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POST
/
chat
/
completions
JavaScript
import SambaNova from 'sambanova';

const client = new SambaNova({
  apiKey: process.env['SAMBANOVA_API_KEY'], // This is the default and can be omitted
});

const completion = await client.chat.completions.create({
  messages: [{ content: 'create a poem using palindromes', role: 'user' }],
  model: 'gpt-oss-120b',
});

console.log(completion);
{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "In madam moon's silver glow,  Aha, a palindrome to know,  Radar spins, a circular tale,  Level heads prevail, without fail.  A man, a plan, a canal, Panama!  Able was I ere I saw Elba,  A Santa at NASA, a curious sight,  Do geese see God, in the pale moonlight?  Mr. Owl ate my metal worm,  Do nine men interpret? Nine men, I nod,  Never odd or even, a palindrome's might,  Madam, in Eden, I'm Adam.  Aibohphobia, a fear to confess,  A palindrome's symmetry, I must address,  Refer, a word that reads the same,  A palindrome's beauty, in its circular game.  In the stillness of the night,  Ava, a palindrome, shining bright,  Hannah, a name that reads the same,  A palindrome's magic, in its circular flame.  Note: Please keep in mind that creating a poem using palindromes can be a challenging task,  and the resulting poem may not be as cohesive or flowing as one that doesn't rely on palindromes.  However, I hope you enjoy the attempt!",
        "role": "assistant"
      }
    }
  ],
  "created": 1737583288.6076705,
  "id": "83a7809d-e18f-44f9-9ab7-2bc494c6c661",
  "model": "gpt-oss-120b",
  "object": "chat.completion",
  "system_fingerprint": "fastcoe",
  "usage": {
    "acceptance_rate": 4.058139324188232,
    "completion_tokens": 350,
    "completion_tokens_after_first_per_sec": 248.09314856382406,
    "completion_tokens_after_first_per_sec_first_ten": 249.67922929952655,
    "completion_tokens_per_sec": 238.91966176995348,
    "end_time": 1737583289.7345645,
    "is_last_response": true,
    "prompt_tokens": 43,
    "start_time": 1737583288.264706,
    "time_to_first_token": 0.06312894821166992,
    "total_latency": 1.4649275719174653,
    "total_tokens": 393,
    "total_tokens_per_sec": 268.27264878740493
  }
}

Documentation Index

Fetch the complete documentation index at: https://sambanova-systems.mintlify.dev/docs/llms.txt

Use this file to discover all available pages before exploring further.

Authorizations

​
Authorization
string
header
required

SambaNova API Key

Body

application/json

Chat prompt and parameters

chat completions request object

​
model
required

The model ID to use (e.g. gpt-oss-120b). See available models

​
messages
(System Message · object | User Message · object | Assistant Message · object | Tool Message · object)[]
required

A list of messages comprising the conversation so far.

Minimum array length: 1
Example:
[
  {
    "role": "user",
    "content": "create a poem using palindromes"
  }
]
​
max_tokens
integer | null

The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length.

Example:

2048

​
max_completion_tokens
integer | null

The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length.

Example:

2048

​
temperature
number | null
default:0.7

What sampling temperature to use, determines the degree of randomness in the response. between 0 and 2, Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. Is recommended altering this, top_p or top_k but not more than one of these.

Required range: 0 <= x <= 2
Example:

0.7

​
top_p
number | null

Cumulative probability for token choices. An alternative to sampling with temperature, called 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. Is recommended altering this, top_k or temperature but not more than one of these.

Required range: 0 <= x <= 1
Example:

1

​
top_k
integer | null

Amount limit of token choices. An alternative to sampling with temperature, the model considers the results of the first K tokens with higher probability. So 10 means only the first 10 tokens with higher probability are considered. Is recommended altering this, top_p or temperature but not more than one of these.

Required range: 1 <= x <= 100
Example:

5

​
presence_penalty
number | null
default:0

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. Not currently implemented; accepted for API compatibility

Required range: -2 <= x <= 2
​
frequency_penalty
number
default:0

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. Not currently implemented; accepted for API compatibility

Required range: -2 <= x <= 2
​
do_sample
boolean | null

If true, sampling is enabled during output generation. If false, deterministic decoding is used.

​
stop

Sequences where the API will stop generating tokens. The returned text will not contain the stop sequence.

Example:

"\n"

​
stream
boolean | null
default:false

If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.

​
stream_options
StreamOptions · object

Options for streaming response. Only set this when setting stream as true

​
response_format
ResponseFormatJSONSchema · object

Specifies that the model should produce output conforming to a given JSON schema.

Example:
{
  "type": "json_schema",
  "json_schema": {
    "name": "User",
    "description": "JSON schema for a simple user object",
    "strict": false,
    "schema": {
      "type": "object",
      "properties": {
        "id": {
          "type": "string",
          "description": "Unique identifier for the user"
        },
        "name": {
          "type": "string",
          "description": "Full name of the user"
        }
      },
      "required": ["id", "name"]
    }
  }
}
​
reasoning_effort
enum<string> | null

Value specifying the amount of reasoning the model is allowed to do, increasing it will increase the number of output reasoning tokens generated by the model, but will improve quality of the responses. allowed values are 'low', 'medium', 'high'

Available options:
low,
medium,
high
​
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. 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.

Available options:
none,
auto,
required
​
parallel_tool_calls
boolean | null

Whether to enable parallel function calling during tool use, This is not yet supported by our models.

​
tools
Tool · object[] | null

A list of tools the model may call. Use this to provide a list of functions the model may generate JSON inputs for.

Maximum array length: 128
​
chat_template_kwargs
Chat Template Kwargs · object

A dictionary of additional keyword arguments to pass into the chat template. Use this to provide extra context or parameters that the model's chat template can process. Keys must be strings; values may be any valid JSON type.

Example:
{ "enable_thinking": true }
​
logprobs
boolean | null
default:false

Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.

​
top_logprobs
integer | null

An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.

Required range: 0 <= x <= 20
​
n
integer | null
default:1

How many completions to generate for each prompt. Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens.

Required range: 1 <= x <= 8
Example:

1

​
logit_bias
Logit Bias · object

Modify the likelihood of specified tokens appearing in the generation. Accepts a JSON object that maps tokens (specified by their token ID in the model tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. Values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

​
seed
integer | null

If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.

Response

Successful Response, Returns a ChatCompletionResponse object (non-streaming), or a stream of server-sent ChatCompletionStreamResponse object events ending with a response.completed event (when stream: true).

chat completion response returned by the model

​
choices
Chat Completion Choice · object[]
required
Minimum array length: 1
​
created
number
required

The Unix timestamp (in seconds) of when the chat completion was created.

​
id
string
required

A unique identifier for the chat completion.

​
model
string
required

The model used for the chat completion.

​
object
enum<string>
required

The object type, always chat.completion.

Available options:
chat.completion
Allowed value: "chat.completion"
​
system_fingerprint
string
required

Backend configuration that the model runs with.

​
usage
Usage · object
required

Usage metrics for the completion, embeddings,transcription or translation request

Examples:
{
  "completion_tokens": 260,
  "completion_tokens_after_first_per_sec": 422.79282728043336,
  "completion_tokens_after_first_per_sec_first_ten": 423.6108998455803,
  "completion_tokens_after_first_per_sec_graph": 423.6108998455803,
  "completion_tokens_per_sec": 314.53312043711406,
  "completion_tokens_details": { "reasoning_tokens": 55 },
  "end_time": 1776189309.02061,
  "is_last_response": true,
  "prompt_tokens": 90,
  "prompt_tokens_details": { "cached_tokens": 0 },
  "start_time": 1776189308.193988,
  "stop_reason": "stop",
  "time_to_first_token": 0.21402883529663086,
  "time_to_first_token_graph": 0.2102978229522705,
  "total_latency": 0.8266220092773438,
  "total_tokens": 350,
  "total_tokens_per_sec": 423.40996981919204
}
{ "prompt_tokens": 43, "total_tokens": 393 }