Grok Chat Completions

llmchatx-searchweb-searchimage-generationtts

Grok Chat Completions is a paid API for AI agents from Grok at grok.mpp.paywithlocus.com, paid per call via MPP, Model-dependent (~$0.001–$0.02) (dynamic), status unknown (last checked 2026-10-10).

Sends a conversation to xAI's Grok models and returns a text (or multimodal) completion, supporting grok-3, grok-3-mini, grok-4, and code-focused variants

PriceModel-dependent (~$0.001–$0.02)varies by input

Seller volume →—

Success—

StatusUnknown

How this service works

Generate text with Grok models. OpenAI-compatible format with support for text, vision, and function calling.

Output

Returns an assistant message object containing the generated text response from the selected Grok model, reflecting the conversation context, sampling parameters, and any multimodal inputs provided.

Example use cases

Example prompt

“Ask Grok-4 to explain the tradeoffs between relational and document databases in three concise paragraphs, using a slightly creative tone with temperature 0.8 and a max of 500 tokens.”

Technical details

Endpoint

POSThttps://grok.mpp.paywithlocus.com/grok/chat?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a standard OpenAI-compatible chat completion interface backed by xAI's Grok model family (grok-3, grok-3-mini, grok-4, grok-code-fast-1). Prefer it over other Grok endpoints when you don't specifically need web search, X/Twitter search, image generation, image editing, code execution sandboxing, or TTS — those specialized capabilities have dedicated sibling endpoints. Use this for general-purpose LLM tasks: Q&A, summarization, reasoning, code help, and multi-turn conversations.

Side effects

Invalid or unsupported model ID returns a 400 or model-not-found error
Missing required 'messages' field returns a validation error
Temperature or top_p outside allowed range causes a parameter error
Exceeding max_tokens limit or context window size truncates or errors the response
Payment failure or insufficient USDC balance blocks the request
Malformed message role (not system/user/assistant) causes a schema error
Image URL unreachable or unsupported format in multimodal input causes a content error

Input

{
  "model": "grok-3-mini",
  "messages": [
    {
      "role": "user",
      "content": "Explain the concept of machine learning in simple terms."
    }
  ],
  "max_tokens": 300,
  "temperature": 0.7
}

Schema · Request body

Field	Type	Description
n	number	Number of completions to generate (default 1)
stop	—	Stop sequences
modelrequired	string	Metered x402 model ID: 'grok-4.6', 'grok-4.5', 'grok-4.5-latest', 'grok-4.3', 'grok-4.20-0309-reasoning', or 'grok-4.20-0309-non-reasoning'. Multi-agent is excluded because xAI does not support an output-token cap for it.
top_p	number	Nucleus sampling threshold (0.0–1.0)
stream	boolean	Stream partial results as SSE (text/event-stream). Recommended for reasoning models that exceed the edge buffered-response timeout.
messagesrequired	array	Conversation messages. Each: { role: "system"\|"user"\|"assistant", content: "text" } or multimodal: { role: "user", content: [{ type: "text", text: "..." }, { type: "image_url", image_url: { url: "..." } }] }
max_tokensrequired	number	Maximum output tokens. Required to bound metered x402 authorization.
temperature	number	Sampling temperature (0.0–2.0, default 1.0)
presence_penalty	number	Presence penalty (-2.0 to 2.0, default 0)
frequency_penalty	number	Frequency penalty (-2.0 to 2.0, default 0)

Output

{
  "data": {
    "id": "98bbc223-42b8-9871-8265-44e0d9433735",
    "model": "grok-4.3",
    "usage": {
      "total_tokens": 494,
      "prompt_tokens": 140,
      "num_sources_used": 0,
      "completion_tokens": 166,
      "cost_in_usd_ticks": 9256000,
      "prompt_tokens_details": {
        "text_tokens": 140,
        "audio_tokens": 0,
        "image_tokens": 0,
        "cached_tokens": 128
      },
      "completion_tokens_details": {
        "audio_tokens": 0,
        "reasoning_tokens": 188,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "object": "chat.completion",
    "choices": [
      {
        "index": 0,
        "message": {
          "role": "assistant",
          "content": "Machine learning is a way of teaching computers to learn patterns from data, instead of following step-by-step instructions written by a programmer.\nThink of it like training a dog: instead of manually commanding every move, you show the dog examples of what you want (sit, stay, fetch) and reward good behavior until it figures out the pattern on its own.\nIn practice:\n- You feed the computer lots of examples (data).\n- It spots patterns and builds its own rules.\n- Over time, it gets better at making predictions or decisions on new data it hasn't seen before.\nA common example is email spam filters. Instead of someone writing \"if the email contains these exact words, mark it spam,\" the system learns from millions of emails that users have marked as spam or not spam, then decides on its own for new messages.",
          "refusal": null,
          "reasoning_content": "The question is: \"Explain the concept of machine learning in simple terms.\""
        },
        "finish_reason": "stop"
      }
    ],
    "created": 1781755336,
    "service_tier": "default",
    "system_fingerprint": "fp_eb3c003fc66c14ed"
  },
  "success": true
}

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How this service works

Generate text with Grok models. OpenAI-compatible format with support for text, vision, and function calling.

Output

Returns an assistant message object containing the generated text response from the selected Grok model, reflecting the conversation context, sampling parameters, and any multimodal inputs provided.

Technical details

Endpoint

POSThttps://grok.mpp.paywithlocus.com/grok/chat?utm_source=zero.xyz

When to prefer

Side effects

Invalid or unsupported model ID returns a 400 or model-not-found error
Missing required 'messages' field returns a validation error
Temperature or top_p outside allowed range causes a parameter error
Exceeding max_tokens limit or context window size truncates or errors the response
Payment failure or insufficient USDC balance blocks the request
Malformed message role (not system/user/assistant) causes a schema error
Image URL unreachable or unsupported format in multimodal input causes a content error

Input

{
  "model": "grok-3-mini",
  "messages": [
    {
      "role": "user",
      "content": "Explain the concept of machine learning in simple terms."
    }
  ],
  "max_tokens": 300,
  "temperature": 0.7
}

Schema · Request body

Field	Type	Description
n	number	Number of completions to generate (default 1)
stop	—	Stop sequences
modelrequired	string	Metered x402 model ID: 'grok-4.6', 'grok-4.5', 'grok-4.5-latest', 'grok-4.3', 'grok-4.20-0309-reasoning', or 'grok-4.20-0309-non-reasoning'. Multi-agent is excluded because xAI does not support an output-token cap for it.
top_p	number	Nucleus sampling threshold (0.0–1.0)
stream	boolean	Stream partial results as SSE (text/event-stream). Recommended for reasoning models that exceed the edge buffered-response timeout.
messagesrequired	array	Conversation messages. Each: { role: "system"\|"user"\|"assistant", content: "text" } or multimodal: { role: "user", content: [{ type: "text", text: "..." }, { type: "image_url", image_url: { url: "..." } }] }
max_tokensrequired	number	Maximum output tokens. Required to bound metered x402 authorization.
temperature	number	Sampling temperature (0.0–2.0, default 1.0)
presence_penalty	number	Presence penalty (-2.0 to 2.0, default 0)
frequency_penalty	number	Frequency penalty (-2.0 to 2.0, default 0)

Output

{
  "data": {
    "id": "98bbc223-42b8-9871-8265-44e0d9433735",
    "model": "grok-4.3",
    "usage": {
      "total_tokens": 494,
      "prompt_tokens": 140,
      "num_sources_used": 0,
      "completion_tokens": 166,
      "cost_in_usd_ticks": 9256000,
      "prompt_tokens_details": {
        "text_tokens": 140,
        "audio_tokens": 0,
        "image_tokens": 0,
        "cached_tokens": 128
      },
      "completion_tokens_details": {
        "audio_tokens": 0,
        "reasoning_tokens": 188,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "object": "chat.completion",
    "choices": [
      {
        "index": 0,
        "message": {
          "role": "assistant",
          "content": "Machine learning is a way of teaching computers to learn patterns from data, instead of following step-by-step instructions written by a programmer.\nThink of it like training a dog: instead of manually commanding every move, you show the dog examples of what you want (sit, stay, fetch) and reward good behavior until it figures out the pattern on its own.\nIn practice:\n- You feed the computer lots of examples (data).\n- It spots patterns and builds its own rules.\n- Over time, it gets better at making predictions or decisions on new data it hasn't seen before.\nA common example is email spam filters. Instead of someone writing \"if the email contains these exact words, mark it spam,\" the system learns from millions of emails that users have marked as spam or not spam, then decides on its own for new messages.",
          "refusal": null,
          "reasoning_content": "The question is: \"Explain the concept of machine learning in simple terms.\""
        },
        "finish_reason": "stop"
      }
    ],
    "created": 1781755336,
    "service_tier": "default",
    "system_fingerprint": "fp_eb3c003fc66c14ed"
  },
  "success": true
}