Models • querychat

Under the hood, querychat is powered by ellmer, a library for building chat-based applications with large language models (LLMs). ellmer supports a wide range of LLM providers – see here for a full list.

library(querychat)
library(palmerpenguins)
library(ellmer)

Specify a model

To use a particular model, pass a "{provider}/{model}" string to the client parameter, which gets passed along to ellmer::chat():

qc <- querychat(penguins, client = "anthropic/claude-sonnet-4-5")
qc$app()  # Launch the app

And, if you’d like to effectively set a new default model, you can use the querychat.client R option or the QUERYCHAT_CLIENT environment variable.

# In your .Rprofile
options(querychat.client = "anthropic/claude-sonnet-4-5")

Note that it can also be useful to pass a full Chat object to the client parameter for more advanced use cases (e.g., custom parameters, tools, etc):

client <- chat_anthropic(model = "claude-sonnet-4-5")
qc <- querychat(penguins, client = client)
qc$app()  # Launch the app

Credentials

Most models require an API key or some other form of authentication. See the reference page for the relevant model provider (e.g., chat_anthropic()) to learn more on how to set up credentials.

GitHub model marketplace

If you are already setup with GitHub credentials, GitHub model marketplace provides a free and easy way to get started. See here for more details on how to get setup.

library(ellmer)

# Just works if GITHUB_TOKEN is set in your environment
client <- chat_github(model = "gpt-4.1")

In general, most providers will prefer credentials stored as environment variables. Common practice is to use an .Renviron file to manage these variables. For example, for chat_openai(), you might add to your .Renviron file:

OPENAI_API_KEY="your_api_key_here"

Then, you can edit your .Renviron file using:

usethis::edit_r_environ()

Recommended models

In theory, you could use any model that has tool calling support, but we currently recommend (as of November 2025):

GPT-4.1 (the default)
Claude 4.5 Sonnet
Google Gemini 3.0

In our testing, we’ve found that those models strike a good balance between accuracy and latency. That said, smaller/faster models like GPT-4.1-mini or Claude Haiku 4.5 work well for most tables and are worth trying first—they’re significantly cheaper and faster. You can always switch to a larger model if you find the results aren’t meeting your needs. On the other end of the spectrum, reasoning models like o3-mini tend to slow down responses without providing meaningfully better results for this task.

We’ve also seen some decent results with frontier local models (e.g., gpt-oss:20b), but even if you have the compute to run the largest models, they still tend to lag behind the cloud-hosted options in terms of accuracy and speed.

Data privacy concerns?

If you have data privacy concerns, consider that your org may provide access to private instances of these models with data residency guarantees. For example, Azure, AWS Bedrock, and Google Vertex AI all provide private instances of popular LLMs. You can interface with these enterprise providers by passing the right string (e.g., "bedrock-anthropic") or Chat object (e.g., chat_bedrock_anthropic()) to the client parameter. See the ellmer docs for more details.