Send images, audio, and video - Mistral Rs

import { Tabs, TabItem } from '@astrojs/starlight/components';

Multimodal models accept the OpenAI content-part message format: content is a list of typed parts instead of a string. The heavily tested families are Qwen3-VL (image, video) and Gemma 4 (image, audio, video); per-model modality support is in the supported models reference.

bash

mistralrs run -m Qwen/Qwen3-VL-4B-Instruct --image photo.jpg -i "What is this?"

--image, --audio, and --video each accept multiple values and require -i. Interactive mode also auto-detects file paths in prompts:

text

> Describe this: /path/to/photo.jpg

</TabItem> <TabItem label="HTTP">

bash

curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "default",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "file:///path/to/photo.jpg"}},
        {"type": "text", "text": "Describe this image."}
      ]
    }]
  }'

Full example.

</TabItem> <TabItem label="Python">

python

from mistralrs import Runner, Which, ChatCompletionRequest

runner = Runner(
    which=Which.MultimodalPlain(model_id="Qwen/Qwen3-VL-4B-Instruct"),
    in_situ_quant="4",
)

response = runner.send_chat_completion_request(
    ChatCompletionRequest(
        model="default",
        messages=[{
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": "file:///path/to/photo.jpg"}},
                {"type": "text", "text": "What do you see in this image?"},
            ],
        }],
        max_tokens=256,
    )
)
print(response.choices[0].message.content)

Full example.

</TabItem> <TabItem label="Rust">

rust

use mistralrs::{ModelBuilder, MultimodalMessages, TextMessageRole};

let model = ModelBuilder::new("Qwen/Qwen3-VL-4B-Instruct").build().await?;

let image = image::open("photo.jpg")?;
let messages = MultimodalMessages::new()
    .add_image_message(TextMessageRole::User, "What is this?", vec![image]);

let response = model.send_chat_request(messages).await?;

add_audio_message and add_video_message follow the same shape; add_multimodal_message mixes all three. Full example.

</TabItem> </Tabs>

Content parts and URL forms

Three part types carry media: image_url, audio_url, and video_url, each wrapping a {"url": ...} object. URLs accept three forms:

file:///absolute/path: local files the server process can read.
http(s)://...: fetched over the network at request time.
data:<mime>;base64,...: inline base64.

A message can contain any number of parts in any combination the model supports; the model sees them in order:

json

{
  "role": "user",
  "content": [
    {"type": "image_url", "image_url": {"url": "file:///before.jpg"}},
    {"type": "image_url", "image_url": {"url": "file:///after.jpg"}},
    {"type": "text", "text": "What changed between these images?"}
  ]
}

Video

Send video with --video on the CLI or a video_url part over HTTP/SDKs:

json

{
  "role": "user",
  "content": [
    {"type": "video_url", "video_url": {"url": "file:///absolute/path/clip.mp4"}},
    {"type": "text", "text": "What happens in this video?"}
  ]
}

Non-GIF formats require FFmpeg on the server; install steps and troubleshooting are in Set up video input.

The engine decodes the file into sampled frames and feeds them through the model's vision path. Per-request frame-sampling controls are not currently exposed.

Both Qwen3-VL and Gemma 4 accept video; see the supported models reference for per-model modality support. Full example.

Audio

Audio support is model-specific: Gemma 4, Gemma 3n, Phi 4 Multimodal, MiniCPM-O, and Voxtral accept audio_url parts (Voxtral is the dedicated audio-understanding model; see speech models).

json

{
  "role": "user",
  "content": [
    {"type": "audio_url", "audio_url": {"url": "file:///clip.wav"}},
    {"type": "text", "text": "Transcribe this."}
  ]
}

WAV, MP3, FLAC, and OGG decode natively. Convert other formats with FFmpeg first. Full example.

In-memory images from Python

For bytes or PIL images, encode as base64 and pass a data URL; the engine handles decoding and preprocessing:

python

import base64
from io import BytesIO
from PIL import Image

img = Image.open("photo.jpg")
buf = BytesIO()
img.save(buf, format="PNG")
b64 = base64.b64encode(buf.getvalue()).decode("ascii")

messages = [{
    "role": "user",
    "content": [
        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}},
        {"type": "text", "text": "Describe this image."},
    ],
}]

Mixing modalities in one request

Any combination the model supports works in a single message; order matters:

python

messages = [{
    "role": "user",
    "content": [
        {"type": "image_url", "image_url": {"url": "file:///chart.png"}},
        {"type": "audio_url", "audio_url": {"url": "file:///commentary.wav"}},
        {"type": "text", "text": "Does the commentary match what the chart shows?"},
    ],
}]

Which modalities a given model accepts is listed in the supported models reference.

Preprocessing

Vision encoders have fixed input resolutions, so each modality is normalized before reaching the model:

Images are resized to the model's input resolution, preserving aspect ratio (large images are downsized).
Video uses the decoded frames.
Audio is resampled to the model's expected rate.

Per-request preprocessing overrides are not exposed. Load-time image bounds are set at launch with --max-num-images (default 1), --max-edge, and --max-image-length (default 1024).