General-Purpose Large Model

General-purpose models lacking domain depth tend to give safe, broad responses and struggle to spark insights in professional contexts.

Enterprises prefer deploying specialized AI optimized for specific business scenarios rather than relying on general-purpose large models to solve all problems.

Task-specific models can allocate more parameters to the target task, whereas general-purpose large models need to distribute parameter capacity across tens of thousands of tasks, domains, and languages.

General-purpose large models are trained on massive internet text and excel at general language tasks, but on scientific problems such as protein structure prediction, climate simulation, and materials genome computation they often lack domain knowledge, provide insufficient support for specialized data formats, and struggle to guarantee numerical precision.

General-purpose large models (such as GPT and Gemini) possess broad knowledge and reasoning capabilities, but their training and inference costs are extremely high.

The key difference between specialized models and general large models is that specialized models learn a specific organization's decision preferences, risk judgment logic, and historical experience.

As general-purpose large models converge in capability, Agent differentiation increasingly depends on understanding of specific workflows.

Without circumventing the GFW and without using specific paid models, a general-purpose large model can likewise run a roughly equivalent video production pipeline.

General-purpose large models tend to be strong on plain text and tables, but weaker than dedicated OCR pipelines on layout and chart tasks.

The knowledge boundary of a general-purpose large model is determined by its training data, so it cannot answer questions related to non-public data such as internal enterprise documents, product prices, and customer information.

General-purpose large models tend to expose weaknesses in vertical scenarios, such as inaccurate understanding of terminology, broken domain reasoning chains, and output formats that do not conform to industry standards.

In vertical domains such as medical imaging, industrial quality inspection and remote sensing interpretation, a specialized model with few parameters, low inference cost and strong interpretability may be more cost-effective than calling a general large model API, and easier to pass regulatory review.

The training data of general large models deviates from the distribution of specific business tasks, whereas log data from real business covers the edge cases actual users may encounter.

On tasks such as text classification, information extraction and structured output, there are already many precedents of small models fine-tuned on high-quality data surpassing general large models.

For small and medium-sized AI companies, the path of independently building general-purpose large models is basically impassable, and a more realistic choice is vertical scenarios or lightweight applications based on open-source models.

Electronic signature and data integrity regulations (such as 21 CFR Part 11) further raise the barrier to system integration and are one of the main reasons general-purpose large models find it difficult to enter the life sciences field directly.

General-purpose large models are prone to hallucination problems in legal scenarios, such as fabricating case precedents, inventing statute numbers and confusing jurisdictions.

General-purpose large models can cover multiple extraction tasks through prompts, but each call is costly and high-latency.

The core tension in the current information extraction field lies in the trade-off between "the efficiency of specialized small models" and "the generalization of general-purpose large models."

A general-purpose large model receives only the prompt, without facts such as recipient information, so it can only output according to high-frequency patterns in its training corpus, thus producing a "one-size-fits-all" generic tone.