This section is for users who want to connect OpenHands to different LLMs.

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Model Recommendations

Based on our evaluations of language models for coding tasks (using the SWE-bench dataset), we can provide some recommendations for model selection. Our latest benchmarking results can be found in this spreadsheet. Based on these findings and community feedback, these are the latest models that have been verified to work reasonably well with OpenHands:

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Cloud / API-Based Models

If you have successfully run OpenHands with specific providers, we encourage you to open a PR to share your setup process to help others using the same provider! For a full list of the providers and models available, please consult the litellm documentation.
OpenHands will issue many prompts to the LLM you configure. Most of these LLMs cost money, so be sure to set spending limits and monitor usage.

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Local / Self-Hosted Models

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Known Issues

As of July 2025, there are known issues with Gemini 2.5 Pro conversations taking longer than normal with OpenHands. We are continuing to investigate.
Most current local and open source models are not as powerful. When using such models, you may see long wait times between messages, poor responses, or errors about malformed JSON. OpenHands can only be as powerful as the models driving it. However, if you do find ones that work, please add them to the verified list above.

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LLM Configuration

The following can be set in the OpenHands UI through the Settings:
  • LLM Provider
  • LLM Model
  • API Key
  • Base URL (through Advanced settings)
There are some settings that may be necessary for some LLMs/providers that cannot be set through the UI. Instead, these can be set through environment variables passed to the docker run command when starting the app using -e:
  • LLM_API_VERSION
  • LLM_EMBEDDING_MODEL
  • LLM_EMBEDDING_DEPLOYMENT_NAME
  • LLM_DROP_PARAMS
  • LLM_DISABLE_VISION
  • LLM_CACHING_PROMPT
We have a few guides for running OpenHands with specific model providers:

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Model Customization

LLM providers have specific settings that can be customized to optimize their performance with OpenHands, such as:
  • Custom Tokenizers: For specialized models, you can add a suitable tokenizer.
  • Native Tool Calling: Toggle native function/tool calling capabilities.
For detailed information about model customization, see LLM Configuration Options.

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API retries and rate limits

LLM providers typically have rate limits, sometimes very low, and may require retries. OpenHands will automatically retry requests if it receives a Rate Limit Error (429 error code). You can customize these options as you need for the provider you’re using. Check their documentation, and set the following environment variables to control the number of retries and the time between retries:
  • LLM_NUM_RETRIES (Default of 4 times)
  • LLM_RETRY_MIN_WAIT (Default of 5 seconds)
  • LLM_RETRY_MAX_WAIT (Default of 30 seconds)
  • LLM_RETRY_MULTIPLIER (Default of 2)
If you are running OpenHands in development mode, you can also set these options in the config.toml file: 
[llm]
num_retries = 4
retry_min_wait = 5
retry_max_wait = 30
retry_multiplier = 2