The frequency_penalty penalizes the repetition of words based on their frequency in the generated text. A higher frequency penalty discourages the model from repeating words that have already appeared frequently in the output, promoting diversity and reducing repetition.
Chat Endpoints
Chat Completion API.
Examples
Real world code examples
Chat Completion
POST /v1/chat/completions
Chat Completion
frequency_penalty
guardrails
max_tokens
The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.
The prompt(s) to generate completions for, encoded as a list of dict with role and content.
metadata
model
ID of the model to use. You can use the List Available Models API to see all of your available models, or see our Model overview for model descriptions.
n
Number of completions to return for each request, input tokens are only billed once.
parallel_tool_calls
Default Value: true
Whether to enable parallel function calling during tool use, when enabled the model can call multiple tools in parallel.
prediction
Enable users to specify an expected completion, optimizing response times by leveraging known or predictable content.
presence_penalty
The presence_penalty determines how much the model penalizes the repetition of words or phrases. A higher presence penalty encourages the model to use a wider variety of words and phrases, making the output more diverse and creative.
prompt_cache_key
A cache key for prompt caching. Use the same key for requests with shared prompt prefixes, such as multi-turn conversations or repeated system prompts, to increase cache hits. Cached tokens are billed at 10% of the standard input token price.
prompt_mode
Available options to the prompt_mode argument on the chat completion endpoint. Values represent high-level intent. Assignment to actual SPs is handled internally. System prompt may include knowledge cutoff date, model capabilities, tone to use, safety guidelines, etc.
random_seed
The seed to use for random sampling. If set, different calls will generate deterministic results.
reasoning_effort
response_format
Specify the format that the model must output. By default it will use \{ "type": "text" \}. Setting to \{ "type": "json_object" \} enables JSON mode, which guarantees the message the model generates is in JSON. When using JSON mode you MUST also instruct the model to produce JSON yourself with a system or a user message. Setting to \{ "type": "json_schema" \} enables JSON schema mode, which guarantees the message the model generates is in JSON and follows the schema you provide.
safe_prompt
Default Value: false
Whether to inject a safety prompt before all conversations.
stop
Stop generation if this token is detected. Or if one of these tokens is detected when providing an array
stream
Default Value: false
Whether to stream back partial progress. If set, tokens will be sent as data-only server-side events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON.
temperature
What sampling temperature to use, we recommend between 0.0 and 0.7. Higher values like 0.7 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both. The default value varies depending on the model you are targeting. Call the /models endpoint to retrieve the appropriate value.
tool_choice
Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. any or required means the model must call one or more tools. Specifying a particular tool via \{"type": "function", "function": \{"name": "my_function"\}\} forces the model to call that tool.
A list of tools the model may call. Use this to provide a list of functions the model may generate JSON inputs for.
top_p
Nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
200 (application/json)
200 (text/event-stream)
Successful Response
Playground
Test the endpoints live
import { Mistral } from "@mistralai/mistralai";
const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });
const response = await client.chat.complete({
model: "mistral-large-latest",
messages: [
{
role: "user",
content: "Who is the best French painter? Answer in one short sentence.",
},
],
});
console.log(response.choices[0].message.content);
import { Mistral } from "@mistralai/mistralai";
const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });
const response = await client.chat.complete({
model: "mistral-large-latest",
messages: [
{
role: "user",
content: "Who is the best French painter? Answer in one short sentence.",
},
],
});
console.log(response.choices[0].message.content);
import os
from mistralai.client import Mistral
client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
response = client.chat.complete(
model="mistral-large-latest",
messages=[
{
"role": "user",
"content": "Who is the best French painter? Answer in one short sentence.",
}
],
)
print(response.choices[0].message.content)
import os
from mistralai.client import Mistral
client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
response = client.chat.complete(
model="mistral-large-latest",
messages=[
{
"role": "user",
"content": "Who is the best French painter? Answer in one short sentence.",
}
],
)
print(response.choices[0].message.content)
curl https://api.mistral.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $MISTRAL_API_KEY" \
-d '{
"model": "mistral-large-latest",
"messages": [
{
"role": "user",
"content": "Who is the best French painter? Answer in one short sentence."
}
]
}'
curl https://api.mistral.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $MISTRAL_API_KEY" \
-d '{
"model": "mistral-large-latest",
"messages": [
{
"role": "user",
"content": "Who is the best French painter? Answer in one short sentence."
}
]
}'
200 (application/json)
200 (text/event-stream)
{
"choices": [
{
"finish_reason": "stop",
"index": "<to fill>"
}
],
"created": "1702256327",
"id": "cmpl-e5cc70bb28c444948073e77776eb30ef",
"model": "mistral-small-latest",
"object": "chat.completion",
"usage": {}
}{
"choices": [
{
"finish_reason": "stop",
"index": "<to fill>"
}
],
"created": "1702256327",
"id": "cmpl-e5cc70bb28c444948073e77776eb30ef",
"model": "mistral-small-latest",
"object": "chat.completion",
"usage": {}
}nullnull