Overview
A daily-update commit in the easy-learn-ai project—a website that helps ordinary people understand AI models—did something deceptively mundane: it split a single 5,005-line JSON file containing all model data into 19 separate files. The post argues this refactor is a mirror reflecting the state of the AI industry in 2026.
The Problem: An Unusable "Model Yellow Pages"
Originally, all model metadata lived in one JSON file (5,005 lines), plus separate files for image-generation and video models. With the explosion of AI models—Alibaba alone has nearly thirty (Qwen3.5-Plus, Qwen3.7-Max, Qwen3-VL, QwQ-32B, Qwen-Image, Wan2.5, etc.), plus DeepSeek, Google Gemini, Anthropic Claude, Baidu ERNIE, ByteDance Seed, Kuaishou Kolors, Meta Llama, Moonshot Kimi—editing a single file meant scrolling for minutes, risking broken data, and reviewing multi-thousand-line diffs. As the author puts it: this is no longer a technical problem, but an organizational one.
The Fix: Split by Vendor + Dynamic Loading
The refactor split the data into 19 files, one per company/organization. The data loader no longer imports files manually; it uses a Webpack feature (require.context-style) to automatically scan the directory and concatenate all JSON files into one list:
> "Grab every .json file in that folder, parse them into an array, and merge into one big list."
Consequence: adding a new vendor requires zero code changes—just drop a new JSON file in the directory. This is a "convention over configuration" philosophy: the folder is the API.
What the Data Reveals About the Industry
China holds half the map
Nine of the 19 vendors are mainland Chinese: Alibaba, Baidu, ByteDance, DeepSeek, Kuaishou, MiniMax, Moonshot, Tencent, and Zhipu AI. In 2023, such a catalog would have been ~80% American companies; now China and the US are roughly even, with China leading in areas like open-source video generation and long context.The open/closed "split personality"
Alibaba ships both open (Qwen3.5-Plus, 397B MoE under Apache; Qwen-Image, 20B MMDiT) and closed models (Qwen3.7-Max with 1M context; Qwen Image Plus via commercial API). DeepSeek open-sources everything from 1.5B distilled models to the 1.6T-parameter V4-Pro. "Open-source for traffic + closed-source for revenue" is now the standard playbook for Chinese LLM companies.MoE is the new baseline
- Qwen3.5-Plus: 397B total / 17B activated
- DeepSeek-V4-Pro: 1.6T total / 49B activated
- DeepSeek-V4-Flash: 284B total / 13B activated
- Wan2.2: 27B total / 14B activated at inference
The context-window arms race
| Model | Context window | |-------|----------------| | Qwen3.6-Plus | 1M tokens | | DeepSeek-V4-Pro | 1M tokens | | Kimi K2.5 | 2.56M tokens | | Claude 4 | 200K+ tokens |
1M tokens ≈ a 3-million-character Chinese novel—enough to ingest the entire *Three-Body Problem* trilogy.
Why It Matters
easy-learn-ai doesn't train models or ship products; it only organizes and presents. When its catalog outgrew 3 files and required dynamic modular loading, it signaled that AI models now number enough to demand infrastructure-grade management—an indicator of the ecosystem moving from its "pioneer era" to an "industrial era." Good architecture, the author notes, makes it easier for others to participate: contribute a new model by adding one conventionally-structured file and opening a PR.
Epilogue
The commit was submitted by lishiqi, an engineer at ByteDance, at 22:55 on a Sunday night. No flashy features—just quietly dividing an unflippable yellow pages into nineteen slim volumes. The author suggests that looking back at 2026's open-source AI history, this may be the year even "organizing model information" required purpose-built architecture.