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	Available	—	text, image_url	64

See method parameters for more details.

qwen/qwen3.5-0.8b

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_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)

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	Available	ocr	image_url, document_url	Page-wise
zai-org/glm-ocr	Available	markdown	image_url, document_url	Page-wise
rednote-hilab/dots.mocr	Available	markdown	image_url, document_url	Page-wise

See method parameters for more details.

paddleocr/pp-ocrv6

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_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)

zai-org/glm-ocr

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_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)

rednote-hilab/dots.mocr

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_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)

Embeddings and Transcription

These models appear on GET /v1/openai/models with a task field other than chat. They use separate OpenAI-compatible endpoints, not chat completions. See Embeddings and Audio Transcriptions for full request schemas.

Model	Status	Task	Endpoint
qwen/qwen3-vl-embedding-2b	Available	embed	POST /v1/openai/embeddings
nvidia/parakeet-tdt-0.6b-v3	Available	transcribe	POST /v1/openai/audio/transcriptions

qwen/qwen3-vl-embedding-2b

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_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))

nvidia/parakeet-tdt-0.6b-v3

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_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 <org>/<slug> (listed on /models)
2. Short: slug only (e.g. pp-ocrv6)
3. Hugging Face: upstream repo id (e.g. PaddlePaddle/PP-OCRv6_medium_det)

Next steps

Flexible Document OCR

End-to-end recipe from model selection to response parsing.

Methods

Per-model method and method_params reference.

Chat Completions API

Full request parameters, streaming, and error handling.

Error Codes

HTTP status mapping, correlation ids, and support details.