{"id":"gen-1785504547-shjhS8UlX9wy9URzcg6J","choices":[{"finish_reason":"stop","index":0,"logprobs":null,"message":{"content":"```html\n\n\n\n \n \n Apps Script: Fetch Artificial Analysis Models\n \n \n \n \n \n\n\n

\n \n

\n \n Google Apps Script\n

Fetch available models from the Artificial Analysis API using UrlFetchApp.

\n\n

\n Code.gs\n

\n/**\n * Fetches the list of available models from the Artificial Analysis API.\n */\nfunction getArtificialAnalysisModels() {\n  // Replace with the official Artificial Analysis models endpoint\n  const url = \"https://api.artificialanalysis.ai/v1/models\"; \n\n  // Insert your API key if required by the endpoint\n  const apiKey = \"YOUR_API_KEY\"; \n\n  const options = {\n    method: \"get\",\n    headers: {\n      \"Authorization\": \"Bearer \" + apiKey,\n      \"Accept\": \"application/json\"\n    },\n    muteHttpExceptions: true\n  };\n\n  try {\n    const response = UrlFetchApp.fetch(url, options);\n    const responseCode = response.getResponseCode();\n    const responseText = response.getContentText();\n\n    if (responseCode === 200) {\n      const json = JSON.parse(responseText);\n      \n      // Data is typically nested in a 'data' array for OpenAI-compatible APIs\n      const models = json.data || json; \n      \n      Logger.log(`Successfully fetched ${models.length} models.`);\n      \n      // Log each model identifier to the Apps Script execution log\n      models.forEach(model => {\n        Logger.log(model.id || model.name);\n      });\n      \n      return models;\n    } else {\n      Logger.log(`API Error (${responseCode}): ${responseText}`);\n      return null;\n    }\n  } catch (error) {\n    Logger.log(`Script failed: ${error.toString()}`);\n    return null;\n  }\n}\n

\n\n

Quick Start Guide

Create a new Google Sheet or Document.
Go to Extensions → Apps Script.
Delete any default code and paste the script above.
Update the url and apiKey variables based on Artificial Analysis documentation.
Save (Ctrl+S / Cmd+S).
Select getArtificialAnalysisModels from the toolbar dropdown and click Run.
View the fetched models in the Execution log at the bottom of the screen.

\n\n

\n\n \n \n \n\n\n```","refusal":null,"role":"assistant","annotations":null,"audio":null,"function_call":null,"tool_calls":null,"reasoning":"**Processing the Core Logic**\n\nI'm currently focused on constructing the Google Apps Script to interface with the Artificial Analysis API. My immediate goal is to retrieve a comprehensive list of all available models, which will then be formatted for display within an iframe.\n\n**Structuring the HTML Output**\n\nI'm now focusing on how to best present the Google Apps Script and its interaction with the Artificial Analysis API. My current thinking is to create an HTML wrapper that not only displays the Apps Script code clearly, possibly with syntax highlighting, but also guides the user on its implementation and usage.\n\n**Developing the Endpoint Call**\n\nI'm refining the Google Apps Script to call the `artificialanalysis.ai/v1/models` endpoint, assuming a standard GET request pattern. I'll add a comment about API key requirements and will present this script within an HTML `

` block, as per the iframe rendering requirement.\n\n**Refining the Model Fetch**\n\nI'm now actively refining the Google Apps Script to fetch model data from the `artificialanalysis.ai/v1/models` endpoint. I'm focusing on the `UrlFetchApp.fetch` call and ensuring proper handling of potential API key requirements and the response's JSON structure.\n\n**Implementing the Core Fetch**\n\nI'm currently writing the Google Apps Script to make the `UrlFetchApp.fetch` call to the `artificialanalysis.ai/v1/models` endpoint. I'm also working on parsing the JSON response to extract model IDs and names.\n\n**Integrating with HTML Structure**\n\nI've begun constructing the HTML wrapper, incorporating Tailwind CSS for styling and Prism.js for code highlighting. The goal is to present the fetched model data clearly within this structured iframe.\n\n**Finalizing the Display Logic**\n\nI'm now completing the script's logic to iterate through the fetched model data and populate the HTML. This involves dynamically creating elements to show each model's ID and name effectively.\n\n**Populating the HTML Interface**\n\nI'm now focused on iterating through the model data fetched by the Google Apps Script and dynamically generating HTML elements to display each model's ID and name within the iframe. This involves populating the structured HTML with the retrieved information.\n\n**Populating the Interface**\n\nI'm now focusing on iterating through the model data fetched by the Google Apps Script and dynamically generating HTML elements to display each model's ID and name within the iframe. This involves populating the structured HTML with the retrieved information.\n\n**Refining the Model List**\n\nI'm now processing the retrieved model data, iterating through the list to extract and log each model's ID and name. My current effort is focused on handling potential variations in the API response structure and ensuring accurate data logging.\n\n**Logging Model Data**\n\nI am currently iterating through the fetched model data to log the ID and name of each model. My focus is on accurately processing the API response and ensuring that each piece of relevant information is captured.\n\n","reasoning_details":[{"type":"reasoning.text","text":"**Processing the Core Logic**\n\nI'm currently focused on constructing the Google Apps Script to interface with the Artificial Analysis API. My immediate goal is to retrieve a comprehensive list of all available models, which will then be formatted for display within an iframe.\n\n**Structuring the HTML Output**\n\nI'm now focusing on how to best present the Google Apps Script and its interaction with the Artificial Analysis API. My current thinking is to create an HTML wrapper that not only displays the Apps Script code clearly, possibly with syntax highlighting, but also guides the user on its implementation and usage.\n\n**Developing the Endpoint Call**\n\nI'm refining the Google Apps Script to call the `artificialanalysis.ai/v1/models` endpoint, assuming a standard GET request pattern. I'll add a comment about API key requirements and will present this script within an HTML `` block, as per the iframe rendering requirement.\n\n**Refining the Model Fetch**\n\nI'm now actively refining the Google Apps Script to fetch model data from the `artificialanalysis.ai/v1/models` endpoint. I'm focusing on the `UrlFetchApp.fetch` call and ensuring proper handling of potential API key requirements and the response's JSON structure.\n\n**Implementing the Core Fetch**\n\nI'm currently writing the Google Apps Script to make the `UrlFetchApp.fetch` call to the `artificialanalysis.ai/v1/models` endpoint. I'm also working on parsing the JSON response to extract model IDs and names.\n\n**Integrating with HTML Structure**\n\nI've begun constructing the HTML wrapper, incorporating Tailwind CSS for styling and Prism.js for code highlighting. The goal is to present the fetched model data clearly within this structured iframe.\n\n**Finalizing the Display Logic**\n\nI'm now completing the script's logic to iterate through the fetched model data and populate the HTML. This involves dynamically creating elements to show each model's ID and name effectively.\n\n**Populating the HTML Interface**\n\nI'm now focused on iterating through the model data fetched by the Google Apps Script and dynamically generating HTML elements to display each model's ID and name within the iframe. This involves populating the structured HTML with the retrieved information.\n\n**Populating the Interface**\n\nI'm now focusing on iterating through the model data fetched by the Google Apps Script and dynamically generating HTML elements to display each model's ID and name within the iframe. This involves populating the structured HTML with the retrieved information.\n\n**Refining the Model List**\n\nI'm now processing the retrieved model data, iterating through the list to extract and log each model's ID and name. My current effort is focused on handling potential variations in the API response structure and ensuring accurate data logging.\n\n**Logging Model Data**\n\nI am currently iterating through the fetched model data to log the ID and name of each model. My focus is on accurately processing the API response and ensuring that each piece of relevant information is captured.\n\n","format":"google-gemini-v1","index":0}]},"native_finish_reason":"STOP"}],"created":1785504547,"model":"google/gemini-3.1-pro-preview","object":"chat.completion","service_tier":"default","system_fingerprint":null,"usage":{"completion_tokens":3729,"prompt_tokens":75,"total_tokens":3804,"completion_tokens_details":{"accepted_prediction_tokens":null,"audio_tokens":0,"reasoning_tokens":2273,"rejected_prediction_tokens":null,"image_tokens":0},"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0,"cache_write_tokens":0,"video_tokens":0},"cost":0.044898,"is_byok":false,"cost_details":{"upstream_inference_cost":0.044898,"upstream_inference_prompt_cost":0.00015,"upstream_inference_completions_cost":0.044748}},"provider":"Google"}

create a short apps script to pull all available models from...