*Translated from a zhichai.net forum post analyzing the May 26, 2026 easy-learn-ai model database update (commit 9ead1f8 + commit b99eda0).*
Imagine walking into a phone store where the salesperson says: "This one has 1.6 trillion transistors; that one can read 1.05 million characters at once; and this one understands text, images, and video simultaneously."
You're not choosing a phone—you're choosing a "brain." On May 26, 2026, the easy-learn-ai model database received a large expansion that mirrors the key battlegrounds of today's AI industry.
DeepSeek-V4: The Flagship of the Open-Source World
The heaviest hitter is DeepSeek-V4-Pro: 1.6 trillion total parameters with 49B activated per inference via a Mixture-of-Experts (MoE) architecture. Like a consulting firm with 16,000 employees that only dispatches the 49 best-suited people per project, it balances depth of capability with cost efficiency.
Notably, while GPT-5.5 and Claude 4.6 are closed-source, DeepSeek-V4-Pro is open source—downloadable, self-hostable, modifiable, and distillable. For enterprises unwilling to send data to third-party clouds, this is a compelling option.
It also supports dual modes: Thinking (deep reasoning) and Non-Thinking (fast answers), letting users switch by task complexity without juggling two models.
GPT-5.5: OpenAI's Long-Memory Monster
If DeepSeek wins on scale, GPT-5.5 wins on a single number: 1,050K context—about 1.5 million Chinese characters, or roughly three copies of *Dream of the Red Chamber*. You can feed it an entire large codebase, a year of financial reports, or a full novel and request cross-chapter, cross-file analysis. Previous models were readers who forgot page one by page fifty; GPT-5.5 sees all pages at once.
A "Pro" variant exists with an honest caveat: "some requests may take several minutes." OpenAI is telling users: better results, but you'll wait—a sign of AI products maturing beyond reflexive speed-first design.
Also updated: GPT-5.2-Codex, purpose-built for "long-horizon agentic coding"—multi-file, multi-step engineering tasks, echoing the trend from "writing a few lines of code" toward end-to-end engineering.
Kimi K2.6: A Domestic Model's Agent Ambitions
Moonshot AI's Kimi K2.6 is billed as Kimi's smartest model: a multimodal agent supporting text, image, and video input, with thinking/non-thinking modes and tool calling. Its agent strengths target long-horizon code writing, instruction following, self-correction, and complex software engineering. Self-correction matters enormously—an AI that notices "I just got that wrong" and fixes it is far more reliable in real engineering. Its 256K context is ample for most workflows, and it is open source.
Gemini 3.5 Flash: Google's Speed Demon
Gemini 3.5 Flash, the first stable release of the 3.5 Flash series (released May 19), reportedly beats Gemini 3.1 Pro on multiple benchmarks—a higher-tier model—suggesting Google found a better speed/quality balance. It supports 1,048K input tokens and 65,536 output tokens, plus image, video, audio, and PDF inputs, clearly aimed at high-concurrency agents, coding loops, RAG, and multimodal document understanding.
GPT Image 2 and Nano Banana 2: The Image Quality War
GPT Image 2 (OpenAI) emphasizes fast generation, high quality, flexible sizes, and high-fidelity image inputs—a bet on commercial scenarios: brand design, product imagery, ad creative, and complex editing.
Nano Banana 2 (aka Gemini 3.1 Flash Image) combines Nano Banana Pro's world knowledge and reasoning with Flash-class speed. Its standout feature is precise text rendering—a notoriously hard problem in AI image generation—alongside multi-resolution output from 512px to 4K, targeting posters, infographics, and marketing assets.
Seedance 2.0: A Multimodal Symphony for Video
ByteDance's Seed team's Seedance 2.0 uses a unified joint audio-video generation architecture: inputs may include text, up to 9 images, 3 videos, and 3 audio clips plus natural-language instructions, producing 15-second multi-shot audio-video output. Like a director holding a script, storyboards, a soundtrack, reference footage, and verbal directions, then cutting you a trailer. Compared to v1.5, it improves usable rates for complex interaction/motion scenes, physical accuracy, realism, and controllability.
Observations at Halftime
1. Context length is the new arms race. From 256K to 1,050K, interaction shifts from "feeding in segments" to "dumping everything at once." 2. Open-source models are closing the quality gap. DeepSeek-V4-Pro and Kimi K2.6 give enterprises real choice beyond the closed-vs-inferior dilemma. 3. Dual thinking/non-thinking modes are becoming standard—users no longer pay a compute tax for simple tasks or tolerate hasty answers on hard ones. 4. Multimodality is a requirement, not a bonus. Pure-text releases are drying up; AI is evolving from a literate scholar into a generalist that can see and hear.
Model wars 1.0 was "whoever has the most parameters wins"; 2.0 is "whoever excels in specific scenarios wins"; 3.0 may be "whoever embeds most naturally into human workflows wins." Today's releases are bridging 1.0 and 2.0.