1. What Happened in the Model World These Past Six Months
Let's start with a quick run through the timeline to get a feel for the pace.
In November 2025, Google released Gemini 3, which topped the major arena leaderboards; the same month, Anthropic's Claude Opus 4.5 became the first model to break 80% on a real-world software engineering benchmark. In December, OpenAI launched GPT-5.2. In February 2026, Anthropic and OpenAI released new models on the same day; in April, GPT-5.5 and Claude Opus 4.7 arrived in quick succession; in June, Anthropic released its next-generation flagship, Claude Fable 5, which it says set new records on nearly every benchmark.
The Chinese camp kept an equally dense schedule: DeepSeek released a preview of its 1.6-trillion-parameter V4 in April, Moonshot AI's Kimi K2 Thinking edged past the American closed-source flagships on agentic benchmarks for the first time in November 2025, and Zhipu AI's GLM-5 went head-to-head with the overseas first tier under an open-source license. Data from public routing platforms shows that in mid-February 2026, the weekly volume processed by Chinese models surpassed that of American models for the first time.
Three structural shifts are hidden in this calendar. First, capabilities are converging: on coding benchmarks, the gap between open-source models and the strongest closed-source models has narrowed to within one percentage point, and "which model to use" increasingly feels like "which cloud to use." Second, costs are collapsing: the cost of processing one million tokens has fallen by more than 99% over three years. Third, pricing is diverging: new American flagships have raised prices against the trend, while Chinese vendors have shifted from price wars to charging — general intelligence is being commoditized at the mid-tier and repriced as scarce at the frontier.
2. After Model Convergence, Where Does the Value Live?
If model capabilities can be swapped at any time and purchased by the token, then the moat of an AI application clearly is not "which model it plugs into." This is not a new argument, but the first half of 2026 turned it into a reality staring us in the face: the industry began openly discussing model commoditization, and the venture-capital consensus is shifting too — what determines the value of a vertical AI company is the compounding assets outside the model: proprietary data, workflow coverage, and industry understanding.
We have bet on this judgment since the day we founded Tianxia Gongchang, and the reasons can be stated very concretely.
A general-purpose model knows what "injection molding" is, but it cannot answer "which factories in Zhejiang do medical-grade injection molding with monthly capacity above 500,000 units" — the answer to that question does not live in internet corpora; it lives in a continuously verified factory database. We have spent years building and continuously updating a data foundation covering 4.8 million real factories in active production, and its core capability is distinguishing "companies registered with a manufacturing license" from "factories actually in production." Models turn over a generation every three months; this data asset only appreciates with time.
Likewise, a general-purpose model can write an elegant piece of industry analysis, but it does not know what a salesperson selling industrial lubricating grease actually needs: not analysis, but a target factory list sorted by scale, region, and process, with verifiable facts behind the list. Only when a large model's language understanding is plugged into real data and real workflows does it become productivity.
3. What Model Progress Means for Us
There is a common worry: as general-purpose models get stronger, will vertical applications get absorbed along the way? Our view is exactly the opposite — every advance in frontier models is a free upgrade for vertical AI.
Tianxia Gongchang AI's multi-turn requirement clarification, its understanding of fuzzy category terms, its data-backed follow-up questions mid-conversation — the ceiling of these capabilities rises with the underlying model. Colloquial requests that two years ago required carefully designed rules just to barely handle ("find a factory that can make squishy toys, that kind of stress-relief toy") are reliably understood by today's models; what no model can ever replace is the data that tells you "factories of this kind are concentrated in Shantou and Dongguan, and here is how many of them are actually in production."
So the fiercer the model race, the more we benefit: the price of intelligence is falling, and we happen to have built our product on the things outside intelligence.
4. A Note at Mid-2026
The industry loves to talk about "year one of AI applications." By our own experience, year one for manufacturing B2B began when models learned to "look it up in the database when they don't know" — generating a fluent paragraph is nothing special; only when every number behind the answer traces back to a verifiable factory entity does it deserve the trust of production settings.
Over the next six months, models will turn over the leaderboards a few more times. Our work will not change: making the data on 4.8 million real factories more accurate and fresher to update, and making the experience of finding factories through conversation feel closer to a seasoned colleague who knows the trade. You are welcome to test this judgment at Tianxia Gongchang AI.