🧠 AI model library

About the AI Dash Model Library

The AI Dash Model Library continuously catalogs mainstream AI large models, covering major domestic and international vendors, and includes key information such as parameter count, context window, pricing, strengths, and limitations. Each model is tagged with its vendor, type, openness level, and recommended use cases—enabling efficient cross-model comparison and informed decision-making. The database is updated daily, with newly released models added as quickly as possible. We also publish in-depth reviews for select models, featuringIn-depth reviewsfive-dimensional scoring and hands-on user experience.

How to Choose the Right AI Model

Selecting a Model Starts with Three Considerations:Task Type(programming, writing, image generation, speech, or reasoning)Language scenario(Chinese-dominant or English-dominant)Deployment method(API invocation or local self-deployment). For Chinese content creation, prioritize domestic models; for programming and Agent scenarios, focus on long-context handling and tool-calling capabilities; for budget-sensitive high-volume tasks, select lighter models with lower per-unit cost. You can also use ourTool Comparison Engineto directly conduct horizontal comparisons across multiple models.

Key Model Evaluations

• GLM-5.2 in-depth review — Zhipu’s open-source programming model, ranked #1 among open-source general-purpose models
• GLM 5.3 in-depth review — Million-token context reasoning model, upgraded Agent capabilities
• DeepSeek V4 Pro In-Depth Evaluation — 1M context length + MoE architecture
• NVIDIA Nemotron 3 Ultra Evaluation — 550B open-source MoE model
• Qwen3.8 2.4T A95B Evaluation — 2.4-trillion-parameter open-weight model
• Claude Sonnet 5 in-depth review — Opus-level Agent capability