{ "openapi": "3.1.0", "info": { "title": "Responses API", "version": "1.0.0" }, "servers": [ { "url": "https://api.cometapi.com" } ], "security": [ { "bearerAuth": [] } ], "paths": { "/v1/responses": { "post": { "summary": "Create Response", "operationId": "createResponse", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "required": [ "model", "input" ], "properties": { "model": { "type": "string", "description": "Model ID to use for this request. See the [Models page](/overview/models) for current options.", "example": "gpt-5.6-sol" }, "input": { "oneOf": [ { "type": "string", "description": "A plain text string as input." }, { "type": "array", "description": "An array of input items with roles and multimodal content.", "items": { "type": "object" } } ], "description": "Text, image, or file inputs to the model, used to generate a response. Can be a simple string for text-only input, or an array of input items for multimodal content (images, files) and multi-turn conversations." }, "instructions": { "type": "string", "description": "A system (or developer) message inserted into the model's context. When used with `previous_response_id`, instructions from the previous response are not carried over — this makes it easy to swap system messages between turns." }, "background": { "type": "boolean", "description": "Whether to run the model response in the background. Background responses do not return output directly — you retrieve the result later via the response ID.", "default": false }, "context_management": { "type": "array", "description": "Context management configuration for this request. Controls how the model manages context when the conversation exceeds the context window.", "items": { "type": "object", "properties": { "type": { "type": "string", "description": "The type of context management." }, "compact_threshold": { "type": "number", "description": "The threshold at which context compaction is triggered." } } } }, "conversation": { "type": [ "string", "object", "null" ], "description": "The conversation this response belongs to. Items from the conversation are prepended to `input` for context. Input and output items are automatically added to the conversation after the response completes. Cannot be used with `previous_response_id`.", "default": null }, "include": { "type": "array", "description": "Additional output data to include in the response. Use this to request extra information that is not included by default.", "items": { "type": "string", "enum": [ "web_search_call.action.sources", "code_interpreter_call.outputs", "computer_call_output.output.image_url", "file_search_call.results", "message.input_image.image_url", "message.output_text.logprobs", "reasoning.encrypted_content" ] } }, "max_output_tokens": { "type": "integer", "description": "An upper bound for the number of tokens that can be generated for a response, including visible output tokens and reasoning tokens." }, "max_tool_calls": { "type": "integer", "description": "The maximum number of total calls to built-in tools that can be processed in a response. This limit applies across all built-in tool calls, not per individual tool. Any further tool call attempts by the model will be ignored." }, "metadata": { "type": "object", "description": "Set of up to 16 key-value pairs that can be attached to the response. Useful for storing additional information in a structured format. Keys have a maximum length of 64 characters; values have a maximum length of 512 characters.", "additionalProperties": { "type": "string" } }, "parallel_tool_calls": { "type": "boolean", "description": "Whether to allow the model to run tool calls in parallel.", "default": true }, "previous_response_id": { "type": "string", "description": "The unique ID of a previous response. Use this to create multi-turn conversations without manually managing conversation state. Cannot be used with `conversation`." }, "prompt": { "type": "object", "description": "Reference to a prompt template and its variables.", "properties": { "id": { "type": "string", "description": "The ID of the prompt template." }, "variables": { "type": "object", "description": "Key-value pairs for template variables.", "additionalProperties": { "type": "string" } }, "version": { "type": "string", "description": "The version of the prompt template to use." } } }, "prompt_cache_key": { "type": "string", "description": "A key used to cache responses for similar requests, helping optimize cache hit rates. Replaces the deprecated `user` field for caching purposes." }, "prompt_cache_retention": { "type": "string", "description": "The retention policy for the prompt cache. Set to `24h` to keep cached prefixes active for up to 24 hours.", "enum": [ "in-memory", "24h" ] }, "reasoning": { "type": "object", "description": "Configuration options for reasoning models (o-series and gpt-5). Controls the depth of reasoning before generating a response.", "properties": { "effort": { "type": "string", "description": "Constrains effort on reasoning. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning.", "enum": [ "none", "minimal", "low", "medium", "high" ] }, "generate_summary": { "type": "string", "description": "Whether to generate a summary of the reasoning process.", "enum": [ "auto", "concise", "detailed" ] }, "summary": { "type": "string", "description": "Deprecated. Use `generate_summary` instead.", "enum": [ "auto", "concise", "detailed" ], "deprecated": true } } }, "safety_identifier": { "type": "string", "description": "A stable identifier for your end-users, used to help detect policy violations. Should be a hashed username or email — do not send identifying information directly.", "maxLength": 64 }, "service_tier": { "type": "string", "description": "Specifies the processing tier for the request. When set, the response will include the actual `service_tier` used.\n\n- `auto`: Uses the tier configured in project settings (default behavior).\n- `default`: Standard pricing and performance.\n- `flex`: Flexible processing with potential cost savings.\n- `priority`: Priority processing with faster response times.", "enum": [ "auto", "default", "flex", "priority" ] }, "store": { "type": "boolean", "description": "Whether to store the generated response for later retrieval via API.", "default": true }, "stream": { "type": "boolean", "description": "If set to `true`, the response data will be streamed to the client as it is generated using server-sent events (SSE). Events include `response.created`, `response.output_text.delta`, `response.completed`, and more.", "default": false }, "stream_options": { "type": "object", "description": "Options for streaming responses. Only set this when `stream` is `true`.", "properties": { "include_obfuscation": { "type": "boolean", "description": "Whether to include obfuscation data in streaming events." } } }, "temperature": { "type": "number", "description": "Sampling temperature between 0 and 2. Higher values (e.g., 0.8) increase randomness; lower values (e.g., 0.2) make output more focused and deterministic. We recommend adjusting either this or `top_p`, but not both.", "default": 1, "minimum": 0, "maximum": 2 }, "text": { "type": "object", "description": "Configuration for text output. Use this to request structured JSON output via JSON mode or JSON Schema.", "properties": { "format": { "type": "object", "description": "The format of the text output.", "properties": { "type": { "type": "string", "description": "The output format type. `text` returns plain text, `json_object` returns valid JSON, `json_schema` returns JSON conforming to a provided schema.", "enum": [ "text", "json_object", "json_schema" ] }, "json_schema": { "type": "object", "description": "The JSON Schema to use when `type` is `json_schema`. Required when using structured outputs." } } }, "verbosity": { "type": "string", "description": "Controls the verbosity of the text output.", "enum": [ "low", "medium", "high" ] } } }, "tool_choice": { "type": [ "string", "object" ], "description": "Controls how the model selects which tool(s) to call.\n\n- `auto` (default): The model decides whether and which tools to call.\n- `none`: The model will not call any tools.\n- `required`: The model must call at least one tool.\n- An object specifying a particular tool to use.", "default": "auto" }, "tools": { "type": "array", "description": "An array of tools the model may call while generating a response. CometAPI supports three categories:\n\n- **Built-in tools**: Platform-provided tools like `web_search_preview` and `file_search`.\n- **Function calls**: Custom functions you define, enabling the model to call your own code with structured arguments.\n- **MCP tools**: Integrations with third-party systems via MCP servers.", "items": { "type": "object" } }, "top_logprobs": { "type": "integer", "description": "Number of most likely tokens to return at each position (0–20), each with an associated log probability. Must include `message.output_text.logprobs` in the `include` parameter to receive logprobs.", "minimum": 0, "maximum": 20 }, "top_p": { "type": "number", "description": "Nucleus sampling parameter. The model considers tokens with `top_p` cumulative probability mass. For example, 0.1 means only the top 10% probability tokens are considered. We recommend adjusting either this or `temperature`, but not both.", "default": 1, "minimum": 0, "maximum": 1 }, "truncation": { "type": "string", "description": "The truncation strategy for handling inputs that exceed the model's context window.\n\n- `auto`: The model truncates the input by dropping items from the beginning of the conversation to fit.\n- `disabled` (default): The request fails with a 400 error if the input exceeds the context window.", "enum": [ "auto", "disabled" ], "default": "disabled" }, "user": { "type": "string", "description": "Deprecated. Use `safety_identifier` and `prompt_cache_key` instead. A stable identifier for your end-user.", "deprecated": true } } }, "examples": { "Text Input": { "summary": "Text Input", "value": { "model": "gpt-5.6-sol", "input": "Tell me a three sentence bedtime story about a unicorn." } }, "Image Input": { "summary": "Image Input", "value": { "model": "gpt-5.6-sol", "input": [ { "role": "user", "content": [ { "type": "input_text", "text": "What is in this image?" }, { "type": "input_image", "image_url": "https://images.unsplash.com/photo-1500530855697-b586d89ba3ee?w=1200" } ] } ] } }, "File Input": { "summary": "File Input", "value": { "model": "gpt-5.6-sol", "input": [ { "role": "user", "content": [ { "type": "input_text", "text": "What is in this file?" }, { "type": "input_file", "file_url": "https://www.berkshirehathaway.com/letters/2024ltr.pdf" } ] } ] } }, "Function Calling": { "summary": "Function Calling", "value": { "model": "gpt-5.6-sol", "input": "What is the weather like in Boston today?", "tools": [ { "type": "function", "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": [ "celsius", "fahrenheit" ], "description": "Temperature unit." } }, "required": [ "location", "unit" ] } } ] } }, "Reasoning": { "summary": "Reasoning", "value": { "model": "o4-mini", "input": "Solve step by step: What is 15% of 240?", "reasoning": { "effort": "high" } } } } } } }, "responses": { "200": { "description": "The generated Response object.", "content": { "application/json": { "schema": { "type": "object", "properties": { "id": { "type": "string", "description": "Unique identifier for the response.", "example": "resp_0a153ae8201f73bc0069a7e8044cc481" }, "object": { "type": "string", "description": "The object type, always `response`.", "enum": [ "response" ], "example": "response" }, "created_at": { "type": "integer", "description": "Unix timestamp (in seconds) of when the response was created.", "example": 1772611588 }, "status": { "type": "string", "description": "The status of the response.", "enum": [ "completed", "in_progress", "failed", "cancelled", "queued" ], "example": "completed" }, "background": { "type": "boolean", "description": "Whether the response was run in the background.", "example": false }, "completed_at": { "type": [ "integer", "null" ], "description": "Unix timestamp of when the response was completed, or `null` if still in progress.", "example": 1772611589 }, "error": { "type": [ "object", "null" ], "description": "Error information if the response failed, or `null` on success.", "properties": { "code": { "type": "string", "description": "The error code." }, "message": { "type": "string", "description": "A human-readable error message." } } }, "incomplete_details": { "type": [ "object", "null" ], "description": "Details about why the response is incomplete, if applicable.", "properties": { "reason": { "type": "string", "description": "The reason the response is incomplete.", "enum": [ "max_output_tokens", "content_filter" ] } } }, "instructions": { "type": [ "string", "null" ], "description": "The system instructions used for this response." }, "max_output_tokens": { "type": [ "integer", "null" ], "description": "The maximum output token limit that was applied." }, "model": { "type": "string", "description": "The model used for the response.", "example": "gpt-4.1-nano" }, "output": { "type": "array", "description": "An array of output items generated by the model. Each item can be a message, function call, or other output type.", "items": { "type": "object", "properties": { "id": { "type": "string", "description": "Unique identifier for the output item." }, "type": { "type": "string", "description": "The type of output item.", "enum": [ "message", "function_call", "web_search_call", "file_search_call", "code_interpreter_call", "computer_call", "reasoning" ] }, "status": { "type": "string", "description": "The status of this output item.", "enum": [ "completed", "in_progress" ] }, "role": { "type": "string", "description": "The role of the message (present when `type` is `message`).", "enum": [ "assistant" ] }, "content": { "type": "array", "description": "The content parts of the message (present when `type` is `message`).", "items": { "type": "object", "properties": { "type": { "type": "string", "description": "The content type.", "enum": [ "output_text" ] }, "text": { "type": "string", "description": "The generated text content." }, "annotations": { "type": "array", "description": "Annotations such as file citations or URL citations.", "items": { "type": "object" } }, "logprobs": { "type": "array", "description": "Log probability information (when requested via `include`).", "items": { "type": "object" } } } } }, "name": { "type": "string", "description": "The name of the function being called (present when `type` is `function_call`)." }, "arguments": { "type": "string", "description": "The JSON-encoded arguments for the function call (present when `type` is `function_call`)." }, "call_id": { "type": "string", "description": "The unique call identifier (present when `type` is `function_call`)." } } } }, "output_text": { "type": "string", "description": "A convenience field containing the concatenated text output from all output message items." }, "parallel_tool_calls": { "type": "boolean", "description": "Whether parallel tool calls were enabled." }, "previous_response_id": { "type": [ "string", "null" ], "description": "The ID of the previous response, if this is a multi-turn conversation." }, "reasoning": { "type": "object", "description": "The reasoning configuration that was used.", "properties": { "effort": { "type": [ "string", "null" ], "description": "The reasoning effort level." }, "summary": { "type": [ "string", "null" ], "description": "The reasoning summary setting." } } }, "service_tier": { "type": "string", "description": "The service tier actually used to process the request.", "example": "default" }, "store": { "type": "boolean", "description": "Whether the response was stored." }, "temperature": { "type": "number", "description": "The temperature value used.", "example": 1 }, "text": { "type": "object", "description": "The text configuration used.", "properties": { "format": { "type": "object", "properties": { "type": { "type": "string", "description": "Format type: `text` (default), `json_object`, or `json_schema`." } }, "description": "Output text format configuration." }, "verbosity": { "type": "string", "description": "The verbosity level used." } } }, "tool_choice": { "type": [ "string", "object" ], "description": "The tool choice setting used." }, "tools": { "type": "array", "description": "The tools that were available for this response.", "items": { "type": "object" } }, "top_p": { "type": "number", "description": "The `top_p` value used.", "example": 1 }, "truncation": { "type": "string", "description": "The truncation strategy used." }, "usage": { "type": "object", "description": "Token usage statistics for this response.", "properties": { "input_tokens": { "type": "integer", "description": "Number of input tokens consumed.", "example": 19 }, "input_tokens_details": { "type": "object", "description": "Breakdown of input token usage.", "properties": { "cached_tokens": { "type": "integer", "description": "Number of input tokens that were cached.", "example": 0 } } }, "output_tokens": { "type": "integer", "description": "Number of output tokens generated.", "example": 9 }, "output_tokens_details": { "type": "object", "description": "Breakdown of output token usage.", "properties": { "reasoning_tokens": { "type": "integer", "description": "Number of tokens used for reasoning.", "example": 0 } } }, "total_tokens": { "type": "integer", "description": "Total number of tokens (input + output).", "example": 28 } } }, "user": { "type": [ "string", "null" ], "description": "The user identifier, if provided." }, "metadata": { "type": "object", "description": "The metadata attached to this response.", "additionalProperties": { "type": "string" } }, "content_filters": { "type": [ "array", "null" ], "description": "Content filter results applied to the response, if any.", "nullable": true }, "frequency_penalty": { "type": "number", "description": "The frequency penalty applied to the request.", "default": 0 }, "max_tool_calls": { "type": [ "integer", "null" ], "description": "Maximum number of tool calls allowed, if set.", "nullable": true }, "presence_penalty": { "type": "number", "description": "The presence penalty applied to the request.", "default": 0 }, "prompt_cache_key": { "type": [ "string", "null" ], "description": "Cache key for prompt caching, if applicable.", "nullable": true }, "prompt_cache_retention": { "type": [ "string", "null" ], "description": "Prompt cache retention policy, if applicable.", "nullable": true }, "safety_identifier": { "type": [ "string", "null" ], "description": "Safety system identifier for the response, if applicable.", "nullable": true }, "top_logprobs": { "type": "integer", "description": "Number of top log probabilities returned per token position.", "default": 0 } } }, "example": { "id": "resp_0a153ae8201f73bc0069a7e8044cc481", "object": "response", "created_at": 1772611588, "status": "completed", "background": false, "completed_at": 1772611589, "error": null, "incomplete_details": null, "instructions": null, "max_output_tokens": null, "model": "gpt-4.1-nano", "output": [ { "id": "msg_0a153ae8201f73bc0069a7e8049a8881", "type": "message", "status": "completed", "content": [ { "type": "output_text", "annotations": [], "text": "Four." } ], "role": "assistant" } ], "parallel_tool_calls": true, "previous_response_id": null, "prompt_cache_key": null, "prompt_cache_retention": null, "reasoning": { "effort": null, "summary": null }, "safety_identifier": null, "service_tier": "auto", "store": true, "temperature": 1, "text": { "format": { "type": "text" }, "verbosity": "medium" }, "tool_choice": "auto", "tools": [], "top_p": 1, "truncation": "disabled", "usage": { "input_tokens": 19, "input_tokens_details": { "cached_tokens": 0 }, "output_tokens": 9, "output_tokens_details": { "reasoning_tokens": 0 }, "total_tokens": 28 }, "user": null, "metadata": {} } } } } }, "x-codeSamples": [ { "lang": "Python", "label": "Text Input", "source": "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.cometapi.com/v1\",\n api_key=os.environ[\"COMETAPI_KEY\"],\n)\n\nresponse = client.responses.create(\n model=\"gpt-5.6-sol\",\n input=\"Tell me a three sentence bedtime story about a unicorn.\",\n)\n\nprint(response.output_text)\n" }, { "lang": "Python", "label": "Image Input", "source": "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.cometapi.com/v1\",\n api_key=os.environ[\"COMETAPI_KEY\"],\n)\n\nresponse = client.responses.create(\n model=\"gpt-5.6-sol\",\n input=[\n {\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"input_text\", \"text\": \"What is in this image?\"},\n {\n \"type\": \"input_image\",\n \"image_url\": \"https://images.unsplash.com/photo-1500530855697-b586d89ba3ee?w=1200\",\n },\n ],\n }\n ],\n)\n\nprint(response.output_text)\n" }, { "lang": "Python", "label": "File Input", "source": "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.cometapi.com/v1\",\n api_key=os.environ[\"COMETAPI_KEY\"],\n)\n\nresponse = client.responses.create(\n model=\"gpt-5.6-sol\",\n input=[\n {\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"input_text\", \"text\": \"What is in this file?\"},\n {\n \"type\": \"input_file\",\n \"file_url\": \"https://www.berkshirehathaway.com/letters/2024ltr.pdf\",\n },\n ],\n }\n ],\n)\n\nprint(response.output_text)\n" }, { "lang": "Python", "label": "Web Search", "source": "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.cometapi.com/v1\",\n api_key=os.environ[\"COMETAPI_KEY\"],\n)\n\nresponse = client.responses.create(\n model=\"gpt-5.6-sol\",\n tools=[{\"type\": \"web_search_preview\"}],\n input=\"What was a positive news story from today?\",\n)\n\nprint(response.output_text)\n" }, { "lang": "Python", "label": "Streaming", "source": "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.cometapi.com/v1\",\n api_key=os.environ[\"COMETAPI_KEY\"],\n)\n\nstream = client.responses.create(\n model=\"gpt-5.6-sol\",\n input=\"Tell me a three sentence bedtime story about a unicorn.\",\n stream=True,\n)\n\nfor event in stream:\n print(event)\n" }, { "lang": "Python", "label": "Functions", "source": "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.cometapi.com/v1\",\n api_key=os.environ[\"COMETAPI_KEY\"],\n)\n\nresponse = client.responses.create(\n model=\"gpt-5.6-sol\",\n input=\"What is the weather like in Boston today?\",\n tools=[\n {\n \"type\": \"function\",\n \"name\": \"get_current_weather\",\n \"description\": \"Get the current weather in a given location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"The city and state, e.g. San Francisco, CA\",\n },\n \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n },\n \"required\": [\"location\", \"unit\"],\n },\n }\n ],\n)\n\nprint(response.output)\n" }, { "lang": "Python", "label": "Reasoning", "source": "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"https://api.cometapi.com/v1\",\n api_key=os.environ[\"COMETAPI_KEY\"],\n)\n\nresponse = client.responses.create(\n model=\"o4-mini\",\n input=\"How much wood would a woodchuck chuck?\",\n reasoning={\"effort\": \"high\"},\n)\n\nprint(response.output_text)\n" }, { "lang": "JavaScript", "label": "Text Input", "source": "import OpenAI from \"openai\";\n\nconst client = new OpenAI({\n baseURL: \"https://api.cometapi.com/v1\",\n apiKey: process.env.COMETAPI_KEY,\n});\n\nconst response = await client.responses.create({\n model: \"gpt-5.6-sol\",\n input: \"Tell me a three sentence bedtime story about a unicorn.\",\n});\n\nconsole.log(response.output_text);\n" }, { "lang": "JavaScript", "label": "Image Input", "source": "import OpenAI from \"openai\";\n\nconst client = new OpenAI({\n baseURL: \"https://api.cometapi.com/v1\",\n apiKey: process.env.COMETAPI_KEY,\n});\n\nconst response = await client.responses.create({\n model: \"gpt-5.6-sol\",\n input: [\n {\n role: \"user\",\n content: [\n { type: \"input_text\", text: \"What is in this image?\" },\n {\n type: \"input_image\",\n image_url: \"https://images.unsplash.com/photo-1500530855697-b586d89ba3ee?w=1200\",\n },\n ],\n },\n ],\n});\n\nconsole.log(response.output_text);\n" }, { "lang": "JavaScript", "label": "File Input", "source": "import OpenAI from \"openai\";\n\nconst client = new OpenAI({\n baseURL: \"https://api.cometapi.com/v1\",\n apiKey: process.env.COMETAPI_KEY,\n});\n\nconst response = await client.responses.create({\n model: \"gpt-5.6-sol\",\n input: [\n {\n role: \"user\",\n content: [\n { type: \"input_text\", text: \"What is in this file?\" },\n {\n type: \"input_file\",\n file_url: \"https://www.berkshirehathaway.com/letters/2024ltr.pdf\",\n },\n ],\n },\n ],\n});\n\nconsole.log(response.output_text);\n" }, { "lang": "JavaScript", "label": "Web Search", "source": "import OpenAI from \"openai\";\n\nconst client = new OpenAI({\n baseURL: \"https://api.cometapi.com/v1\",\n apiKey: process.env.COMETAPI_KEY,\n});\n\nconst response = await client.responses.create({\n model: \"gpt-5.6-sol\",\n tools: [{ type: \"web_search_preview\" }],\n input: \"What was a positive news story from today?\",\n});\n\nconsole.log(response.output_text);\n" }, { "lang": "JavaScript", "label": "Streaming", "source": "import OpenAI from \"openai\";\n\nconst client = new OpenAI({\n baseURL: \"https://api.cometapi.com/v1\",\n apiKey: process.env.COMETAPI_KEY,\n});\n\nconst stream = await client.responses.create({\n model: \"gpt-5.6-sol\",\n input: \"Tell me a three sentence bedtime story about a unicorn.\",\n stream: true,\n});\n\nfor await (const event of stream) {\n if (event.type === \"response.output_text.delta\") {\n process.stdout.write(event.delta);\n }\n}\n" }, { "lang": "JavaScript", "label": "Functions", "source": "import OpenAI from \"openai\";\n\nconst client = new OpenAI({\n baseURL: \"https://api.cometapi.com/v1\",\n apiKey: process.env.COMETAPI_KEY,\n});\n\nconst response = await client.responses.create({\n model: \"gpt-5.6-sol\",\n input: \"What is the weather like in Boston today?\",\n tools: [\n {\n type: \"function\",\n name: \"get_current_weather\",\n description: \"Get the current weather in a given location\",\n parameters: {\n type: \"object\",\n properties: {\n location: { type: \"string\", description: \"The city and state\" },\n unit: { type: \"string\", enum: [\"celsius\", \"fahrenheit\"] },\n },\n required: [\"location\", \"unit\"],\n },\n },\n ],\n});\n\nconsole.log(response.output);\n" }, { "lang": "JavaScript", "label": "Reasoning", "source": "import OpenAI from \"openai\";\n\nconst client = new OpenAI({\n baseURL: \"https://api.cometapi.com/v1\",\n apiKey: process.env.COMETAPI_KEY,\n});\n\nconst response = await client.responses.create({\n model: \"o4-mini\",\n input: \"How much wood would a woodchuck chuck?\",\n reasoning: { effort: \"high\" },\n});\n\nconsole.log(response.output_text);\n" }, { "lang": "Shell", "label": "Text Input", "source": "curl https://api.cometapi.com/v1/responses \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $COMETAPI_KEY\" \\\n -d '{\n \"model\": \"gpt-5.6-sol\",\n \"input\": \"Tell me a three sentence bedtime story about a unicorn.\"\n }'\n" }, { "lang": "Shell", "label": "Image Input", "source": "curl https://api.cometapi.com/v1/responses \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $COMETAPI_KEY\" \\\n -d '{\n \"model\": \"gpt-5.6-sol\",\n \"input\": [\n {\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"input_text\", \"text\": \"What is in this image?\"},\n {\"type\": \"input_image\", \"image_url\": \"https://images.unsplash.com/photo-1500530855697-b586d89ba3ee?w=1200\"}\n ]\n }\n ]\n }'\n" }, { "lang": "Shell", "label": "File Input", "source": "curl https://api.cometapi.com/v1/responses \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $COMETAPI_KEY\" \\\n -d '{\n \"model\": \"gpt-5.6-sol\",\n \"input\": [\n {\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"input_text\", \"text\": \"What is in this file?\"},\n {\"type\": \"input_file\", \"file_url\": \"https://www.berkshirehathaway.com/letters/2024ltr.pdf\"}\n ]\n }\n ]\n }'\n" }, { "lang": "Shell", "label": "Web Search", "source": "curl https://api.cometapi.com/v1/responses \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $COMETAPI_KEY\" \\\n -d '{\n \"model\": \"gpt-5.6-sol\",\n \"tools\": [{\"type\": \"web_search_preview\"}],\n \"input\": \"What was a positive news story from today?\"\n }'\n" }, { "lang": "Shell", "label": "Streaming", "source": "curl https://api.cometapi.com/v1/responses \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $COMETAPI_KEY\" \\\n -d '{\n \"model\": \"gpt-5.6-sol\",\n \"input\": \"Tell me a three sentence bedtime story about a unicorn.\",\n \"stream\": true\n }'\n" }, { "lang": "Shell", "label": "Functions", "source": "curl https://api.cometapi.com/v1/responses \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $COMETAPI_KEY\" \\\n -d '{\n \"model\": \"gpt-5.6-sol\",\n \"input\": \"What is the weather like in Boston today?\",\n \"tools\": [\n {\n \"type\": \"function\",\n \"name\": \"get_current_weather\",\n \"description\": \"Get the current weather in a given location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\"type\": \"string\", \"description\": \"The city and state\"},\n \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]}\n },\n \"required\": [\"location\", \"unit\"]\n }\n }\n ]\n }'\n" }, { "lang": "Shell", "label": "Reasoning", "source": "curl https://api.cometapi.com/v1/responses \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $COMETAPI_KEY\" \\\n -d '{\n \"model\": \"o4-mini\",\n \"input\": \"How much wood would a woodchuck chuck?\",\n \"reasoning\": {\"effort\": \"high\"}\n }'\n" } ] } } }, "components": { "securitySchemes": { "bearerAuth": { "type": "http", "scheme": "bearer", "description": "Bearer token authentication. Use your CometAPI key." } } } }