Local images
To query a vision model with a local image:from together import Together
import base64
client = Together()
getDescriptionPrompt = "what is in the image"
imagePath = "/home/Desktop/dog.jpeg"
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
base64_image = encode_image(imagePath)
stream = client.chat.completions.create(
model="moonshotai/Kimi-K3",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": getDescriptionPrompt},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
},
},
],
}
],
stream=True,
)
for chunk in stream:
print(
chunk.choices[0].delta.content or "" if chunk.choices else "",
end="",
flush=True,
)
import Together from "together-ai";
import fs from "fs/promises";
const together = new Together();
const getDescriptionPrompt = "what is in the image";
const imagePath = "./dog.jpeg";
async function main() {
const imageUrl = await fs.readFile(imagePath, { encoding: "base64" });
const stream = await together.chat.completions.create({
model: "moonshotai/Kimi-K3",
stream: true,
messages: [
{
role: "user",
content: [
{ type: "text", text: getDescriptionPrompt },
{
type: "image_url",
image_url: {
url: `data:image/jpeg;base64,${imageUrl}`,
},
},
],
},
],
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content || "");
}
}
main();
# Replace <BASE64_IMAGE> with your base64-encoded image data.
curl -X POST "https://api.together.ai/v1/chat/completions" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "moonshotai/Kimi-K3",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "what is in the image"
},
{
"type": "image_url",
"image_url": {
"url": "data:image/jpeg;base64,<BASE64_IMAGE>"
}
}
]
}
]
}'
Output
The image contains two dogs sitting close to each other
Video input
Video understanding (passing avideo_url content block to a chat completion) is supported on select VLMs that run only as a dedicated endpoint, for example Qwen/Qwen3-VL-8B-Instruct. Spin up a dedicated endpoint, then pass the endpoint name as model and a video_url block alongside text:
Python
from together import Together
client = Together()
response = client.chat.completions.create(
model="<ACCOUNT>/Qwen/Qwen3-VL-8B-Instruct-<ENDPOINT_HASH>", # your dedicated endpoint name
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What's happening in this video?"},
{
"type": "video_url",
"video_url": {
"url": "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4"
},
},
],
}
],
)
print(response.choices[0].message.content)
Multiple images
from together import Together
client = Together()
# Multi-modal message with multiple images
response = client.chat.completions.create(
model="moonshotai/Kimi-K3",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Compare these two images."},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
},
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/slack.png"
},
},
],
}
],
)
print(response.choices[0].message.content)
// Multi-modal message with multiple images
async function main() {
const response = await together.chat.completions.create({
model: "moonshotai/Kimi-K3",
messages: [
{
role: "user",
content: [
{ type: "text", text: "Compare these two images." },
{
type: "image_url",
image_url: {
url: "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png",
},
},
{
type: "image_url",
image_url: {
url: "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/slack.png",
},
},
],
},
],
});
process.stdout.write(response.choices[0]?.message?.content || "");
}
main();
curl -X POST "https://api.together.ai/v1/chat/completions" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "moonshotai/Kimi-K3",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Compare these two images."
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
}
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/slack.png"
}
}
]
}
]
}'
Sample model output
Sample model output
The first image is a collage of multiple identical landscape photos showing a natural scene with rocks, trees, and a stream under a blue sky. The second image is a screenshot of a mobile app interface, specifically the navigation menu of the Canva app, which includes icons for Home, DMs (Direct Messages), Activity, Later, Canvases, and More.
#### Comparison:
1. **Content**:
- The first image focuses on a natural landscape.
- The second image shows a digital interface from an app.
2. **Purpose**:
- The first image could be used for showcasing nature, design elements in graphic work, or as a background.
- The second image represents the functionality and layout of the Canva app's navigation system.
3. **Visual Style**:
- The first image has vibrant colors and realistic textures typical of outdoor photography.
- The second image uses flat design icons with a simple color palette suited for user interface design.
4. **Context**:
- The first image is likely intended for artistic or environmental contexts.
- The second image is relevant to digital design and app usability discussions.