📌 Source Commit: e6c189a
📌 Project: easy-learn-ai
> Imagine walking into a library. In the past, there was only one giant bookshelf holding a 5,000-page tome called *The Complete Guide to Global AI Models*. Finding information on a specific model meant climbing a ladder, digging through a table of contents, and hunting for a needle in a haystack. > > Today, you push the door open and everything has changed — 19 independent bookshelves, neatly arranged, each labeled with a vendor's name: OpenAI, Google, Alibaba, DeepSeek, Anthropic… Every model card on each shelf is carefully organized, annotated with release dates, capability tags, context window sizes, and even links to related papers.
This is the core work of the latest commit in the easy-learn-ai project: transforming one thick book into a clearly categorized museum of models.
1. This Refactor Is About Much More Than "Splitting Files"
On the surface, this is a code-level restructuring: more than 5,000 lines of data originally packed into a single large file were split into 19 independent JSON files, one per vendor.
But viewed from a higher vantage point, this change reflects several of the deepest structural shifts in the AI industry from 2024 to 2026.
🔧 Architecture: From "Centralized" to "Federal"
In the early days of AI (2022–2023), models and players were few. Cramming all model info into one file was reasonable — OpenAI GPT-3.5, GPT-4, plus a handful of open-source models, countable on one hand.
By 2026, the picture is completely different. The database now covers 19 vendors and nearly 100 models across the US, China, and Europe. The single-file model has become technical debt: every update touches the same giant file, merge conflicts are common, and new contributors are discouraged.
The split architecture has clear benefits:
- Parallel maintenance: updating Alibaba's Qwen series never touches OpenAI's file
- Independent evolution: each vendor can extend fields its own way (image models need output resolution; text models need context length)
- Clear ownership: issues can be quickly traced to a specific vendor's data file
- Flagship: GPT-4.1 (1M context, 32K output) — maximum intelligence
- Mid-tier: GPT-4o (128K context, multimodal) — balancing performance and cost
- Lightweight: GPT-4o mini / GPT-4.1 nano — low-cost scenarios
- Qwen3.5-Plus: 397B total parameters with only 17B activated via MoE, MMLU-Pro of 87.8%, API priced at 1/18 of Gemini 3 Pro
- Qwen3.6/3.7 series: 1M context, 64K output, 80K thinking budget — directly targeting international flagships
- Dual open/closed tracks: open versions serve the community; closed versions serve enterprises
It's a "federalist" mindset — the center no longer does everything; members self-govern under a shared protocol.
2. The Data Contains a Map of the AI World
Reading through the 19 files reveals a clear map of global AI power.
🇺🇸 United States: Full-Stack Ambition Built on First-Mover Advantage
OpenAI's file is nearly 1,000 lines — the largest in the database. From GPT-3.5 Turbo to the GPT-4.1 series, OpenAI shows a classic tiered strategy:
This "large-mid-small" three-tier structure is now the standard playbook for leading vendors.
Google's Gemini series shows a different route: differentiation via context window. Gemini 2.5 Pro and 3.5 Flash both offer 1M-token contexts — enough to feed an entire book in one shot. For long novels, legal contract review, and codebase-wide analysis, context length is productivity.
Anthropic's Claude series takes a "deep reasoning" path. Claude Opus 4.8 supports 1M context and 128K output, but the real selling point is adaptive thinking — the model dynamically adjusts reasoning depth to question difficulty. Simple questions get instant answers; hard ones get careful deliberation. This "elastic intelligence" may define the next generation of AI.
xAI's Grok series has less data but moves fast: open-sourcing Grok-1 (314B parameters) in March 2024, reaching Grok 4 by August 2025. Musk's company is known for speed — but whether it can match competitors on quality can't be answered by the data file alone; it requires real benchmark results.
🇨🇳 China: The Open-Source + Price-Performance Combo
Alibaba's Qwen series may be the most impressive domestic model family in this update. From Qwen3-235B-A22B to Qwen3.7-Max, the iteration speed is remarkable:
DeepSeek's R1 series represents another Chinese route: extreme open-source + a distillation ecosystem. R1 is a reasoning model (built specifically to "think"), and DeepSeek did something clever — distilled it into 7 versions at different scales, from 1.5B to 70B parameters.
What does that mean? A small business can run a 7B or 14B R1 distill on its own servers, achieving reasoning close to o1-mini, without calling expensive APIs. This "democratization of high-end capability" could profoundly change how AI applications are deployed.
Moonshot AI (Kimi), a leading startup, took the long-context route. moonshot-v1-128k offered 128K context as early as early 2024, with vision versions supporting image understanding. Kimi's productization has always been strong among startups.
Zhipu AI's GLM series, Baidu's ERNIE, Tencent's Hunyuan, and ByteDance's Seed are all documented in detail. The richness of China's AI ecosystem now fully rivals America's.
🌍 Europe and Beyond: Small but Excellent Vertical Players
Black Forest Labs (Germany) is the star of image generation with its FLUX series. FLUX.2 [pro] uses a Rectified Flow Transformer architecture, supports multi-reference images (up to 10) and JSON-structured control, with up to 4MP output. Founded by the original Stable Diffusion team, it represents Europe's top strength in AI vision.
Stability AI, Midjourney, Runway, Pika, and other image/video generation vendors each have dedicated entries. AI creative tools have gone from "a field" to "a complete industry."
3. Five Technical Trends Worth Watching
1. The Context Window Arms Race Is in Full Swing
In 2023, 8K context was mainstream. In 2026, 1M (one million token) context appears in multiple flagships: Gemini 2.5 Pro, GPT-4.1 series, Qwen3.6-Plus, Claude Opus 4.8…
What is 1M tokens? Roughly 2 million Chinese characters, or 3–4 copies of *Dream of the Red Chamber*. You can throw an entire codebase, legal contract, or textbook at the model for one-shot comprehension.
2. "Thinking Modes" Become Standard
DeepSeek-R1, Qwen3-Thinking, Gemini 3.1 Pro… more and more models distinguish "fast thinking" from "slow thinking." Fast for everyday dialogue; slow for math, code, and logic. This resembles the brain's dual-system theory: System 1 (intuition) and System 2 (reasoning). AI is learning to switch modes by task difficulty, like humans.
3. MoE Architecture Is the Large Model's "Invisibility Cloak"
Qwen3.5-Plus has 397B total parameters but activates only 17B per inference. That's the magic of Mixture-of-Experts — splitting a big model into many small experts, invoking only the needed few, preserving capability while cutting inference cost. Like a hospital triage system: not every department consults every patient. MoE may become the default for all future large models.
4. Multimodality Is No Longer a Bonus — It's the Entry Ticket
In 2024, a model could choose to be text-only. In 2026, almost no flagship lacks image understanding. The Gemini series natively supports video, audio, and PDF input. AI is evolving from "text processor" to "world understander."
5. Distillation Brings Frontier Capability to the Masses
DeepSeek-R1's distilled series is the prime example: a 1.5B model (runnable on a phone) achieves reasoning close to o1-mini through distillation. This shatters the "bigger = better" myth — what matters isn't parameter count, but the efficiency of knowledge transfer.
4. easy-learn-ai Itself Is Worth Watching
Finally, about the project. easy-learn-ai's mission is clear: making AI knowledge easy to access and understand. It doesn't build models; it builds the "map of models" — helping developers and learners quickly learn: what models exist, what each is good at, and how to choose.
This commit's database refactor is preparation for future expansion. As models grow from dozens to hundreds and new vendors keep emerging, a sound data architecture is the project's lifeline.
From single-file to multi-file, from small data to big data, from "I can remember it" to "I need systematic management" — this is not just a code change, but a microcosm of the AI industry moving from "wild growth" to "refined management."
5. Closing Thoughts
If you've ever felt the world of AI models is too complex and chaotic to know where to start — that feeling is justified. The world really is changing, and fast.
The good news: someone is doing the organizing work. Projects like easy-learn-ai are drawing maps for the chaotic AI landscape. With a map, getting lost becomes far less likely.
Next time you need to pick a model for a project, think about the data seen today: How long is the context window? Multimodal or not? Open or closed source? What's the price? The answers are all on that map.
> "In an age of information explosion, organizing information is itself a form of creativity."