Venice AI Chat Completions

x402

Venice AI Chat Completions is a paid API for AI agents from api.venice.ai, paid per call via x402, $10/call, status unknown (last checked 2026-10-11).

Run multimodal text inference (chat completions) against a specified model, supporting text, images, audio, video, and file inputs with optional web search and streaming.

Price / call$10

Seller volume →—

Success—

StatusUnknown

How this service works

OpenAI-compatible chat completions.

Output

A chat completion object containing the assistant's generated message text, finish reason, token usage breakdown (prompt, completion, total), model used, and optionally tool calls, web search citations, and reasoning content. The response mirrors the OpenAI chat completion format.

Example use cases

Example prompt

“Using the zai-org-glm-5-1 model on Venice, answer this question for me with web search enabled: 'What are the latest developments in quantum computing as of 2025?' and keep the response concise.”

Technical details

Endpoint

POSThttps://api.venice.ai/api/v1/chat/completions?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need private, unrestricted LLM inference with multimodal input support (images, audio, video, PDFs) and want OpenAI-compatible chat completions with optional web search. Prefer it over standard OpenAI when you need x402 micropayment-based access, file-to-text auto-extraction, or access to Venice-specific models. Use streaming (stream=true) for long responses.

Side effects

402 Payment Required — insufficient x402 wallet balance, includes top-up instructions
401 Unauthorized — missing or invalid Bearer API key or X-Sign-In-With-X header
400 Bad Request — missing required fields (messages or model), or invalid model ID
429 Too Many Requests — rate limit exceeded for the account
Model not found — specified model ID not available to the authenticated user
Timeout — long-running non-streaming request exceeds server timeout; use stream=true instead

Input

Schema · Request body

Field	Type	Description
n	integer	How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
seed	integer	The random seed used to generate the response. This is useful for reproducibility.
stop	—	Up to 4 sequences where the API will stop generating further tokens. Defaults to null.
text	object	OpenAI-compatible text configuration parameter.
user	string	This field is discarded on the request but is supported in the Venice API for compatibility with OpenAI clients.
min_p	number	Sets a minimum probability threshold for token selection. Tokens with probabilities below this value are filtered out.
modelrequired	string	The ID of the model you wish to prompt. May also be a model trait, or a model compatibility mapping. See the models endpoint for a list of models available to you. You can use feature suffixes to enable features from the venice_parameters object. Please see "Model Feature Suffix" documentation for more details.
store	boolean	This field is accepted for OpenAI compatibility but is not used by Venice.
tools	array	A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for.
top_k	integer	The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p	number	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.
stream	boolean	Whether to stream back partial progress. Defaults to false.
include	array	OpenAI-compatible parameter specifying additional data to include in the response.
logprobs	boolean	Whether to include log probabilities in the response. This is not supported by all models.
max_temp	number	Maximum temperature value for dynamic temperature scaling.
messagesrequired	array	A list of messages comprising the conversation so far. Depending on the model you use, different message types (modalities) are supported, like text and images. Non-multimodal models reject image content. For vision models that support multiple images (supportsMultipleImages), images are preserved across all messages in the conversation history. For single-image vision models, only the last image-containing message retains its images.
metadata	object	OpenAI-compatible metadata parameter for request tracking.
min_temp	number	Minimum temperature value for dynamic temperature scaling.
reasoning	object	Configuration for reasoning behavior on supported models.
verbosity	string	Controls the verbosity of the text response. Currently supported values are `low`, `medium`, and `high`.
max_tokens	integer	The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API. Values of 0 or less are ignored and the model will use its default maximum. This value is now deprecated in favor of max_completion_tokens.
temperature	number	What sampling temperature to use, 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. We generally recommend altering this or top_p but not both.
tool_choice	—
top_logprobs	integer	The number of highest probability tokens to return for each token position.
stop_token_ids	array	Array of token IDs where the API will stop generating further tokens.
stream_options	object
response_format	—	Format in which the response should be returned.
presence_penalty	number	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.
prompt_cache_key	string	When supplied, this field may be used to optimize conversation routing to improve cache performance and thus reduce latency.
reasoning_effort	string	OpenAI-compatible parameter to control reasoning effort level for supported models. Takes precedence over reasoning.effort if both are provided.
frequency_penalty	number	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.
venice_parameters	object	Unique parameters to Venice's API implementation. Customize these to control the behavior of the model.
repetition_penalty	number	The parameter for repetition penalty. 1.0 means no penalty. Values > 1.0 discourage repetition.
parallel_tool_calls	boolean	Whether to enable parallel function calling during tool use.
max_completion_tokens	integer	An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
prompt_cache_retention	string	OpenAI-compatible parameter to control prompt cache retention. "extended" or "24h" extends retention to 24 hours for supported models.

Output

Schema · Response

Field	Type	Description
idrequired	string	The ID of the request.
modelrequired	string	The model id used for the request.
usagerequired	object
objectrequired	string	The type of the object returned.
choices	array	A list of chat completion choices. Can be more than one if n is greater than 1. Certain models may not return this field under certain conditions.
createdrequired	integer	The time at which the request was created.
prompt_logprobs	—	Log probability information for the prompt.
venice_parameters	object	Unique parameters to Venice's API implementation.

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