Introduction
The post opens with an analogy: the AI model ecosystem used to be like an old-fashioned grocery store — everything piled together, navigable only by the owner who had memorized it. The easy-learn-ai project set out to fix this with a beginner-friendly LLM knowledge base, but rapid model releases (DeepSeek V4, GPT-5.5, Kimi K2.6, etc.) caused all information to pile into a single model.json file of over 5,000 lines.
The Refactor: Splitting the Shelf
A recent commit decomposed model.json into 20 independent brand-specific JSON files under src/data/models/:
alibaba.json— the Qwen familyanthropic.json— Claude modelsbaidu.json— ERNIE modelsblack-forest-labs.json— FLUX image modelsbytedance.json— ByteDance Seed modelsdeepseek.json— DeepSeek modelsgoogle.json— Gemini modelskuaishou.json— Kling video modelsmeta.json— Llama modelsmidjourney.json,minimax.json,moonshot.json,openai.json,pika.json,runway.json,stability-ai.json,tencent.json,xai.json,zhipu-ai.json- Full R1-Distill lineup (Qwen-based 1.5B / 7B / 14B / 32B / 70B) enabling local inference with complete chain-of-thought structure.
- V4 series (Pro and Flash): MoE with 1.6T total parameters but only 49B activated per token — cost-efficient at scale.
- Evolution: moonshot-v1-8k → K2 (1T-param open-source MoE) → K2.5 (Agent Swarm coordinating up to 100 sub-agents) → K2.6 (multimodal agent model).
- Kimi-Linear-48B uses linear attention to cut KV cache by 75% and speed up decoding 6x, with a 1M context window.
- Longest lineup: GPT-3.5 through GPT-5.5, o1 to o4-mini, Codex series, and GPT OSS open-source releases (120B/20B, Apache 2.0).
- GPT-5.5: 1050K context; GPT-5.5 Pro for hard problems; GPT-5.3-Codex for coding.
- "Minimal reasoning" option in GPT-5 series lets users dial reasoning effort from none to deep.
- Qwen3.5-Plus: 397B total / 17B active parameters, claimed to beat the trillion-param Qwen3-Max at 1/18 the API price of Gemini 3 Pro.
- Full size coverage from 0.5B lightweight to 3.7-Max flagship, open and closed, text to multimodal.
- Seed-OSS-36B-Base released open source: 512K context, 12T training tokens, adjustable thinking budget.
- Doubao-Seed-Code: 256K context, image/video understanding, chain-of-thought plus tool calling; plus doubao-1.5-pro/vision/lite variants.
- easy-learn-ai project commit e6c189a
- DeepSeek API docs: https://api-docs.deepseek.com
- Kimi platform: https://platform.moonshot.cn
- OpenAI models docs: https://platform.openai.com/docs/models
- Google Gemini API docs: https://ai.google.dev/gemini-api/docs/models
- Alibaba Qwen model list: https://help.aliyun.com/zh/model-studio/text-generation-model/
Each file records model parameters, context windows, capability tags, release dates, and links.
Highlights From Selected Brands
DeepSeek
Moonshot (Kimi)
OpenAI
Alibaba (Qwen)
ByteDance (Seed)
Three Industry Trends
1. 1M-token context is the new baseline — DeepSeek V4, Gemini 2.0 Flash, Kimi-Linear-48B, GPT-4.1/5, and Qwen3.6-Plus all offer 1M (roughly 750k Chinese characters, ~3 copies of Dream of the Red Chamber), enabling whole-novel analysis, full codebase review, or year-long financial trend analysis in one pass.
2. MoE is now standard — DeepSeek V3 (671B total / 37B active), Kimi K2 (1T / 32B), Qwen3.5-Plus (397B / 17B), GPT OSS (120B). Like a hospital triaging specialists, MoE routes each input to relevant "experts."
3. Reasoning modes are a standard knob — DeepSeek V3.2/V4 Thinking/Non-Thinking, OpenAI reasoning.effort from none to xhigh, Kimi K2-thinking, Baidu ERNIE-5.x-Thinking. Users can now trade latency for depth per task.
Why the Map Matters
The refactor is more than code cleanup: it makes the ecosystem navigable and the project sustainable. New models become a single entry in a brand file; new series become a new file. A 5,000-line monolith becomes a parallel-maintainable system — like a city growing from one main road into a grid.