chat_anthropic: Chat with an Anthropic Claude model

In ellmer: Chat with Large Language Models

Chat with an Anthropic Claude model

Description

Anthropic provides a number of chat based models under the Claude moniker. Note that a Claude Pro membership does not give you the ability to call models via the API; instead, you will need to sign up (and pay for) a developer account.

Usage

chat_anthropic(
  system_prompt = NULL,
  params = NULL,
  model = NULL,
  cache = c("5m", "1h", "none"),
  api_args = list(),
  base_url = "https://api.anthropic.com/v1",
  beta_headers = character(),
  api_key = NULL,
  credentials = NULL,
  api_headers = character(),
  echo = NULL
)

chat_claude(
  system_prompt = NULL,
  params = NULL,
  model = NULL,
  cache = c("5m", "1h", "none"),
  api_args = list(),
  base_url = "https://api.anthropic.com/v1",
  beta_headers = character(),
  api_key = NULL,
  credentials = NULL,
  api_headers = character(),
  echo = NULL
)

models_claude(
  base_url = "https://api.anthropic.com/v1",
  api_key = NULL,
  credentials = NULL
)

models_anthropic(
  base_url = "https://api.anthropic.com/v1",
  api_key = NULL,
  credentials = NULL
)

Arguments

system_prompt	A system prompt to set the behavior of the assistant.
params	Common model parameters, usually created by params().
model	The model to use for the chat (defaults to "claude-sonnet-4-6"). We regularly update the default, so we strongly recommend explicitly specifying a model for anything other than casual use. Use models_anthropic() to see all options.
cache	How long to cache inputs? Defaults to "5m" (five minutes). Set to "none" to disable caching or "1h" to cache for one hour. See details below.
api_args	Named list of arbitrary extra arguments appended to the body of every chat API call. Combined with the body object generated by ellmer with modifyList().
base_url	The base URL to the endpoint; the default is Claude's public API.
beta_headers	Optionally, a character vector of beta headers to opt-in claude features that are still in beta.
api_key	Use credentials instead.
credentials	Override the default credentials. You generally should not need this argument; instead set the ANTHROPIC_API_KEY environment variable. The best place to set this is in .Renviron, which you can easily edit by calling usethis::edit_r_environ(). If you do need additional control, this argument takes a zero-argument function that returns either a string (the API key), or a named list (added as additional headers to every request).
api_headers	Named character vector of arbitrary extra headers appended to every chat API call.
echo	One of the following options: none: don't emit any output (default when running in a function). output: echo text and tool-calling output as it streams in (default when running at the console). all: echo all input and output. Note this only affects the chat() method.

Value

A Chat object.

Caching

Caching with Claude is a bit more complicated than other providers but we believe that on average it will save you both money and time, so we have enabled it by default. With other providers, like OpenAI and Google, you only pay for cache reads, which cost 10% of the normal price. With Claude, you also pay for cache writes, which cost 125% of the normal price for 5 minute caching and 200% of the normal price for 1 hour caching.

How does this affect the total cost of a conversation? Imagine the first turn sends 1000 input tokens and receives 200 output tokens. The second turn must first send both the input and output from the previous turn (1200 tokens). It then sends a further 1000 tokens and receives 200 tokens back.

To compare the prices of these two approaches we can ignore the cost of output tokens, because they are the same for both. How much will the input tokens cost? If we don't use caching, we send 1000 tokens in the first turn and 2200 (1000 + 200 + 1000) tokens in the second turn for a total of 3200 tokens. If we use caching, we'll send (the equivalent of) 1000 * 1.25 = 1250 tokens in the first turn. In the second turn, 1000 of the input tokens will be cached so the total cost is 1000 * 0.1 + (200 + 1000) * 1.25 = 1600 tokens. That makes a total of 2850 tokens, i.e. 11% fewer tokens, decreasing the overall cost.

Obviously, the details will vary from conversation to conversation, but if you have a large system prompt that you re-use many times you should expect to see larger savings. You can see exactly how many input and cache input tokens each turn uses, along with the total cost, with chat$get_tokens(). If you don't see savings for your use case, you can suppress caching with cache = "none".

I know this is already quite complicated, but there's one final wrinkle: Claude will only cache longer prompts, with caching requiring at least 1024-4096 tokens, depending on the model. So don't be surprised it if you don't see any differences with caching if you have a short prompt.

See all the details at https://docs.claude.com/en/docs/build-with-claude/prompt-caching.

See Also

Other chatbots: chat_aws_bedrock(), chat_azure_openai(), chat_cloudflare(), chat_databricks(), chat_deepseek(), chat_github(), chat_google_gemini(), chat_groq(), chat_huggingface(), chat_lmstudio(), chat_mistral(), chat_ollama(), chat_openai(), chat_openai_compatible(), chat_openrouter(), chat_perplexity(), chat_portkey(), chat_posit()

Examples

chat <- chat_anthropic()
chat$chat("Tell me three jokes about statisticians")

ellmer documentation built on July 14, 2026, 1:07 a.m.