The cost bases of the providers are therefore not directly comparable, but the price risk for U.S. labs still exists. A countervailing factor is that Chinese models are unlikely to be considered for security-critical U.S. applications (government agencies, robotics) due to potential backdoors.

Context

The bottom line, in my view, is that Kimi K3 is less a “DeepSeek moment 2.0” than a reality check for the valuation logic:

In 2025, the question was which chips China would use for training—now the question is whether cutting-edge intelligence permanently justifies premium prices.

The supply chain’s response (ASML, TSMC) has been clear so far: demand exists—the response at the model level is less certain.

Limitations

Initial benchmarks are partly based on manufacturer specifications = independent long-term tests are still pending.

Training costs and the potential distillation ratio for K3 cannot be verified.

Moonshot’s valuation ($31.5 billion) is from a private funding round; the trillion-dollar valuations of Anthropic and OpenAI are prospective—neither is directly comparable to market capitalizations.

Sources:

Artificial Analysis – Kimi K3 Benchmarks: https://x.com/ArtificialAnlys/status/2077832874183860404

OpenRouter – Kimi K3 Prices & Specs: https://openrouter.ai/moonshotai/kimi-k3

Anthropic – Official API Pricing Documentation: https://platform.claude.com/docs/en/about-claude/pricing

OpenRouter – Grok 4.5 Pricing: https://openrouter.ai/x-ai/grok-4.5

Price Comparison: Kimi K3 / Fable 5 / GPT-5.6 Sol: https://drawpie.com/blog/kimi-k3-vs-fable-5-vs-gpt-5-6-sol-price-benchmarks/

Decrypt – Cost per Task in Benchmark Comparison: https://decrypt.co/373716/china-kimi-k3-largest-open-source-ai-model-ever-beats-claude-fable-gpt-5-6-sol

The Decoder – K3 Classification & Price Level: https://the-decoder.com/kimis-open-model-k3-nears-gpt-5-6-sol-and-fable-5-while-signaling-the-end-of-super-cheap-chinese-ai/

ASML – Q2 2026 Earnings Call Transcript: https://www.investing.com/news/transcripts/earnings-call-transcript-asml-q2-2026-beats-guidance-as-ai-demand-lifts-outlook-93CH-4792156

TSMC – Q2 2026 Earnings Call Transcript: https://www.investing.com/news/transcripts/earnings-call-transcript-tsmc-lifts-2026-outlook-as-ai-demand-stays-hot-in-q2-2026-93CH-4794777

Anthropic – Report on Distillation Attacks (February 2026): https://anthropic.com/news/detecting-and-preventing-distillation-attacks

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The $META (-0.19%) share has not been performing for some time now, despite the growth spurt resulting from the use of AI. The analysts' current price target is currently +35%. The market is punishing Meta for its very high investments in AI infrastructure. This is understandable, as high expenditure puts pressure on margins. However, the market is largely ignoring the potential return on these investments. If the AI offensive works, it will not only protect the core business, but also make it significantly more profitable. Why I $META (-0.19%) attractive and what reasons speak for a positive future:

1. the core business

Social media has become the central advertising channel of the global economy, growing by >30% per year. Today, companies of all sizes search for and find their customers primarily on Instagram, Facebook and, in the future, Threads. Usage is habit-based and cross-platform; users are not active on either TikTok or Meta, but usually in parallel.

$META (-0.19%) has a reach with its apps that can hardly be replicated. This makes the advertising business highly profitable and extremely scalable. Even without new products, this is an excellent business.

2. two major monetization levers are only just beginning

WhatsApp and Threads have been built up for years, but have hardly been monetized until now. That is now changing:

- WhatsApp Business and cloud solutions are becoming an infrastructure for commerce and customer service

- Threads is gradually building an advertising model and ideally complements the Instagram ecosystem for text-based reach as a competitor to X.

Neither of these are bets on new markets, but the logical expansion of existing user relationships.

3. meta AI

Skepticism about the massive investments in AI infrastructure and models is understandable. Strategically, however, I see it differently: Meta is the first major operational (software) beneficiary of AI, not because it sells models, but because AI makes its own advertising machine better.

Better targeting, automated ad creation and more efficient playout increase the return on ad spend for advertising customers. This leads directly to higher budgets for Meta. A cycle is created: more investment in infrastructure leads to more computing power, which leads to better models, better models lead to better ads, better ads lead to more revenue, and more revenue finances the next round of investment.

In addition, Meta wants to use AI specifically for personal AI and for the development of new, independent apps. The massive expansion of its own cloud capacity is a double competitive advantage. Firstly, it accelerates the training and improvement of the company's own models (Llama, Muse Spark). Secondly, computing power itself becomes digital gold because it is the key bottleneck in the market. If you have capacity, you can develop, implement and scale products faster.

Theoretically, Meta could also rent out excess capacity to third parties and thus build up an additional cloud business. However, I don't see this as a strategic direction. The real value lies in consistently using the infrastructure for your own ecosystem and thus increasing the distance to competitors.

The feedback to Other Bets is also particularly interesting. The further development of AI glasses and the VR business not only benefits from the company's own AI infrastructure, it also strengthens the core business. Both areas provide additional, context-rich usage data and create new areas of interaction that further improve targeting, personalization and ultimately the monetization of the advertising platform.

4. financial strength allows strategic patience

$META (-0.19%) finances these investments largely from its own free cash flow. The company has low debt and is highly profitable. This gives the management the opportunity to think long-term, even if the market fears short-term pressure on margins.

Risks that I consciously take:

- Around 98 percent of sales depend on the advertising market. In a recession, the first thing to be cut is marketing, which would hit Meta directly.

- Regulation in the EU around personalized advertising remains an ongoing issue.

- Although the regulatory risk of increasing minimum ages for use has a negative impact on user numbers, it only has a marginal economic impact on Meta because minors do not account for a significant proportion of revenue.

- The allocation of capital is heavily dependent on Mark Zuckerberg, and the success of the AI expenditure has not yet been proven.

These points are well known and weigh on the valuation. Nevertheless, the risk/reward ratio is positive for me.

I am holding $META (-0.19%)because I get two things at the same time. Firstly, a core business that is hardly vulnerable even without AI and generates enormous cash flows. Secondly, a cost-intensive but strategically correct AI offensive, the success of which has hardly been recognized by the market to date. If Meta succeeds in making the advertising platform noticeably more efficient through AI, improving its models and applications and monetizing WhatsApp and threads, I believe that large share price gains are realistic in the long term.

No investment advice

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