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Model Selection Recommendation Framework: Recommending Different Model Combinations by Task Type (Generation / Retrieval / Classification / Multimodal)

Enterprise model selection should not rely solely on leaderboards — it should first consider the task type.

This content can be retained, but it should be made clear that it belongs to a “public source-based selection framework” rather than an official Dify model recommendation matrix. Public sources are already sufficient to support a task-oriented model classification approach: Dify officially distinguishes between model providers, model capability integration, and workspace-level configuration; community articles extensively discuss different task structures including generation, classification, RAG, Agent, and multimodal. Therefore, organizing a selection framework by task type is reasonable.

1. Selection Premises Confirmed by Public Sources

1. Model Selection Depends First on the Task, Not the Brand

Public technical articles have repeatedly reminded us that “generative AI” is not a unified capability set. Generation, retrieval augmentation, classification, multimodal, and Agent reasoning all have different focus areas, so a single set of criteria cannot be used to select all models.

2. Dify’s Public Structure Supports Multi-Model Coexistence

The Model Providers and model integration documentation already demonstrate that Dify is not a platform designed for a single model. This inherently supports the approach of “combining models by task.”

3. Enterprise Selection Is Typically a Combination Problem, Not a Single-Choice Problem

The most common path in public practice is:

  • Powerful models handle complex generation
  • Lightweight models handle classification / rewriting
  • Embedding / rerank / OCR / VLM serve specialized capabilities

2. Generation Tasks

Focus on: expression quality, long-text capability, structured output stability.

3. Retrieval-Augmented Tasks

Focus on: embedding, rerank, long-context reading capability.

4. Classification Tasks

Focus on: low cost, stability, controllable output.

5. Multimodal Tasks

Focus on: image / PDF / table comprehension capability and cost.

6. Combination Recommendations

Enterprises typically do not use just one model, but rather:

  • Heavy models for complex judgment
  • Lightweight models for classification and rewriting
  • Specialized models for embedding / rerank / OCR / VLM

7. Conclusion

The key to model selection is not “the strongest” but “whether it matches the task structure.”

Public Source References

note.com

  • No particularly strong direct hits on note.com at this time. Current evidence is better drawn from Dify Model Providers and general Japanese technical selection articles.

zenn.dev / Official Documentation / Other Public Sources

  • モデルプロバイダー - Dify Docs | https://docs.dify.ai/ja/use-dify/workspace/model-providers
  • 『生成AI』というデカい主語を整理して技術選定する方法 | https://zenn.dev/akari1106/articles/e0a611f9fac69a
  • LLMチャットUI vs OpenAI API vs フレームワーク5種の選定基準 | https://zenn.dev/epicai_techblog/articles/e4e1a27584e631

Verified Information from Public Sources for This Article

  • Selecting models by task type rather than brand is a framework supportable by public sources
  • Dify’s public structure naturally supports multi-model coexistence and task-based combination
  • This article can be retained as a public source-based selection methodology