Imagine you are an explorer, and before you lies a map of the AI world in 2026. Your task: mark out the borders, strength, and specialties of every "kingdom."
This isn't a science fiction setup — it's the work completed by the easy-learn-ai project in commit e6c189a. By splitting model data into 20 independent vendor profiles, an unprecedented panoramic view of the AI ecosystem comes into focus.
Let me walk you through this map.
Tier One: The Trillion-Parameter Giants
OpenAI: The Pioneer Still Leads
The GPT series has evolved from 2023's GPT-4 (8K context) to today. In easy-learn-ai's database, OpenAI takes up the largest share — 981 lines of JSON, recording the full evolution from GPT-3.5 Turbo to the GPT-5 series.
An interesting data point: GPT-4 originally had only an 8K context window, while today Claude Opus 4.8 supports 1M (one million token) context. What does that mean? Feed it all seven Harry Potter books and it can still recall a detail mentioned in the first one. This leap in "long memory" capability is the core battleground of LLM competition in 2025–2026.
Anthropic: The Safety-Obsessed Faction
The Claude series has always taken a "safety first" route. The latest Claude Opus 4.8 (released May 2026) has an interesting feature called "adaptive thinking" — it dynamically adjusts its depth of reasoning based on question difficulty. Simple questions get instant answers; complex ones make it "pause and think" like a human.
On Terminal-Bench 2.0 (a hardcore AI coding benchmark), Claude Opus 4.6 achieved the highest score. Anthropic is proving with real capability that "safe" does not mean "weak."
Google: The Multimodal Native
The Gemini series was designed as multimodal from day one — text, images, audio, video, PDF, all handled in a unified way. Gemini 2.0 Flash's 1M context window (released February 2025) makes it a powerful tool for long documents. The latest Gemini 3.1 Pro scores 77.1% on the ARC-AGI-2 benchmark — a test dubbed "AI's IQ test," where average human performance is around 85% and early GPT-4 versions scored about 50%.
Tier Two: The Rise of Chinese Power
DeepSeek: The Open-Source Disruptor
If 2025 had a "dark horse of the year" in AI, it was DeepSeek.
DeepSeek-R1 (released January 2025) is a "reasoning model" — unlike ordinary models, it shows its full thought process before answering. Ask it a math question and it won't just give the answer; it derives it step by step on scratch paper like a student. This "chain-of-thought" approach brought it close to OpenAI o1 level on math and coding tasks.
But what truly stunned the industry was DeepSeek's open-source strategy. R1 itself is open, and they also released distilled versions — DeepSeek-R1-Distill-Qwen-1.5B, 7B, 14B, 32B… including versions small enough to run on your laptop. The 1.5B parameter version supports 128K context and 32K output, meaning you can run a "deep-thinking AI" locally, completely free.
In easy-learn-ai's database, DeepSeek occupies 487 lines — for a company only two years old, that volume shows how rich its product line has become.
Alibaba Qwen: Another Answer to Open Source
The Qwen series is Alibaba's LLM family. The latest Qwen3.5-Plus (released February 2026) uses a hybrid architecture: 397B total parameters but only 17B activated per inference — like a consulting team of 397 experts where only 17 join each answer. This "mixture of experts" (MoE) architecture keeps the model's capability massive while sharply reducing inference cost.
A striking number: Qwen3.5-Plus's API price is only 1/18 of Gemini 3 Pro's.
On the closed-source side, Qwen3.7-Max (May 2026) supports 1M context and 64K output, optimized specifically for Agent capabilities. Imagine an AI assistant that can read all your company's financial reports from the past five years in one go and then write you an investment analysis — that's what 1M context means.
Baidu ERNIE: The Veteran Holds On
ERNIE is Baidu's LLM product. ERNIE 4.5 (released 2025) has a unique advantage in Chinese understanding — Baidu commands the largest corpus of the Chinese internet. Its 362 lines of data record the evolution from ERNIE 3.0 to 4.5, along with the various ERNIE sizes.
ByteDance: The Latecomer's Ambition
ByteDance's Seed series takes up 518 lines in easy-learn-ai's data, but considering ByteDance's accumulated expertise in recommendation algorithms and short video, its models have a natural edge in multimodal understanding — especially video content. The Doubao model's penetration in the Chinese market is growing fast.
Moonshot: The Extreme Pursuer of Long Context
Kimi is Moonshot's product, famous for ultra-long context. Kimi K2 (2025) supports 256K context, and the latest versions are moving toward 1M. For users handling long documents — lawyers, researchers, analysts — Kimi is a very attractive option.
Tier Three: Vertical Specialists
Image Generation: Magic from Text to Visuals
- Black Forest Labs (FLUX): Only 56 lines of data, but FLUX is already a strong challenger to Stable Diffusion in open-source image generation, with clear advantages in detail and text rendering.
- Midjourney: 29 lines, but each represents an aesthetic benchmark in image generation. Midjourney V7's style control is beloved by designers.
- Stability AI: The Stable Diffusion series, the elder statesman of open-source image generation. 137 lines record the full journey from SD 1.4 to SDXL to SD3.
- Runway: 52 lines. Gen-3 Alpha's video generation turns "shooting movies with text" from sci-fi into reality.
- Pika: 48 lines. Pika 1.5's effects features (like turning a car in a video into a balloon) went viral on social media.
- Kling (Kuaishou): Kuaishou's video generation model, 51 lines. In Chinese-language video generation, Kling's understanding of the physical world is impressive.
- MiniMax: 221 lines. Hailuo AI's speech synthesis is very natural in Chinese-language scenarios.
- Zhipu AI: 570 lines. The GLM series is highly regarded in academia; ChatGLM was one of the earliest open-source conversational models in China.
- Tencent: The Hunyuan model, 353 lines. Unique advantages in multimodal understanding for gaming and social scenarios.
- GPT-4 (2023): 8K
- Claude Opus 4.8 (2026): 1M (a 128x increase)
- Qwen3.7-Max (2026): 1M
- Gemini 2.0 Flash (2025): 1M
- Writing code? Claude Opus 4.8 or DeepSeek-R1
- Processing very long documents? Gemini 2.0 Flash or Kimi
- Generating images? Midjourney or FLUX
- Chinese conversation? Qwen3.7-Max or ERNIE
- On a budget? Qwen3.5-Plus (priced at 1/18 of Gemini) or DeepSeek distilled versions (free, runs locally)
Video Generation: Bringing Static Worlds to Life
Other Chinese Players
Signals Behind the Data
Signal One: Chinese Open-Source Power Cannot Be Ignored
Of these 20 vendors, nearly half are Chinese companies (Alibaba, Baidu, ByteDance, DeepSeek, Moonshot, MiniMax, Zhipu, Tencent, Kuaishou). More crucially, Chinese open-source models (DeepSeek, Qwen, ChatGLM) now rival top Western models in quality and influence.
DeepSeek-R1's release even directly affected NVIDIA's stock price — as the market realized "high-performance AI doesn't necessarily require expensive hardware," the story of compute hegemony began to crack.
Signal Two: Context Length Is the New Arms Race
Look at a few numbers:
The exponential growth of context windows is essentially solving one core problem: giving AI "long-term memory." When AI can process an entire book, an entire codebase, or an entire database in one pass, the boundaries of its capabilities will be redefined.
Signal Three: Models Are Becoming "Toolified"
Note the modelTags field in easy-learn-ai's data. More and more models are tagged as supporting "tool use" — meaning AI is no longer just "chatting"; it can call calculators, query databases, send emails, and operate software. The arrival of the Agent era marks a fundamental shift: AI evolves from "answering questions" to "completing tasks."
What This Means for Ordinary People
You might ask: with so many models, what does this have to do with me?
The answer is: choice.
Two years ago, AI users had two options: use ChatGPT, or don't. Today, you can pick the best model for your specific needs:
Conclusion
The value of easy-learn-ai's "map" lies not in which technical parameters it records, but in showing us a complete ecosystem.
20 vendors, hundreds of models, spanning text/image/video/multimodal — this is not one company's product catalog; it is a portrait of an era. In this era, AI is no longer the专利 of some lab, but a technological wave advanced jointly by dozens of teams and thousands of engineers worldwide.
As users, all we need is a good map to find our own path across this vast AI continent.
Source commit: e6c189a