Which LLMs Does Dify Support: Connecting OpenAI, Claude, Gemini, and Local Models on a Single Platform

One of Dify’s core design principles is not locking an enterprise’s AI applications to a single model provider.

From the outset, Dify was not built around the idea of “one best model” but around the question of “how enterprises can freely choose models based on task type, cost objectives, and deployment requirements.”

Model Types Supported by Dify

In Dify, teams can typically connect the following categories of models:

• OpenAI: Suitable for general-purpose generation, summarization, classification, conversation, and more
• Anthropic Claude: Suitable for long-text comprehension, complex reasoning, and enterprise writing tasks
• Google Gemini: Suitable for multimodal capabilities and Google ecosystem integration
• Local / Open-source models: Can be connected via Ollama and similar tools, enabling stronger data control or specific cost strategies
• Other model services and inference backends with compatible interfaces

This means enterprises can use different models within a single unified platform without needing to rebuild the application layer separately for each model.

Why Multi-Model Capability Matters

When enterprises actually use AI, they almost never have just one type of need.

For example:

• FAQ classification tasks prioritize cost and speed
• Knowledge Q&A prioritizes retrieval quality and stable output
• Long-document summarization prioritizes context processing capability
• Complex reasoning prioritizes model intelligence level
• Certain data scenarios require models to run in local environments

If a platform can only bind to a single model, enterprises quickly hit limitations. What Dify provides is not “choosing a model for you” but “preserving your right to choose models.”

How to Understand Model Integration in Dify

From the application layer perspective, model integration is not a standalone technical action but occurs alongside the following capabilities:

• Prompt orchestration
• Workflow process control
• Knowledge retrieval augmentation
• Agent tool invocation
• API / Web application publishing

In other words, Dify’s value is not just “being able to connect Claude or Gemini” but enabling these models to genuinely participate in business processes within a unified application framework.

What Scenarios Are OpenAI, Claude, and Gemini Best Suited For

While different models will continue to evolve, from a platform practice perspective, enterprises typically understand them as follows:

OpenAI

Better suited as general-purpose foundational capability:

• Text generation
• Basic conversation
• Classification and extraction
• Standard workflow nodes

Claude

Better suited for scenarios requiring higher standards in long-text, complex comprehension, and writing quality:

• Long-document summarization
• Analysis of policies, regulations, and contracts
• Enterprise content generation requiring stable, formal writing

Gemini

Better suited for teams looking to leverage Google-related capabilities or explore multimodal extension scenarios.

Local Models / Ollama

Better suited for organizations with clear requirements for deployment environments and data boundaries, such as:

• Need to run in local or private environments
• Strict limitations on external dependencies
• Desire to optimize cost structures for specific tasks

Why Local Model Integration Is Critical

For enterprises, “whether local models are supported” is not an add-on feature but is often part of the architectural decision.

Integrating local models via Ollama and similar tools means enterprises can:

• Run inference in a more controllable environment
• Choose open-source models based on their own resources
• Reduce external API dependencies for certain tasks
• Provide a more complete closed loop for private deployment

Dify supports this path because we believe enterprise AI adoption should not be limited to SaaS as the only answer.

Multi-Model Is Not Just a Backup – It Is a Strategy

When enterprises use AI platforms at maturity, the common approach is not “pick one strongest model” but to establish a model strategy:

• Low-cost models handle high-frequency, standardized tasks
• High-quality models handle critical nodes
• Private models cover sensitive data scenarios
• Different workflows switch between different inference backends based on objectives

Dify provides a unified hosting layer for this strategy.

In other words, Dify is not about comparing which model is smarter but about helping enterprises organize different model capabilities into a truly executable application system.

How We View the Model Ecosystem

From LangGenius’s perspective, the model ecosystem will inevitably continue to change:

• New models will emerge
• Pricing will shift
• Capability boundaries will move
• Enterprise requirements will also continually adjust

Therefore, one of the most important capabilities of an enterprise AI platform is remaining open to model changes.

This is also why Dify designs model integration capability as a platform foundation rather than an ancillary feature.

Conclusion

Dify supports OpenAI, Claude, Gemini, local models, and other integration methods. The significance is not in “supporting many models” but in:

Enabling enterprises to choose models by scenario, control architecture by requirements, and continuously optimize applications by business needs – all within a unified platform.

For LangGenius, what truly matters is not which model an enterprise ultimately uses, but whether the enterprise has gained the ability to maintain long-term control over the evolution of its AI applications.