60:I[510592,["/_next/static/chunks/41aagivimsyys.js","/_next/static/chunks/1w6b9x4ox-2ab.js","/_next/static/chunks/2du4f4hdcx8vy.js","/_next/static/chunks/3lydxrl6slrf2.js","/_next/static/chunks/1la1pzs9j8idc.js","/_next/static/chunks/2hrea-dwwjjr_.js","/_next/static/chunks/28ougzph2g_t9.js","/_next/static/chunks/3r5yd4k4rms8-.js","/_next/static/chunks/125flzb3dd6q2.js","/_next/static/chunks/0wbn6hf2c6k2t.js","/_next/static/chunks/3pjc6r7w9qxan.js","/_next/static/chunks/39vtz3iby1cf_.js","/_next/static/chunks/2v5t_o330ua3s.js","/_next/static/chunks/3trxs7mom31q3.js","/_next/static/chunks/3nk0wrm9knx3m.js","/_next/static/chunks/1kxuebyfge68i.js","/_next/static/chunks/22e07luyn9bfp.js","/_next/static/chunks/1_lyn-lih218f.js","/_next/static/chunks/1dwajbnfjnvzx.js","/_next/static/chunks/0bvim9_g0hu68.js","/_next/static/chunks/3c7e5ojau5akn.js","/_next/static/chunks/0gf5i4fmb2vxz.js","/_next/static/chunks/3a57oltrmz5kh.js","/_next/static/chunks/3j9dwf14_qrhp.js","/_next/static/chunks/21k90yd6lrzsc.js","/_next/static/chunks/3-5mrnq0kjby0.js","/_next/static/chunks/1r40kzfox4d83.js","/_next/static/chunks/24_gg9g_4g22i.js","/_next/static/chunks/0l0c166aeafbb.js","/_next/static/chunks/0czpc-f8wjri8.js","/_next/static/chunks/3xaq4o65p_dw0.js","/_next/static/chunks/2dyt8rg18m736.js","/_next/static/chunks/43b9k5vjei3d1.js","/_next/static/chunks/3eatuedmz1636.js","/_next/static/chunks/2pqhypz2vo8mu.js","/_next/static/chunks/1nz6e-lpv7lb-.js","/_next/static/chunks/2l525havh4_yi.js","/_next/static/chunks/0fpx6rmsgi4um.js"],"PlaygroundContextProvider"] 61:T15a8,# MiniMax-M3 ## Description MiniMax-M3 is a multimodal vision-language model (VLM) built on a Mixture-of-Experts architecture for long-context reasoning, agentic workflows, and creative tasks. The model processes text, image, and video inputs and produces text outputs, with emphasis on long-form video understanding, long-horizon coding, and design and creative workflows. *This model is ready for non-commercial use.* ## Third-Party Community Consideration: This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [MiniMax-M3 Model Card](https://huggingface.co/MiniMaxAI). ## License and Terms of Use: **GOVERNING TERMS:** The trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf); use of this model is governed by the [NVIDIA Software and Model Evaluation license](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/). ADDITIONAL INFORMATION: [Non-Commercial MiniMax License](https://github.com/MiniMax-AI/MiniMax-M2.7/blob/main/LICENSE). Copyright (c) 2026 MiniMax. ## Deployment Geography: Global ## Use Case: **Use Case:** MiniMax-M3 is intended for multimodal understanding across text, image, and video; long-form video understanding (up to 30 minutes); long-horizon coding tasks (8+ hours); agentic and tool-use workflows; and design and creative tasks. ## Release Date: **Build.NVIDIA.com:** 06/07/2026 via [link](https://build.nvidia.com/minimaxai/minimax-m3)
**Huggingface:** 06/07/2026 via [link](https://huggingface.co/MiniMaxAI) ## Reference(s): - [MiniMax-M3 Model Card](https://huggingface.co/MiniMaxAI) ## Model Architecture: **Architecture Type:** Transformer **Network Architecture:** Mixture-of-Experts (multimodal) **Total Parameters:** 428B **Active Parameters:** Approximately 22B per token (A22B) **Vision Encoder:** ViT for image and video input ### Input: **Input Types:** Text, Image, Video **Input Formats:** String, Red, Green, Blue (RGB), Video frames **Input Parameters:** One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D) **Other Input Properties:** Supports long-form video input up to 30 minutes. **Input Context Length (ISL):** 1 million tokens ### Output: **Output Types:** Text **Output Format:** String **Output Parameters:** One-Dimensional (1D) **Other Output Properties:** None __Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.__ ## Software Integration: **Runtime Engines:** - **vLLM** - **SGLang** **Supported Hardware:** - **NVIDIA Blackwell:** B200, B100, GB200 - **NVIDIA Hopper:** H100, H200 **Preferred Operating Systems:** Linux __The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.__ ## Model Version(s) MiniMax-M3 v1.0 ## Training, Testing, and Evaluation Datasets: ### Training Dataset **Data Modality:** Text, Image, Video **Image Training Data Size:** Undisclosed **Text Training Data Size:** Undisclosed **Training Data Collection:** Undisclosed **Training Labeling:** Undisclosed **Training Properties:** Undisclosed ### Testing Dataset **Testing Data Collection:** Undisclosed **Testing Labeling:** Undisclosed **Testing Properties:** Undisclosed ### Evaluation Dataset **Evaluation Benchmark Score:** Undisclosed **Evaluation Data Collection:** Undisclosed **Evaluation Labeling:** Undisclosed **Evaluation Properties:** Undisclosed ## Inference **Acceleration Engine:** Dynamo + SGLang **Test Hardware:** NVIDIA GB200x4 ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse. Please make sure you have proper rights and permissions for all input image and video content. Video and image inputs may capture identifiable people, voices, likenesses, biometric information, personal health information, or intellectual property; the model does not blur, redact, anonymize, or maintain the proportions of subjects included in the input, and long-form video input (up to 30 minutes) can contain incidental third-party subjects. Obtain consent where required and avoid submitting content you are not authorized to process. Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).62:T52f,{ "messages": [ { "role": "user", "content": "What is the weather in Santa Clara, CA?" } ], "model": "minimaxai/minimax-m3", "max_tokens": 1024, "stream": true, "tool_choice": "auto", "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "A tool that gets the current weather at a location, if one is specified, and defaults to the user's location.", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The location to find the weather of, or if not provided, it's the default location." }, "unit": { "type": "string", "enum": [ "u", "m" ], "description": "Whether to use SI or USCS units (celsius or fahrenheit). Infer this from the user's location." } } } } } ] } 63:T1d2a,<% // Catalog ejs locals replace JSON with {stringify} only (spread skips non-enumerable parse). const deepClone = (value) => { if (Array.isArray(value)) return value.map(deepClone); if (value && typeof value === "object") { const out = {}; for (const key of Object.keys(value)) out[key] = deepClone(value[key]); return out; } return value; }; const mediaConfig = {"image": {"mimeTypes": {}}, "video": {"mimeTypes": {}}, "audio": {"mimeTypes": {}, "defaultFormat": "wav"}}; const stripNulls = (value) => { if (Array.isArray(value)) return value.map(stripNulls); if (value && typeof value === "object") { const out = {}; for (const [key, item] of Object.entries(value)) { if (item === null || item === undefined) continue; out[key] = stripNulls(item); } return out; } return value; }; const payloadForSnippet = stripNulls(deepClone(request || {})); const hasStream = payloadForSnippet.stream !== undefined && payloadForSnippet.stream !== null; const pyStream = payloadForSnippet.stream ? "True" : "False"; if (hasStream) payloadForSnippet.stream = "__STREAM__"; const hasMediaKind = (kind) => Object.keys(mediaConfig[kind].mimeTypes || {}).length > 0; const isInlineDataUrl = (url, kind) => ( typeof url === "string" && new RegExp(`^data:${kind}/[^;]+;base64,`, "i").test(url) ); const mediaUrl = (part, key) => { const slot = part?.[key]; return typeof slot === "string" ? slot : slot?.url; }; const setMediaUrl = (part, key, url) => { if (typeof part[key] === "string") part[key] = url; else { if (!part[key] || typeof part[key] !== "object") part[key] = {}; part[key].url = url; } }; let imageCount = 0; let videoCount = 0; let audioCount = 0; (payloadForSnippet.messages || []).forEach((message) => { if (!Array.isArray(message?.content)) return; message.content.forEach((part) => { const imageUrl = mediaUrl(part, "image_url"); const videoUrl = mediaUrl(part, "video_url"); const audioUrl = mediaUrl(part, "audio_url"); // File helpers only for inline base64; http(s) URLs stay as-is in the payload. if (hasMediaKind("image") && part?.type === "image_url" && isInlineDataUrl(imageUrl, "image")) { setMediaUrl(part, "image_url", `__IMAGE_DATA_URL_${imageCount}__`); imageCount += 1; } else if (hasMediaKind("video") && part?.type === "video_url" && isInlineDataUrl(videoUrl, "video")) { setMediaUrl(part, "video_url", `__VIDEO_DATA_URL_${videoCount}__`); videoCount += 1; } else if (hasMediaKind("audio") && part?.type === "audio_url" && isInlineDataUrl(audioUrl, "audio")) { setMediaUrl(part, "audio_url", `__AUDIO_DATA_URL_${audioCount}__`); audioCount += 1; } else if (hasMediaKind("audio") && part?.type === "input_audio" && part.input_audio?.data) { part.input_audio.data = `__AUDIO_B64_${audioCount}__`; part.input_audio.format = part.input_audio.format || mediaConfig.audio.defaultFormat; audioCount += 1; } }); }); const pyRepr = (value, level = 0) => { if (value === null) return "None"; if (typeof value === "boolean") return value ? "True" : "False"; if (typeof value === "number") return String(value); if (typeof value === "string") return JSON.stringify(value); const pad = " ".repeat(level); const innerPad = " ".repeat(level + 2); if (Array.isArray(value)) { if (value.length === 0) return "[]"; return "[\n" + value.map((item) => `${innerPad}${pyRepr(item, level + 2)}`).join(",\n") + "\n" + pad + "]"; } if (typeof value === "object") { const entries = Object.entries(value); if (entries.length === 0) return "{}"; return "{\n" + entries.map(([key, item]) => `${innerPad}${JSON.stringify(key)}: ${pyRepr(item, level + 2)}`).join(",\n") + "\n" + pad + "}"; } return "None"; }; const replaceAll = (s, a, b) => s.split(a).join(b); let renderedPayload = pyRepr(payloadForSnippet); if (hasStream) renderedPayload = replaceAll(renderedPayload, '"__STREAM__"', "stream"); for (let i = 0; i < imageCount; i++) { renderedPayload = replaceAll(renderedPayload, `"__IMAGE_DATA_URL_${i}__"`, `image_data_urls[${i}]`); } for (let i = 0; i < videoCount; i++) { renderedPayload = replaceAll(renderedPayload, `"__VIDEO_DATA_URL_${i}__"`, `video_data_urls[${i}]`); } for (let i = 0; i < audioCount; i++) { renderedPayload = replaceAll(renderedPayload, `"__AUDIO_DATA_URL_${i}__"`, `audio_data_urls[${i}]`); renderedPayload = replaceAll(renderedPayload, `"__AUDIO_B64_${i}__"`, `audio_b64s[${i}]`); } // Either file helpers XOR inline base64 - never emit unused helpers. const usesFileHelpers = imageCount + videoCount + audioCount > 0 && [...Array(imageCount).keys()].every((i) => renderedPayload.includes(`image_data_urls[${i}]`)) && [...Array(videoCount).keys()].every((i) => renderedPayload.includes(`video_data_urls[${i}]`)) && [...Array(audioCount).keys()].every( (i) => renderedPayload.includes(`audio_data_urls[${i}]`) || renderedPayload.includes(`audio_b64s[${i}]`) ); if (!usesFileHelpers) { imageCount = 0; videoCount = 0; audioCount = 0; const restored = stripNulls(deepClone(request || {})); const restoredStream = restored.stream !== undefined && restored.stream !== null; if (restoredStream) restored.stream = "__STREAM__"; renderedPayload = pyRepr(restored); if (restoredStream) renderedPayload = replaceAll(renderedPayload, '"__STREAM__"', "stream"); } const hasMedia = usesFileHelpers; const mimePy = (mimes) => "{" + Object.entries(mimes).map(([k, v]) => `${JSON.stringify(k)}: ${JSON.stringify(v)}`).join(", ") + "}"; %> <% if (hasMedia) { %>import base64 from pathlib import Path <% } %>import requests invoke_url = "https://integrate.api.nvidia.com/v1/chat/completions" stream = <%- pyStream %> <% if (hasMedia) { %> def media_data_url(kind, index, mime_types): for suffix, mime in mime_types.items(): path = Path(f"{kind}_{index}{suffix}") if path.exists(): return f"data:{mime};base64,{base64.b64encode(path.read_bytes()).decode()}" raise FileNotFoundError(f"Expected {kind}_{index} with suffix in {list(mime_types)}") <% if (imageCount > 0) { %> image_data_urls = [media_data_url("image", i, <%- mimePy(mediaConfig.image.mimeTypes) %>) for i in <%- imageCount === 1 ? "[1]" : ("range(1, " + (imageCount + 1) + ")") %>] <% } %><% if (videoCount > 0) { %> video_data_urls = [media_data_url("video", i, <%- mimePy(mediaConfig.video.mimeTypes) %>) for i in <%- videoCount === 1 ? "[1]" : ("range(1, " + (videoCount + 1) + ")") %>] <% } %><% if (audioCount > 0) { %> audio_data_urls = [media_data_url("audio", i, <%- mimePy(mediaConfig.audio.mimeTypes) %>) for i in <%- audioCount === 1 ? "[1]" : ("range(1, " + (audioCount + 1) + ")") %>] audio_b64s = [url.split(",", 1)[1] for url in audio_data_urls] <% } %><% } %> headers = { "Authorization": "Bearer $NVIDIA_API_KEY", "Accept": "text/event-stream" if stream else "application/json", } payload = <%- renderedPayload %> response = requests.post(invoke_url, headers=headers, json=payload, stream=stream) if stream: for line in response.iter_lines(): if line: print(line.decode("utf-8")) else: print(response.json()) 64:T1647,<% const deepClone = (value) => { if (Array.isArray(value)) return value.map(deepClone); if (value && typeof value === "object") { const out = {}; for (const key of Object.keys(value)) out[key] = deepClone(value[key]); return out; } return value; }; const mediaConfig = {"image": {"mimeTypes": {}}, "video": {"mimeTypes": {}}, "audio": {"mimeTypes": {}, "defaultFormat": "wav"}}; const payloadForSnippet = deepClone(request || {}); const messagesForSnippet = deepClone(payloadForSnippet.messages || []); const hasMediaKind = (kind) => Object.keys(mediaConfig[kind].mimeTypes || {}).length > 0; const isInlineDataUrl = (url, kind) => ( typeof url === "string" && new RegExp(`^data:${kind}/[^;]+;base64,`, "i").test(url) ); const mediaUrl = (part, key) => { const slot = part?.[key]; return typeof slot === "string" ? slot : slot?.url; }; const setMediaUrl = (part, key, url) => { if (typeof part[key] === "string") part[key] = url; else { if (!part[key] || typeof part[key] !== "object") part[key] = {}; part[key].url = url; } }; let imageCount = 0; let hasUnsupportedMedia = false; messagesForSnippet.forEach((message) => { if (!Array.isArray(message?.content)) return; message.content.forEach((part) => { const imageUrl = mediaUrl(part, "image_url"); if (hasMediaKind("image") && part?.type === "image_url" && isInlineDataUrl(imageUrl, "image")) { setMediaUrl(part, "image_url", `__IMAGE_DATA_URL_${imageCount}__`); imageCount += 1; } else if (part?.type === "video_url" && isInlineDataUrl(mediaUrl(part, "video_url"), "video")) { // LangChain cannot ship local file helpers for video; reject inline only. hasUnsupportedMedia = true; setMediaUrl(part, "video_url", "__UNSUPPORTED_MEDIA_DATA__"); } else if (part?.type === "audio_url" && isInlineDataUrl(mediaUrl(part, "audio_url"), "audio")) { hasUnsupportedMedia = true; setMediaUrl(part, "audio_url", "__UNSUPPORTED_MEDIA_DATA__"); } else if (part?.type === "input_audio" && part.input_audio?.data) { hasUnsupportedMedia = true; part.input_audio.data = "__UNSUPPORTED_MEDIA_DATA__"; } }); }); const pyRepr = (value, level = 0) => { if (value === null) return "None"; if (typeof value === "boolean") return value ? "True" : "False"; if (typeof value === "number") return String(value); if (typeof value === "string") return JSON.stringify(value); const pad = " ".repeat(level); const innerPad = " ".repeat(level + 2); if (Array.isArray(value)) { if (value.length === 0) return "[]"; return "[\n" + value.map((item) => `${innerPad}${pyRepr(item, level + 2)},`).join("\n") + "\n" + pad + "]"; } if (typeof value === "object") { const entries = Object.entries(value); if (entries.length === 0) return "{}"; return "{\n" + entries.map(([key, item]) => `${innerPad}${JSON.stringify(key)}: ${pyRepr(item, level + 2)},`).join("\n") + "\n" + pad + "}"; } return "None"; }; const replaceAll = (s, a, b) => s.split(a).join(b); let renderedMessages = pyRepr(messagesForSnippet); for (let i = 0; i < imageCount; i++) { renderedMessages = replaceAll(renderedMessages, `"__IMAGE_DATA_URL_${i}__"`, `image_data_urls[${i}]`); } // Either file helpers XOR inline base64 - never emit unused helpers. const usesFileHelpers = imageCount > 0 && [...Array(imageCount).keys()].every((i) => renderedMessages.includes(`image_data_urls[${i}]`)); if (!usesFileHelpers) { imageCount = 0; renderedMessages = pyRepr(deepClone((request || {}).messages || [])); } %> from langchain_nvidia_ai_endpoints import ChatNVIDIA <% if (imageCount > 0) { %>from pathlib import Path import base64 <% } %> client = ChatNVIDIA( model="<%- request.model %>", api_key="$NVIDIA_API_KEY", temperature=<%- request.temperature %>, top_p=<%- request.top_p %>, max_completion_tokens=<%- request.max_tokens %>, ) <% if (imageCount > 0) { %> def find_image_path(index): image_mime_types = <%- pyRepr(mediaConfig.image.mimeTypes) %> for suffix in image_mime_types: path = Path(f"image_{index}{suffix}") if path.exists(): return path expected = "/".join(f".{suffix.lstrip('.')}" for suffix in image_mime_types) raise FileNotFoundError(f"Expected image_{index}{expected}") def read_image_data_url(path): image_mime_types = <%- pyRepr(mediaConfig.image.mimeTypes) %> with open(path, "rb") as f: image_b64 = base64.b64encode(f.read()).decode() return f"data:{image_mime_types[path.suffix.lower()]};base64,{image_b64}" image_data_urls = [read_image_data_url(find_image_path(i)) for i in range(1, <%- imageCount + 1 %>)] <% } %><% if (hasUnsupportedMedia) { %> raise ValueError("LangChain snippet currently supports text and image input. Use Python, Node.js, or Shell for video or audio input.") <% } %> lc_messages = <%- renderedMessages %> <% if (request.stream) { %> for chunk in client.stream(lc_messages<%= request.chat_template_kwargs ? ', chat_template_kwargs=' + pyRepr(request.chat_template_kwargs) : '' %>): if chunk.additional_kwargs and "reasoning_content" in chunk.additional_kwargs: print(chunk.additional_kwargs["reasoning_content"], end="") print(chunk.content, end="") <% } else { %> response = client.invoke(lc_messages<%= request.chat_template_kwargs ? ', chat_template_kwargs=' + pyRepr(request.chat_template_kwargs) : '' %>) if response.additional_kwargs and "reasoning_content" in response.additional_kwargs: print(response.additional_kwargs["reasoning_content"]) print(response.content) <% } %> 65:T1a86,<% const deepClone = (value) => { if (Array.isArray(value)) return value.map(deepClone); if (value && typeof value === "object") { const out = {}; for (const key of Object.keys(value)) out[key] = deepClone(value[key]); return out; } return value; }; const mediaConfig = {"image": {"mimeTypes": {}}, "video": {"mimeTypes": {}}, "audio": {"mimeTypes": {}, "defaultFormat": "wav"}}; const payloadForSnippet = deepClone(request || {}); const hasStream = payloadForSnippet.stream !== undefined && payloadForSnippet.stream !== null; if (hasStream) payloadForSnippet.stream = "__STREAM__"; const hasMediaKind = (kind) => Object.keys(mediaConfig[kind].mimeTypes || {}).length > 0; const isInlineDataUrl = (url, kind) => ( typeof url === "string" && new RegExp(`^data:${kind}/[^;]+;base64,`, "i").test(url) ); let imageCount = 0; let videoCount = 0; let audioCount = 0; (payloadForSnippet.messages || []).forEach((message) => { if (!Array.isArray(message?.content)) return; message.content.forEach((part) => { if (hasMediaKind("image") && part?.type === "image_url" && isInlineDataUrl(part.image_url?.url, "image")) { part.image_url.url = `__IMAGE_DATA_URL_${imageCount}__`; imageCount += 1; } else if (hasMediaKind("video") && part?.type === "video_url" && isInlineDataUrl(part.video_url?.url, "video")) { part.video_url.url = `__VIDEO_DATA_URL_${videoCount}__`; videoCount += 1; } else if (hasMediaKind("audio") && part?.type === "audio_url" && isInlineDataUrl(part.audio_url?.url, "audio")) { part.audio_url.url = `__AUDIO_DATA_URL_${audioCount}__`; audioCount += 1; } else if (hasMediaKind("audio") && part?.type === "input_audio" && part.input_audio?.data) { part.input_audio.data = `__AUDIO_B64_${audioCount}__`; part.input_audio.format = part.input_audio.format || mediaConfig.audio.defaultFormat; audioCount += 1; } }); }); const replaceAll = (s, a, b) => s.split(a).join(b); let renderedPayload = JSON.stringify(payloadForSnippet, null, 2); if (hasStream) renderedPayload = replaceAll(renderedPayload, '"__STREAM__"', "stream"); for (let i = 0; i < imageCount; i++) { renderedPayload = replaceAll(renderedPayload, `"__IMAGE_DATA_URL_${i}__"`, `imageDataUrls[${i}]`); } for (let i = 0; i < videoCount; i++) { renderedPayload = replaceAll(renderedPayload, `"__VIDEO_DATA_URL_${i}__"`, `videoDataUrls[${i}]`); } for (let i = 0; i < audioCount; i++) { renderedPayload = replaceAll(renderedPayload, `"__AUDIO_DATA_URL_${i}__"`, `audioDataUrls[${i}]`); renderedPayload = replaceAll(renderedPayload, `"__AUDIO_B64_${i}__"`, `audioB64s[${i}]`); } // Either file helpers XOR inline base64 - never emit unused helpers. const usesFileHelpers = imageCount + videoCount + audioCount > 0 && [...Array(imageCount).keys()].every((i) => renderedPayload.includes(`imageDataUrls[${i}]`)) && [...Array(videoCount).keys()].every((i) => renderedPayload.includes(`videoDataUrls[${i}]`)) && [...Array(audioCount).keys()].every( (i) => renderedPayload.includes(`audioDataUrls[${i}]`) || renderedPayload.includes(`audioB64s[${i}]`) ); if (!usesFileHelpers) { imageCount = 0; videoCount = 0; audioCount = 0; const restored = deepClone(request || {}); const restoredStream = restored.stream !== undefined && restored.stream !== null; if (restoredStream) restored.stream = "__STREAM__"; renderedPayload = JSON.stringify(restored, null, 2); if (restoredStream) renderedPayload = replaceAll(renderedPayload, '"__STREAM__"', "stream"); } const hasMedia = usesFileHelpers; %> import axios from 'axios'; <% if (hasMedia) { %>import { existsSync } from 'node:fs'; import { readFile } from 'node:fs/promises'; import { extname } from 'node:path'; <% } %> const invokeUrl = "https://integrate.api.nvidia.com/v1/chat/completions"; const stream = <%- request.stream %>; const headers = { "Authorization": "Bearer $NVIDIA_API_KEY", "Accept": stream ? "text/event-stream" : "application/json" }; <% if (hasMedia) { %> function findMediaPath(kind, index, mimeTypes) { for (const suffix of Object.keys(mimeTypes)) { const path = `${kind}_${index}${suffix}`; if (existsSync(path)) { return path; } } const expected = Object.keys(mimeTypes).join("/"); throw new Error(`Expected ${kind}_${index}${expected}`); } async function readMediaB64(path) { return Buffer.from(await readFile(path)).toString('base64'); } function mediaDataUrl(path, mediaB64, mimeTypes) { return `data:${mimeTypes[extname(path).toLowerCase()]};base64,${mediaB64}`; } <% } %> async function main() { <% if (imageCount > 0) { %> const imageMimeTypes = <%- JSON.stringify(mediaConfig.image.mimeTypes, null, 2) %>; const imagePaths = [<% for (let i = 1; i <= imageCount; i++) { %>findMediaPath("image", <%- i %>, imageMimeTypes)<%- i < imageCount ? ", " : "" %><% } %>]; const imageB64s = await Promise.all(imagePaths.map(readMediaB64)); const imageDataUrls = imagePaths.map((path, index) => mediaDataUrl(path, imageB64s[index], imageMimeTypes)); <% } %><% if (videoCount > 0) { %> const videoMimeTypes = <%- JSON.stringify(mediaConfig.video.mimeTypes, null, 2) %>; const videoPaths = [<% for (let i = 1; i <= videoCount; i++) { %>findMediaPath("video", <%- i %>, videoMimeTypes)<%- i < videoCount ? ", " : "" %><% } %>]; const videoB64s = await Promise.all(videoPaths.map(readMediaB64)); const videoDataUrls = videoPaths.map((path, index) => mediaDataUrl(path, videoB64s[index], videoMimeTypes)); <% } %><% if (audioCount > 0) { %> const audioMimeTypes = <%- JSON.stringify(mediaConfig.audio.mimeTypes, null, 2) %>; const audioPaths = [<% for (let i = 1; i <= audioCount; i++) { %>findMediaPath("audio", <%- i %>, audioMimeTypes)<%- i < audioCount ? ", " : "" %><% } %>]; const audioB64s = await Promise.all(audioPaths.map(readMediaB64)); const audioDataUrls = audioPaths.map((path, index) => mediaDataUrl(path, audioB64s[index], audioMimeTypes)); <% } %> const payload = <%- renderedPayload %>; const response = await axios.post(invokeUrl, payload, { headers: headers, responseType: stream ? 'stream' : 'json' }); if (stream) { response.data.on('data', (chunk) => { console.log(chunk.toString()); }); } else { console.log(JSON.stringify(response.data)); } } main().catch(error => { if (error.response) { console.error(`HTTP ${error.response.status}`); if (error.response.data?.on) { error.response.data.on('data', (chunk) => console.error(chunk.toString())); } else { console.error(error.response.data); } } else { console.error(error); } }); 66:T153d,<% const deepClone = (value) => { if (Array.isArray(value)) return value.map(deepClone); if (value && typeof value === "object") { const out = {}; for (const key of Object.keys(value)) out[key] = deepClone(value[key]); return out; } return value; }; const mediaConfig = {"image": {"mimeTypes": {}}, "video": {"mimeTypes": {}}, "audio": {"mimeTypes": {}, "defaultFormat": "wav"}}; const payloadForSnippet = deepClone(request || {}); const hasMediaKind = (kind) => Object.keys(mediaConfig[kind].mimeTypes || {}).length > 0; const isInlineDataUrl = (url, kind) => ( typeof url === "string" && new RegExp(`^data:${kind}/[^;]+;base64,`, "i").test(url) ); let imageCount = 0; let videoCount = 0; let audioCount = 0; (payloadForSnippet.messages || []).forEach((message) => { if (!Array.isArray(message?.content)) return; message.content.forEach((part) => { if (hasMediaKind("image") && part?.type === "image_url" && isInlineDataUrl(part.image_url?.url, "image")) { part.image_url.url = `__IMAGE_DATA_URL_${imageCount}__`; imageCount += 1; } else if (hasMediaKind("video") && part?.type === "video_url" && isInlineDataUrl(part.video_url?.url, "video")) { part.video_url.url = `__VIDEO_DATA_URL_${videoCount}__`; videoCount += 1; } else if (hasMediaKind("audio") && part?.type === "audio_url" && isInlineDataUrl(part.audio_url?.url, "audio")) { part.audio_url.url = `__AUDIO_DATA_URL_${audioCount}__`; audioCount += 1; } else if (hasMediaKind("audio") && part?.type === "input_audio" && part.input_audio?.data) { part.input_audio.data = `__AUDIO_B64_${audioCount}__`; part.input_audio.format = part.input_audio.format || mediaConfig.audio.defaultFormat; audioCount += 1; } }); }); const replaceAll = (s, a, b) => s.split(a).join(b); let renderedPayload = JSON.stringify(payloadForSnippet, null, 2); for (let i = 0; i < imageCount; i++) { renderedPayload = replaceAll(renderedPayload, `"__IMAGE_DATA_URL_${i}__"`, `"${"${"}image_${i + 1}_data_url}"`); } for (let i = 0; i < videoCount; i++) { renderedPayload = replaceAll(renderedPayload, `"__VIDEO_DATA_URL_${i}__"`, `"${"${"}video_${i + 1}_data_url}"`); } for (let i = 0; i < audioCount; i++) { renderedPayload = replaceAll(renderedPayload, `"__AUDIO_DATA_URL_${i}__"`, `"${"${"}audio_${i + 1}_data_url}"`); renderedPayload = replaceAll(renderedPayload, `"__AUDIO_B64_${i}__"`, `"${"${"}audio_${i + 1}_b64}"`); } // Either file helpers XOR inline base64 - never emit unused helpers. const usesFileHelpers = imageCount + videoCount + audioCount > 0 && [...Array(imageCount).keys()].every((i) => renderedPayload.includes(`image_${i + 1}_data_url`)) && [...Array(videoCount).keys()].every((i) => renderedPayload.includes(`video_${i + 1}_data_url`)) && [...Array(audioCount).keys()].every( (i) => renderedPayload.includes(`audio_${i + 1}_data_url`) || renderedPayload.includes(`audio_${i + 1}_b64`) ); if (!usesFileHelpers) { imageCount = 0; videoCount = 0; audioCount = 0; renderedPayload = JSON.stringify(deepClone(request || {}), null, 2); } const hasMedia = usesFileHelpers; %> stream=<%- request.stream %> if [ "$stream" = true ]; then accept_header='Accept: text/event-stream' else accept_header='Accept: application/json' fi <% if (hasMedia) { %> mime_type_for() { case "$1" in <% for (const [suffix, mimeType] of Object.entries({...mediaConfig.image.mimeTypes, ...mediaConfig.video.mimeTypes, ...mediaConfig.audio.mimeTypes})) { %> *<%- suffix %>) echo "<%- mimeType %>" ;; <% } %> *) echo "Unsupported media file: $1" >&2; return 1 ;; esac } find_media_path() { local kind="$1" local index="$2" shift 2 local ext path for ext in "$@"; do path="${kind}_${index}.${ext}" if [ -f "$path" ]; then echo "$path" return 0 fi done echo "Expected ${kind}_${index}.$*" >&2 return 1 } media_b64() { base64 < "$1" | tr -d '\n' } media_data_url() { local path="$1" local mime_type media_base64 mime_type=$(mime_type_for "$path") || return 1 media_base64=$(media_b64 "$path") || return 1 printf 'data:%s;base64,%s' "$mime_type" "$media_base64" } <% for (let i = 1; i <= imageCount; i++) { %> image_<%- i %>_path=$(find_media_path image <%- i %><% for (const suffix of Object.keys(mediaConfig.image.mimeTypes)) { %> <%- suffix.slice(1) %><% } %>) image_<%- i %>_data_url=$(media_data_url "$image_<%- i %>_path") <% } %><% for (let i = 1; i <= videoCount; i++) { %> video_<%- i %>_path=$(find_media_path video <%- i %><% for (const suffix of Object.keys(mediaConfig.video.mimeTypes)) { %> <%- suffix.slice(1) %><% } %>) video_<%- i %>_data_url=$(media_data_url "$video_<%- i %>_path") <% } %><% for (let i = 1; i <= audioCount; i++) { %> audio_<%- i %>_path=$(find_media_path audio <%- i %><% for (const suffix of Object.keys(mediaConfig.audio.mimeTypes)) { %> <%- suffix.slice(1) %><% } %>) audio_<%- i %>_b64=$(media_b64 "$audio_<%- i %>_path") audio_<%- i %>_data_url=$(media_data_url "$audio_<%- i %>_path") <% } %><% } %> cat > payload.json < JSON curl https://integrate.api.nvidia.com/v1/chat/completions \ -H "Authorization: Bearer $NVIDIA_API_KEY" \ -H "Content-Type: application/json" \ -H "$accept_header" \ -d @payload.json57:[["$","$L40",".0",{"state":{"mutations":[],"queries":[{"dehydratedAt":1790067257184,"state":{"data":"$37:props:state:queries:0:state:data","dataUpdateCount":1,"dataUpdatedAt":1790067257139,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":"$37:props:state:queries:0:queryKey","queryHash":"[\"search\",\"labelsets\",\"ENDPOINT\"]"},{"dehydratedAt":1790067257184,"state":{"data":null,"dataUpdateCount":1,"dataUpdatedAt":1790067257139,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":"$37:props:state:queries:1:queryKey","queryHash":"[\"killswitches/riva-magpie-2.enabled\"]"},{"dehydratedAt":1790067257184,"state":{"data":"$37:props:state:queries:2:state:data","dataUpdateCount":1,"dataUpdatedAt":1790067257140,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":"$37:props:state:queries:2:queryKey","queryHash":"[\"killswitches/media/select-example.models.yaml\"]"},{"dehydratedAt":1790067257184,"state":{"data":"$37:props:state:queries:3:state:data","dataUpdateCount":1,"dataUpdatedAt":1790067257140,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":"$37:props:state:queries:3:queryKey","queryHash":"[\"content/legal/model-terms-of-service.yaml\"]"},{"dehydratedAt":1790067257184,"state":{"data":"$37:props:state:queries:4:state:data","dataUpdateCount":1,"dataUpdatedAt":1790067257140,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":"$37:props:state:queries:4:queryKey","queryHash":"[\"content/legal/playground-disclaimers.yaml\"]"}]},"children":["$","$L60",null,{"initialState":{"artifact":{"artifactType":"ENDPOINT","name":"minimax-m3","displayName":"minimax-m3","publisher":"minimaxai","shortDescription":"MiniMax M3 Preview is a multimodal MoE vision-language model with strong reasoning, coding, and tool-calling capabilities.","logo":"https://assets.ngc.nvidia.com/products/api-catalog/images/minimax-m2_7.jpg","labels":["cloudPartnerType:endpoint:cloud_partner_type_bitdeer","cloudPartnerType:endpoint:cloud_partner_type_gmicloud","cloudPartnerType:endpoint:cloud_partner_type_openrouter","cloudPartnerType:endpoint:cloud_partner_type_together","cloudPartnerType:endpoint:cloud_partner_type_vultr","coding","nimType:endpoint:nim_type_preview","nimType:endpoint:nim_type_upgrade_available","playgroundType:endpoint:playgroundtype_chat","reasoning","text-to-text"],"attributes":[{"key":"AVAILABLE","value":"false"},{"key":"DEPRECATION","value":"09/08/2026"},{"key":"PREVIEW","value":"true"},{"key":"last_month_api_invocation_count","value":"10389337"},{"key":"last_month_download_count","value":"0"}],"updatedDate":"2026-06-12T14:01:30.878Z","canGuestDownload":true,"createdDate":"2026-06-12T14:01:30.878Z","description":"$61","isPublic":true,"isReadOnly":true,"orgName":"qc69jvmznzxy"},"specAttributes":"$58:props:spec:attributes","modelPath":"/minimaxai/minimax-m3","openApiSpec":{"openapi":"3.1.0","info":{"title":"NVIDIA NIM API for minimaxai/minimax-m3","description":"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/minimaxai-minimax-m3 for more details. This trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the NVIDIA Open Model License Agreement. Additional Information: Modified MIT License.","version":"1.0.0","termsOfService":"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf","contact":{"name":"NVIDIA Enterprise Support","url":"https://www.nvidia.com/en-us/support/enterprise/"},"license":{"name":"NVIDIA Open Model License Agreement","url":"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/"}},"servers":[{"url":"https://integrate.api.nvidia.com/v1/"}],"paths":{"/chat/completions":{"post":{"operationId":"create_chat_completion_v1_chat_completions_post","tags":["Chat"],"summary":"Creates a model response for the given chat conversation.","description":"Given a list of messages comprising a conversation, the model will return a response. Compatible with OpenAI. See https://platform.openai.com/docs/api-reference/chat/create","requestBody":{"content":{"application/json":{"schema":{"additionalProperties":false,"properties":{"model":{"type":"string","title":"Model","default":"minimaxai/minimax-m3"},"messages":{"description":"A list of messages comprising the conversation so far. The roles of the messages must be alternating between `user` and `assistant`. The last input message should have role `user`. A message with the the `system` role is optional, and must be the very first message if it is present; `context` is also optional, but must come before a user question.","examples":[[{"content":"I am going to Paris, what should I see?","role":"user"}]],"items":{"additionalProperties":false,"properties":{"role":{"description":"The role of the message author.","enum":["system","user","assistant","tool"],"title":"Role","type":"string"},"content":{"description":"The contents of the message. A string, or (for multimodal user messages) a list of content parts: text plus image_url/video_url (each a public URL or a base64 data URI).","title":"Content","anyOf":[{"type":"string"},{"type":"array","items":{"oneOf":[{"type":"object","title":"ContentPartText","required":["type","text"],"properties":{"type":{"type":"string","enum":["text"],"const":"text","title":"Type"},"text":{"type":"string","title":"Text"}}},{"type":"object","title":"ContentPartImage","required":["type","image_url"],"properties":{"type":{"type":"string","enum":["image_url"],"const":"image_url","title":"Type"},"image_url":{"type":"object","title":"ImageURL","required":["url"],"properties":{"url":{"type":"string","title":"Url","description":"A public image URL or a base64 data URI (data:image/;base64,)."}}}}},{"type":"object","title":"ContentPartVideo","required":["type","video_url"],"properties":{"type":{"type":"string","enum":["video_url"],"const":"video_url","title":"Type"},"video_url":{"type":"object","title":"VideoURL","required":["url"],"properties":{"url":{"type":"string","title":"Url","description":"A public video URL or a base64 data URI (data:video/mp4;base64,)."}}}}}]}},{"type":"null"}]},"reasoning_content":{"description":"Reasoning/thinking trace emitted by the model in responses when thinking mode is active (non-OpenAI extension).","title":"Reasoning Content","anyOf":[{"type":"string"},{"type":"null"}]},"tool_calls":{"description":"Tool calls generated by the model (present on assistant responses with finish_reason=tool_calls).","title":"Tool Calls","anyOf":[{"type":"array","items":{"type":"object","title":"ToolCall","required":["id","type","function"],"properties":{"id":{"type":"string","title":"Id"},"type":{"type":"string","enum":["function"],"const":"function","title":"Type"},"function":{"type":"object","title":"ToolCallFunction","required":["name","arguments"],"properties":{"name":{"type":"string","title":"Name"},"arguments":{"type":"string","title":"Arguments","description":"Function arguments as a JSON-encoded string."}}}}}},{"type":"null"}]},"tool_call_id":{"description":"For messages with role=tool, the id of the tool call this message responds to.","title":"Tool Call Id","anyOf":[{"type":"string"},{"type":"null"}]}},"required":["role","content"],"title":"Message","type":"object"},"title":"Messages","type":"array"},"temperature":{"default":1,"description":"The sampling temperature to use for text generation. The higher the temperature value is, the less deterministic the output text will be. It is not recommended to modify both temperature and top_p in the same call.","maximum":1,"minimum":0,"title":"Temperature","type":"number"},"top_p":{"default":0.95,"description":"The top-p sampling mass used for text generation. The top-p value determines the probability mass that is sampled at sampling time. For example, if top_p = 0.2, only the most likely tokens (summing to 0.2 cumulative probability) will be sampled. It is not recommended to modify both temperature and top_p in the same call.","maximum":1,"exclusiveMinimum":0,"title":"Top P","type":"number"},"max_tokens":{"default":8192,"description":"The maximum number of tokens to generate in any given call. Note that the model is not aware of this value, and generation will simply stop at the number of tokens specified.","maximum":16384,"minimum":1,"title":"Max Tokens","type":"integer"},"stream":{"default":false,"description":"If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events (SSE) as they become available (JSON responses are prefixed by `data: `), with the stream terminated by a `data: [DONE]` message.","title":"Stream","type":"boolean"},"seed":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Seed","description":"Optional seed for best-effort deterministic sampling."},"chat_template_kwargs":{"anyOf":[{"type":"object"},{"type":"null"}],"title":"Chat Template Kwargs","description":"Optional kwargs forwarded to the model chat template. For MiniMax-M3 this controls reasoning: {\"thinking_mode\": \"enabled\"} (think), {\"thinking_mode\": \"disabled\"} (no-think), or {\"thinking_mode\": \"adaptive\"}."},"tools":{"anyOf":[{"type":"array","items":{"type":"object","title":"ChatCompletionTool","required":["type","function"],"properties":{"type":{"type":"string","enum":["function"],"const":"function","title":"Type"},"function":{"type":"object","title":"ChatCompletionFunction","required":["name"],"properties":{"name":{"type":"string","title":"Name","description":"Function name."},"description":{"type":"string","title":"Description","description":"Function description."},"parameters":{"type":"object","title":"ChatCompletionFunctionParameters","description":"JSON Schema object describing the function arguments.","additionalProperties":true}}}}}},{"type":"null"}],"title":"Tools","description":"Optional OpenAI-compatible tool (function) definitions for tool calling."},"tool_choice":{"anyOf":[{"type":"string","enum":["none","auto","required"]},{"type":"object","title":"ChatCompletionNamedToolChoice","required":["type","function"],"properties":{"type":{"type":"string","enum":["function"],"const":"function","title":"Type"},"function":{"type":"object","required":["name"],"properties":{"name":{"type":"string","title":"Name"}}}}},{"type":"null"}],"title":"Tool Choice","description":"Controls tool selection: none, auto, required, or a named function."}},"required":["messages"],"title":"ChatRequest","type":"object"}}},"required":true},"responses":{"200":{"description":"Invocation is fulfilled","content":{"application/json":{"schema":{"properties":{"id":{"description":"A unique identifier for the completion.","format":"uuid","title":"Id","type":"string"},"choices":{"description":"The list of completion choices the model generated for the input prompt.","items":{"properties":{"index":{"description":"The index of the choice in the list of choices (always 0).","title":"Index","type":"integer"},"message":{"allOf":[{"additionalProperties":false,"properties":{"role":{"description":"The role of the message author.","enum":["system","user","assistant","tool"],"title":"Role","type":"string"},"content":{"description":"The contents of the message. A string, or (for multimodal user messages) a list of content parts: text plus image_url/video_url (each a public URL or a base64 data URI).","title":"Content","anyOf":[{"type":"string"},{"type":"array","items":{"oneOf":[{"type":"object","title":"ContentPartText","required":["type","text"],"properties":{"type":{"type":"string","enum":["text"],"const":"text","title":"Type"},"text":{"type":"string","title":"Text"}}},{"type":"object","title":"ContentPartImage","required":["type","image_url"],"properties":{"type":{"type":"string","enum":["image_url"],"const":"image_url","title":"Type"},"image_url":{"type":"object","title":"ImageURL","required":["url"],"properties":{"url":{"type":"string","title":"Url","description":"A public image URL or a base64 data URI (data:image/;base64,)."}}}}},{"type":"object","title":"ContentPartVideo","required":["type","video_url"],"properties":{"type":{"type":"string","enum":["video_url"],"const":"video_url","title":"Type"},"video_url":{"type":"object","title":"VideoURL","required":["url"],"properties":{"url":{"type":"string","title":"Url","description":"A public video URL or a base64 data URI (data:video/mp4;base64,)."}}}}}]}},{"type":"null"}]},"reasoning_content":{"description":"Reasoning/thinking trace emitted by the model in responses when thinking mode is active (non-OpenAI extension).","title":"Reasoning Content","anyOf":[{"type":"string"},{"type":"null"}]},"tool_calls":{"description":"Tool calls generated by the model (present on assistant responses with finish_reason=tool_calls).","title":"Tool Calls","anyOf":[{"type":"array","items":{"type":"object","title":"ToolCall","required":["id","type","function"],"properties":{"id":{"type":"string","title":"Id"},"type":{"type":"string","enum":["function"],"const":"function","title":"Type"},"function":{"type":"object","title":"ToolCallFunction","required":["name","arguments"],"properties":{"name":{"type":"string","title":"Name"},"arguments":{"type":"string","title":"Arguments","description":"Function arguments as a JSON-encoded string."}}}}}},{"type":"null"}]},"tool_call_id":{"description":"For messages with role=tool, the id of the tool call this message responds to.","title":"Tool Call Id","anyOf":[{"type":"string"},{"type":"null"}]}},"required":["role","content"],"title":"Message","type":"object"}],"description":"A chat completion message generated by the model.","examples":[{"content":"Ah, Paris, the City of Light! There are so many amazing things to see and do in this beautiful city ...","role":"assistant"}]},"finish_reason":{"anyOf":[{"enum":["stop","length","tool_calls"],"type":"string"},{"type":"null"}],"default":null,"description":"The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, or `length` if the maximum number of tokens specified in the request was reached.","examples":["stop"],"title":"Finish Reason"}},"required":["index","message"],"title":"Choice","type":"object"},"title":"Choices","type":"array"},"usage":{"allOf":[{"properties":{"completion_tokens":{"description":"Number of tokens in the generated completion.","examples":[25],"title":"Completion Tokens","type":"integer"},"prompt_tokens":{"description":"Number of tokens in the prompt.","examples":[9],"title":"Prompt Tokens","type":"integer"},"total_tokens":{"description":"Total number of tokens used in the request (prompt + completion).","examples":[34],"title":"Total Tokens","type":"integer"}},"required":["completion_tokens","prompt_tokens","total_tokens"],"title":"Usage","type":"object"}],"description":"Usage statistics for the completion request."}},"required":["id","choices","usage"],"title":"ChatCompletion","type":"object"}},"text/event-stream":{"schema":{"properties":{"id":{"description":"A unique identifier for the completion.","format":"uuid","title":"Id","type":"string"},"choices":{"description":"The list of completion choices the model generated for the input prompt.","items":{"properties":{"index":{"description":"The index of the choice in the list of choices (always 0).","title":"Index","type":"integer"},"delta":{"allOf":[{"additionalProperties":false,"properties":{"role":{"description":"The role of the message author.","enum":["system","user","assistant","tool"],"title":"Role","type":"string"},"content":{"description":"The contents of the message. A string, or (for multimodal user messages) a list of content parts: text plus image_url/video_url (each a public URL or a base64 data URI).","title":"Content","anyOf":[{"type":"string"},{"type":"array","items":{"oneOf":[{"type":"object","title":"ContentPartText","required":["type","text"],"properties":{"type":{"type":"string","enum":["text"],"const":"text","title":"Type"},"text":{"type":"string","title":"Text"}}},{"type":"object","title":"ContentPartImage","required":["type","image_url"],"properties":{"type":{"type":"string","enum":["image_url"],"const":"image_url","title":"Type"},"image_url":{"type":"object","title":"ImageURL","required":["url"],"properties":{"url":{"type":"string","title":"Url","description":"A public image URL or a base64 data URI (data:image/;base64,)."}}}}},{"type":"object","title":"ContentPartVideo","required":["type","video_url"],"properties":{"type":{"type":"string","enum":["video_url"],"const":"video_url","title":"Type"},"video_url":{"type":"object","title":"VideoURL","required":["url"],"properties":{"url":{"type":"string","title":"Url","description":"A public video URL or a base64 data URI (data:video/mp4;base64,)."}}}}}]}},{"type":"null"}]},"reasoning_content":{"description":"Reasoning/thinking trace emitted by the model in responses when thinking mode is active (non-OpenAI extension).","title":"Reasoning Content","anyOf":[{"type":"string"},{"type":"null"}]},"tool_calls":{"description":"Tool calls generated by the model (present on assistant responses with finish_reason=tool_calls).","title":"Tool Calls","anyOf":[{"type":"array","items":{"type":"object","title":"ToolCall","required":["id","type","function"],"properties":{"id":{"type":"string","title":"Id"},"type":{"type":"string","enum":["function"],"const":"function","title":"Type"},"function":{"type":"object","title":"ToolCallFunction","required":["name","arguments"],"properties":{"name":{"type":"string","title":"Name"},"arguments":{"type":"string","title":"Arguments","description":"Function arguments as a JSON-encoded string."}}}}}},{"type":"null"}]},"tool_call_id":{"description":"For messages with role=tool, the id of the tool call this message responds to.","title":"Tool Call Id","anyOf":[{"type":"string"},{"type":"null"}]}},"required":["role","content"],"title":"Message","type":"object"}],"description":"A chat completion delta generated by streamed model responses.","examples":[{"content":"Ah,","role":"assistant"}]},"finish_reason":{"anyOf":[{"enum":["stop","length","tool_calls"],"type":"string"},{"type":"null"}],"default":null,"description":"The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, or `length` if the maximum number of tokens specified in the request was reached. Will be `null` if the model has not finished generating.","title":"Finish Reason"}},"required":["index","delta"],"title":"ChoiceChunk","type":"object"},"title":"Choices","type":"array"}},"required":["id","choices"],"title":"ChatCompletionChunk","type":"object"}}}},"202":{"description":"Result is pending. 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