runpod/models

7 notes, read out of this brain and free to use. Each one was extracted from a source and is re-checked against its exam.

Runpod language model generation example

Example of generating text with Runpod: `const { text } = await generateText({ model: runpod('qwen/qwen3-32b-awq'), prompt: 'What is the capital of Germany?' });` The response includes `text` (generated text string), `finishReason` (why generation stopped), and `usage` (token usage information).

Runpod language model streaming example

Example of streaming text with Runpod: `const { textStream } = await streamText({ model: runpod('qwen/qwen3-32b-awq'), prompt: 'Write a short poem about artificial intelligence in exactly 4 lines.', temperature: 0.7 }); for await (const delta of textStream) { process.stdout.write(delta); }`

Runpod language model capabilities

Runpod language models and their capabilities: | Model ID | Description | Streaming | Object Generation | Tool Usage | Reasoning Notes | | --- | --- | --- | --- | --- | --- | | `qwen/qwen3-32b-awq` | 32B parameter multilingual model with strong reasoning capabilities | Yes | No | Yes | Standard reasoning events | | `openai/gpt-oss-120b` | 120B parameter open-source GPT model | Yes | No | Yes | Standard reasoning events | An up-to-date list of all available models is available in the Runpod Public Endpoint Reference at https://docs.runpod.io/hub/public-endpoint-reference.

Runpod chat conversation example

Example of chat conversation with Runpod: `const { text } = await generateText({ model: runpod('qwen/qwen3-32b-awq'), messages: [{ role: 'system', content: 'You are a helpful assistant.' }, { role: 'user', content: 'What is the capital of France?' }] });`

Runpod tool calling example

Example of tool calling with Runpod: `const { text, toolCalls } = await generateText({ model: runpod('openai/gpt-oss-120b'), prompt: 'What is the weather like in San Francisco?', tools: { getWeather: tool({ description: 'Get weather information for a city', inputSchema: z.object({ city: z.string().describe('The city name') }), execute: async ({ city }) => { return \`The weather in ${city} is sunny.\`; } }) } });` The response includes `toolCalls` (array of tool calls made) and `toolResults` (results from executed tools).

Runpod image model generation example

Example of generating images with Runpod: `const { image } = await generateImage({ model: runpod.imageModel('qwen/qwen-image'), prompt: 'A serene mountain landscape at sunset', aspectRatio: '4:3' }); import { writeFileSync } from 'fs'; writeFileSync('landscape.jpg', image.uint8Array);` The response includes `image.uint8Array` (binary image data), `image.base64` (base64 encoded string), `image.mediaType` (MIME type), and `warnings` (array of warnings).

Runpod image model capabilities

Runpod image models and supported aspect ratios: | Model ID | Description | Supported Aspect Ratios | | --- | --- | --- | | `bytedance/seedream-3.0` | Advanced text-to-image model | 1:1, 4:3, 3:4 | | `bytedance/seedream-4.0` | Text-to-image (v4) | 1:1 (supports 1024, 2048, 4096) | | `bytedance/seedream-4.0-edit` | Image editing (v4, multi-image) | 1:1 (supports 1024, 1536, 2048, 4096) | | `black-forest-labs/flux-1-schnell` | Fast image generation (4 steps) | 1:1, 4:3, 3:4 | | `black-forest-labs/flux-1-dev` | High-quality image generation | 1:1, 4:3, 3:4 | | `black-forest-labs/flux-1-kontext-dev` | Context-aware image generation | 1:1, 4:3, 3:4 | | `qwen/qwen-image` | Text-to-image generation | 1:1, 4:3, 3:4 | | `qwen/qwen-image-edit` | Image editing (prompt-guided) | 1:1, 4:3, 3:4 | An up-to-date list of all available image models is in the Runpod Public Endpoint Reference at https://docs.runpod.io/hub/public-endpoint-reference.

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