One Map of the AI World: The Hidden Cards and Landscape of 20 Model Vendors
Imagine you are an explorer, and spread before you is a map of the AI world in 2026. Your task: mark out the borders, strengths, and specialties of every "kingdom."
This isn't science fiction — 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.
First Tier: The Trillion-Parameter Giants
OpenAI: The Pioneer Still Leading
The GPT series has evolved from GPT-4 (8K context) in 2023 to today. In the easy-learn-ai database, OpenAI occupies the largest share — 981 lines of JSON, recording the complete evolution from GPT-3.5 Turbo to the GPT-5 series.
An interesting data point: GPT-4 originally had an 8K context window, while today Claude Opus 4.8 supports 1M (one million token) context. What does that mean? You could feed in all seven Harry Potter books and it would still remember a detail mentioned in the first one. This leap in "long memory" capability is the core battleground of 2025–2026 large model competition.
Anthropic: The Safety-Obsessed Perfectionist
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 thinking depth based on question difficulty. Simple questions get instant answers; complex ones get a human-like "pause to think."
On Terminal-Bench 2.0 (a hardcore benchmark testing AI coding ability), Claude Opus 4.6 scored the highest. Anthropic is proving with results that "safe" doesn't mean "weak."
Google: The Multimodal Native
The Gemini series was designed as multimodal from day one — text, images, audio, video, PDFs, handled in a unified way. Gemini 2.0 Flash's 1M context window (released February 2025) makes it a powerful tool for long documents. Meanwhile, the latest Gemini 3.1 Pro scored 77.1% on the ARC-AGI-2 benchmark — known as "AI's IQ test," where the human average is about 85% and early GPT-4 versions scored roughly 50%.
Second Tier: The Rise of Chinese Power
DeepSeek: The Open-Source Disruptor
If 2025 had a "dark horse of the year" in AI, it would surely be DeepSeek.
DeepSeek-R1 (released January 2025) is a "reasoning model" — unlike ordinary models, it shows its full chain of thought before answering. Ask it a math problem and it won't just give the answer; it derives it step by step like a student working through scratch paper. This Chain-of-Thought approach brings it close to OpenAI o1 on math and coding tasks.
But what truly shocked the industry was its open-source strategy. Not only is R1 itself open source — they released distilled versions too: DeepSeek-R1-Distill-Qwen-1.5B, 7B, 14B, 32B... versions small enough to run on your laptop. The 1.5B version supports 128K context and 32K output, meaning you can run a "deep-thinking AI" locally, completely free.
In the easy-learn-ai database, DeepSeek occupies 487 lines — for a company only two years old, that volume shows a rich product line.
Alibaba Qwen: Another Answer to Open Source
Qwen is Alibaba's large model 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 participate in any given answer. This Mixture-of-Experts (MoE) architecture keeps capability massive while slashing inference costs.
A striking number: Qwen3.5-Plus's API price is just 1/18 of Gemini 3 Pro's.
On the closed-source side, Qwen3.7-Max (May 2026) supports 1M context and 64K output, optimized for Agent capabilities. Imagine an AI assistant that can read all your company's financial reports from the past five years at once and write you an investment analysis — that's what 1M context means.
Baidu ERNIE: The Veteran Holding Its Ground
ERNIE is Baidu's large model product. ERNIE 4.5 (released 2025) has unique strengths in Chinese language understanding — Baidu holds the largest corpus of the Chinese internet. 362 lines of data record the evolution from ERNIE 3.0 to 4.5, plus the various sizes of the ERNIE series.
ByteDance: The Latecomer's Ambition
ByteDance's Seed series occupies 518 lines in the easy-learn-ai data. Considering ByteDance's expertise in recommendation algorithms and short video, its models have a natural edge in multimodal understanding — especially video content. Doubao's penetration in the Chinese market is rising fast.
Moonshot: The Extreme Long-Context Pursuer
Kimi, from Moonshot, is known for ultra-long context. Kimi K2 (2025) supports 256K context, and the latest versions are pushing toward 1M. For users handling long documents (lawyers, researchers, analysts), Kimi is a very attractive option.
Third Tier: Vertical Domain Specialists
Image Generation: Magic from Text to Visuals
- Black Forest Labs (FLUX): 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 makes "filming movies with text" a reality rather than sci-fi.
- Pika: 48 lines. Pika 1.5's special effects (like turning a car in a video into a balloon) went viral on social media.
- Kuaishou Kling: Kuaishou's video generation model, 51 lines. Its understanding of the physical world is impressive in Chinese video generation.
- MiniMax: 221 lines. Hailuo AI's speech synthesis is very natural in Chinese scenarios.
- Zhipu AI: 570 lines. The GLM series is highly regarded in academia; ChatGLM was one of China's earliest open-source chat models.
- Tencent: The Hunyuan large 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 (1/18 the price of Gemini) or DeepSeek distills (free, runs locally)
Video Generation: Bringing Static Worlds to Life
Other Chinese Players
Signals Behind the Data
Signal 1: 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 importantly, 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 2: Context Length Is the New Arms Race
Look at these 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, codebase, or database in one pass, its capability boundaries will be redefined.
Signal 3: Models Are Becoming "Toolified"
Note the modelTags field in the easy-learn-ai data. More and more models are tagged as supporting "tool use" — meaning AI no longer just chats; 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 It 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 the easy-learn-ai "map" isn't in what technical parameters it records, but that it shows us a complete ecosystem.
20 vendors, hundreds of models, covering text/image/video/multimodal — this isn't one company's product catalog; it's a portrait of an era. In this era, AI is no longer the patent of a single lab, but a technological wave driven jointly by dozens of teams and thousands of engineers worldwide.
And as users, all we need is a good map to find our own path across this vast AI continent.
Source commit: e6c189a