> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vlm.run/llms.txt
> Use this file to discover all available pages before exploring further.
# Models
> VLM Run Gateway model catalog for OCR, VQA, and document inference
The VLM Run Gateway serves open-weight vision and document models through a single
OpenAI-compatible API. Each model declares the input types it accepts
(`text`, `image_url`, `document_url`) and the operations it
supports via the `method` field.
## Visual Question Answering (VQA)
VQA models accept text plus up to 64 images in the same message.
| Model | Status | Method | Accepted Inputs | Max images |
| --------------------------------------------------------------- | --------- | ------ | ------------------- | ---------- |
| [`qwen/qwen3.5-0.8b`](https://huggingface.co/Qwen/Qwen3.5-0.8B) | Available | — | `text`, `image_url` | 64 |
See [`method`](/gateway/methods#visual-qa) parameters for more details.
```python Python [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.vlm.run/v1/openai",
api_key="",
)
response = client.chat.completions.create(
model="qwen/qwen3.5-0.8b",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What is happening in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/sample.jpg"
},
},
],
}
],
)
print(response.choices[0].message.content)
```
```bash cURL [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
curl https://gateway.vlm.run/v1/openai/chat/completions \
-X POST \
-H "Authorization: Bearer $VLMRUN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3.5-0.8b",
"messages": [
{
"role": "user",
"content": [
{ "type": "text", "text": "What is happening in this image?" },
{
"type": "image_url",
"image_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/sample.jpg"
}
}
]
}
]
}'
```
## Document and Image OCR
These models accept a PDF via `document_url` or an image via `image_url`. The VLM Run Gateway
rasterizes each page and runs per-page inference, returning concatenated markdown (non-streaming)
or ordered page blocks (streaming). Use `method: "ocr"` for full OCR, or `method: "detect"` when
you only need text detection and bounding polygons. `zai-org/glm-ocr` and `rednote-hilab/dots.mocr`
produce reading-order markdown via `method: "markdown"`. For images, the Gateway runs inference on
the entire image. For consistency purposes, we do not stream on a token-by-token basis.
| Model | Status | Method | Accepted Inputs | Streaming |
| ------------------------------------------------------------------------------- | --------- | ---------- | --------------------------- | --------- |
| [`paddleocr/pp-ocrv6`](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_det) | Available | `ocr` | `image_url`, `document_url` | Page-wise |
| [`zai-org/glm-ocr`](https://huggingface.co/zai-org/GLM-OCR) | Available | `markdown` | `image_url`, `document_url` | Page-wise |
| [`rednote-hilab/dots.mocr`](https://huggingface.co/rednote-hilab/dots.mocr) | Available | `markdown` | `image_url`, `document_url` | Page-wise |
See [`method`](/gateway/methods#document-and-image-ocr) parameters for more details.
```python Python [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.vlm.run/v1/openai",
api_key="",
)
response = client.chat.completions.create(
model="paddleocr/pp-ocrv6",
messages=[
{
"role": "user",
"content": [
{
"type": "document_url",
"document_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.markdown/goog-10-k-2024.pdf"
},
},
],
}
],
extra_body={"method": "ocr", "document_dpi": 150},
)
print(response.choices[0].message.content)
```
```bash cURL [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
curl https://gateway.vlm.run/v1/openai/chat/completions \
-X POST \
-H "Authorization: Bearer $VLMRUN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "paddleocr/pp-ocrv6",
"method": "ocr",
"document_dpi": 150,
"messages": [
{
"role": "user",
"content": [
{
"type": "document_url",
"document_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.markdown/goog-10-k-2024.pdf"
}
}
]
}
]
}'
```
```python Python [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.vlm.run/v1/openai",
api_key="",
)
response = client.chat.completions.create(
model="zai-org/glm-ocr",
messages=[
{
"role": "user",
"content": [
{
"type": "document_url",
"document_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.markdown/goog-10-k-2024.pdf"
},
},
],
}
],
extra_body={"method": "markdown", "document_dpi": 150},
)
print(response.choices[0].message.content)
```
```bash cURL [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
curl https://gateway.vlm.run/v1/openai/chat/completions \
-X POST \
-H "Authorization: Bearer $VLMRUN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai-org/glm-ocr",
"method": "markdown",
"document_dpi": 150,
"messages": [
{
"role": "user",
"content": [
{
"type": "document_url",
"document_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.markdown/goog-10-k-2024.pdf"
}
}
]
}
]
}'
```
```python Python [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.vlm.run/v1/openai",
api_key="",
)
response = client.chat.completions.create(
model="rednote-hilab/dots.mocr",
messages=[
{
"role": "user",
"content": [
{
"type": "document_url",
"document_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.markdown/goog-10-k-2024.pdf"
},
},
],
}
],
extra_body={"method": "markdown", "document_dpi": 150},
)
print(response.choices[0].message.content)
```
```bash cURL [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
curl https://gateway.vlm.run/v1/openai/chat/completions \
-X POST \
-H "Authorization: Bearer $VLMRUN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "rednote-hilab/dots.mocr",
"method": "markdown",
"document_dpi": 150,
"messages": [
{
"role": "user",
"content": [
{
"type": "document_url",
"document_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.markdown/goog-10-k-2024.pdf"
}
}
]
}
]
}'
```
## Embeddings and Transcription
These models appear on [`GET /v1/openai/models`](/gateway/api-reference/get-models)
with a `task` field other than `chat`. They use separate OpenAI-compatible
endpoints, not chat completions. See
[Embeddings](/gateway/api-reference/post-embeddings) and
[Audio Transcriptions](/gateway/api-reference/post-audio-transcriptions) for
full request schemas.
| Model | Status | Task | Endpoint |
| ----------------------------------------------------------------------------------- | --------- | ------------ | -------------------------------------- |
| [`qwen/qwen3-vl-embedding-2b`](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) | Available | `embed` | `POST /v1/openai/embeddings` |
| [`nvidia/parakeet-tdt-0.6b-v3`](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) | Available | `transcribe` | `POST /v1/openai/audio/transcriptions` |
```python Python [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.vlm.run/v1/openai",
api_key="",
)
response = client.embeddings.create(
model="qwen/qwen3-vl-embedding-2b",
input="Extract a vector representation for this text.",
)
print(len(response.data[0].embedding))
```
```python Python [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}}
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.vlm.run/v1/openai",
api_key="",
)
with open("sample.mp3", "rb") as audio_file:
response = client.audio.transcriptions.create(
model="nvidia/parakeet-tdt-0.6b-v3",
file=audio_file,
)
print(response.text)
```
## Model Aliases
Many models accept multiple request IDs:
1. **Preferred:** lowercase `/` (listed on `/models`)
2. **Short:** slug only (e.g. `pp-ocrv6`)
3. **Hugging Face:** upstream repo id (e.g. [`PaddlePaddle/PP-OCRv6_medium_det`](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_det))
## Next steps
End-to-end recipe from model selection to response parsing.
Per-model `method` and `method_params` reference.
Full request parameters, streaming, and error handling.
HTTP status mapping, correlation ids, and support details.