How to Choose the Right Large Language Model in 2025: A Six‑Dimension Guide

01 The Truth Behind the Numbers: China’s AI Scale Effect

The number of publicly known large models grew from a few dozen in early 2023 to 3,755 by mid‑2025, with Chinese companies contributing 1,509 models (over 40% of the global total). Data from the 2025 World AI Conference shows China’s AI market reached roughly ¥300 billion in 2024 and is projected to exceed ¥700 billion by 2026, while China now holds 60% of global AI patents. This indicates a shift from experimental toys to core infrastructure, with China taking a leading role.

02 Six‑Dimensional Classification: How to Slice 3,755 Models

Although the sheer count is overwhelming, the models can be organized along six axes.

Dimension 1 – Modality Capability

Pure Text : Writing, reasoning, coding – examples: GPT‑4, Claude, DeepSeek‑V3, Llama.

Multimodal : Image‑text understanding, video generation – examples: GPT‑4o, Gemini, Alibaba Qwen‑VL, Kuaishou Keling.

Embodied Intelligence : Robot control, physical interaction – examples: Figure AI, Zhiyuan Expedition A2, Yushu Go2.

Code‑Specialized : Algorithm generation, code completion – examples: GitHub Copilot, CodeLlama, Tongyi Lingma.

Trend: Pure‑text models are converging in ability (deflation), while multimodal and embodied models represent the next battleground.

Dimension 2 – Generalist vs Specialist (Application Scenario)

General‑base models act like a "generalist PhD" (e.g., GPT‑4, DeepSeek‑V3) that can converse on many topics but lack deep domain expertise. Vertical industry models are "specialist doctors":

Healthcare: Med‑PaLM, MedGPT (understand medical records, diagnose).

Finance: BloombergGPT, Ant Financial Model (read financial reports, perform risk control).

Legal: ChatLaw, LegalPilot (interpret statutes, draft contracts).

Education: iFlytek Spark Teacher Assistant (generate questions, grade assignments).

Recommendation for SMEs: combine an open‑source base model with vertical fine‑tuning for the best cost‑performance.

Dimension 3 – Openness (Data Sovereignty)

Closed‑source Commercial Model ←→ Open‑source Model
OpenAI GPT‑4      Meta Llama      Alibaba Qwen
Google Gemini      DeepSeek       Baidu Wenxin
Anthropic Claude   Baichuan AI    Zhipu ChatGLM

Closed‑source : Typically higher capability but requires uploading data to the cloud; suited for non‑sensitive workloads.

Open‑source : Allows private deployment, keeping data on‑premise; ideal for finance, government, and state‑owned enterprises.

Special note: For internal AI office platforms, an open‑source model with private deployment (e.g., Qwen, DeepSeek, ChatGLM) is mandatory.

Dimension 4 – Scale (Parameter Count)

Edge‑side Small Models (1B‑7B parameters): Run on phones or IoT devices; very low cost.

Medium Models (13B‑70B): Mainstream for enterprise private deployment; moderate cost.

Large Models (100B+): Accessed via cloud APIs; high cost.

Super‑Large Models (1T+): National‑level infrastructure; extremely high cost.

Trend: 7B‑parameter models distilled from larger predecessors now match the capabilities of 100B models from two years ago. A hybrid architecture that combines small and large models is expected to dominate.

Dimension 5 – Technological Generation

First Generation (2020‑2022) : Transformer architecture, only continuation tasks (GPT‑3, BERT).

Second Generation (2022‑2023) : Learned to follow instructions, conversational (ChatGPT, Claude).

Third Generation (2023‑2024) : Mixture‑of‑Experts (MoE) for higher efficiency (GPT‑4, DeepSeek‑V3).

Fourth Generation (2024‑present) : Native multimodal + deep reasoning (GPT‑4o, Gemini 2.0, OpenAI o3).

Advice: Choose models from the third generation onward; earlier generations are largely obsolete.

Dimension 6 – Deployment Form

Cloud API : Pay‑per‑token, fast prototyping (OpenAI, Alibaba Cloud Bailei).

Private Deployment : Local weight storage, suitable for sensitive data (Qwen, DeepSeek).

Edge Execution : Runs entirely offline on devices (Apple Intelligence, Xiaomi edge models).

Hybrid Architecture : Large model handles complex tasks, small model handles routine tasks.

03 Three‑Step Checklist for Enterprise Model Selection

Given the decision paralysis caused by thousands of models, follow these steps:

Step 1 – Define Red Lines

Can data leave the domain? Determines open‑source vs closed‑source.

What is the budget ceiling? Determines API‑based vs private deployment.

Step 2 – Choose the Base Model

General scenarios: DeepSeek‑V3, Qwen2.5‑Max, Llama 3.1.

Chinese‑optimized: Wenxin Yiyan, Tongyi Qwen, ChatGLM.

Code‑focused: CodeLlama, StarCoder.

Step 3 – Enhance

Use Retrieval‑Augmented Generation (RAG) to connect the model with corporate knowledge bases instead of training from scratch.

For sensitive use‑cases, deploy small models locally; for general use, call large‑model APIs.

04 Conclusion: From Quantity to Quality

Although 3,755 models exist, fewer than 5% reach production and generate real business value. By 2025 the competition will focus less on raw parameter counts and more on industry understanding, scenario fit, and cost efficiency. Enterprises should stop asking "whether to use" large models and start asking "how to select, apply, and achieve ROI".

Data sources: 2025 World AI Conference (WAIC), CCID Consulting, China Business Industry Research Institute.