About

🔑 model key: hive/vision-language-model

The Hive Vision Language Model is trained on Hive’s proprietary data, delivering leading performance with the speed and flexibility required for production vision tasks.

Best-in-class moderation – Flags sexual content, hate, drugs, and other moderation classes—even in nuanced edge cases across text and images.
Deep multimodal comprehension – Detects fine-grained objects, reads text, and understands spatial and semantic relationships to provide a rich understanding of input images.
All-in-one task engine – Generate captions, answer visual questions, run OCR, or analyze image characteristics—all through a single endpoint.

How to Get Started

Authentication is required to use these models. You’ll need an API Key, which can be created in the left sidebar.

Follow these steps to generate your key:

Click ‘Playground API Keys’ in the sidebar.
Click ‘Create API Key’ to create a new key scoped to your organization. The same key can be used with any "Playground available" model.

⚠️ Important: Keep your API Key safe!

Click '+' to create a new API Key — Click 'Playground API Keys' in the left menu to get started.

Once you've created an API Key, you can submit API requests using the **Secret Key.**
Please keep your Secret Key safe.

Querying Hive Vision Language Model

Hive offers an OpenAI-compatible Rest API for querying LLMs and multimodal LLMs. Here are the ways to call it:

Using the OpenAI SDK
Directly invoking the REST API

Using this API, the model will successively generate new tokens until either the maximum number of output tokens has been reached or if the model’s end-of-sequence (EOS) token has been generated.

Note: Some fields such as top_k are supported via the REST API, but is not supported by the OpenAI SDK.

Currently, the VLM only supports synchronous task submission. We plan to add asynchronous support in the future.

Performance Tips

🕑
Minimizing Latency
Hive VLM tokenizes each image into square "patches."
Images small enough to fit into a single patch (square images ≤ 633x633) cost only 256 input tokens
Larger images are split into up to 6 patches (max 1,536 tokens) which increases latency and price.
For the fastest response, resize or center-crop your images to ≤ 633x633 before calling the API.

🔠
Maximizing OCR Accuracy
For OCR tasks, it is recommended to keep your image patch size higher.
The higher the patch size (up to 6), the more accurate the VLM performs on text-on-image tasks.

👍
For help achieving your specific use case, feel free to reach out to us!

Prompt + Image URL (quick start!)

from openai import OpenAI
client = OpenAI(base_url="https://api.thehive.ai/api/v3/", api_key="<SECRET_KEY>")

completion = client.chat.completions.create(
    model="hive/vision-language-model",
    messages=[
        {"role":"user","content":[
            {"type":"text","text":"Describe the scene in one sentence."},
            {"type":"image_url","image_url":{"url":"https://d24edro6ichpbm.thehive.ai/example-images/vlm-example-image.jpeg"}}
        ]}
    ],
    max_tokens=50,
)
print(completion.choices[0].message.content)

import OpenAI from "openai";
const openai = new OpenAI({ baseURL:"https://api.thehive.ai/api/v3/", apiKey:"<SECRET_KEY>" });

const completion = await openai.chat.completions.create({
  model: "hive/vision-language-model",
  messages: [
    { role: "user", content: [
        { type: "text", text: "Describe the scene in one sentence." },
        { type: "image_url", image_url: { url: "https://d24edro6ichpbm.thehive.ai/example-images/vlm-example-image.jpeg" } }
    ] }
  ],
  max_tokens: 50,
});
console.log(completion.choices[0].message.content);

curl -X POST https://api.thehive.ai/api/v3/chat/completions \
  -H 'Authorization: Bearer <SECRET_KEY>' \
  -H 'Content-Type: application/json' \
  -d '{
    "model":"hive/vision-language-model",
    "max_tokens":50,
    "messages":[
      {"role":"user","content":[
        {"type":"text","text":"Describe the scene in one sentence."},
        {"type":"image_url","image_url":{"url":"https://d24edro6ichpbm.thehive.ai/example-images/vlm-example-image.jpeg"}}
      ]}
    ]
  }'

Prompt + Image Base64

Just swap the value inside "url" to be your encoded base64 string; everything else is identical.

{"type":"image_url","image_url":{"url":"data:image/jpeg;base64,<BASE64_DATA>"}}

Prompt + Image URL with JSON Schema

Use this when you need the model to return structured JSON that conforms to a schema you predefine.

from enum import Enum
from pydantic import BaseModel
from openai import OpenAI

class Subject(str, Enum): person="person"; animal="animal"; vehicle="vehicle"; food="food"; scenery="scenery"
class ClassificationOutput(BaseModel): subject: Subject

client = OpenAI(base_url="https://api.thehive.ai/api/v3/", api_key="<SECRET_KEY>")
completion = client.beta.chat.completions.parse(
    model="hive/vision-language-model",
    messages=[
        {"role":"user","content":[
            {"type":"text","text":"Classify the main subject (person, animal, vehicle, food, scenery). Return JSON only."},
            {"type":"image_url","image_url":{"url":"https://d24edro6ichpbm.thehive.ai/example-images/vlm-example-image.jpeg"}}
        ]}
    ],
    response_format=ClassificationOutput,
    max_tokens=50,
)
print(completion.choices[0].message.parsed.subject)

const schema = {
  type:"object",
  properties:{ subject:{ type:"string", enum:["person","animal","vehicle","food","scenery"] } },
  required:["subject"], additionalProperties:false,
};

const completion = await openai.chat.completions.create({
  model:"hive/vision-language-model",
  messages:[{ role:"user", content:[
    { type:"text", text:"Classify the main subject (person, animal, vehicle, food, scenery). Return JSON only." },
    { type:"image_url", image_url:{ url:"https://d24edro6ichpbm.thehive.ai/example-images/vlm-example-image.jpeg" } }
  ]}],
  response_format:{ type:"json_schema", schema },
  max_tokens:50,
});

console.log(JSON.parse(completion.choices[0].message.content).subject);

curl -X POST https://api.thehive.ai/api/v3/chat/completions \
  -H 'Authorization: Bearer <SECRET_KEY>' \
  -H 'Content-Type: application/json' \
  -d '{
    "model":"hive/vision-language-model",
    "max_tokens":50,
    "response_format":{
      "type":"json_schema",
      "json_schema":{
        "schema":{
          "type":"object",
          "properties":{"subject":{"type":"string","enum":["person","animal","vehicle","food","scenery"]}},
          "required":["subject"],
          "additionalProperties":false
        },
        "strict":true
      }
    },
    "messages":[
      {"role":"user","content":[
        {"type":"text","text":"Classify the main subject (person, animal, vehicle, food, scenery). Return JSON only."},
        {"type":"image_url","image_url":{"url":"https://d24edro6ichpbm.thehive.ai/example-images/vlm-example-image.jpeg"}}
      ]}
    ]
  }'

Response Example

After making a request, you’ll receive a JSON response with the model's output text. Here’s a sample output:

{
  "id": "1234567890-abcdefg",
  "object": "chat.completion",
  "model": "hive/vision-language-model",
  "created": 1749840139221,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "{ \"subject\": \"scenery\" }"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 1818,
    "completion_tokens": 11,
    "total_tokens": 1829
  }
}

Note: If you provide a JSON schema, the API still returns a string, which can be parsed into JSON.

Parameter Reference

Input

Field	Type	Definition
messages	array of objects	Conversation history. For VLM calls each user message typically contains: • text prompt • one image_url object (url = http URL or base-64 data URI).
model	string	Required. The name of the model to call.
role	string	The role of the participant in the conversation. Must be system, user, or assistant.
content	string OR array of objects	Your content string. If array, each object must have a `type` and corresponding data, as shown in the examples above.
text	string	Referenced inside content arrays, containing the text message to be sent.
image_url	object	Contains the image URL or Base64-encoded string, inside the subfield url.
response_format	object	`response_format` constrains the model response to follow the JSON Schema you define. This setting is very useful if you'd like Hive VLM to follow a specified set of tasks, defined in a JSON. Note: this setting may increase latency.
max_tokens	int	Output token cap. Default 512 (1 – 2048).
temperature	float	Controls randomness (0 = deterministic). Default 0 (0 - 1).
top_p	float	Nucleus sampling cutoff. Default 0.1 (0-1).
top_k	int	Limits sampling to top K tokens. Default 1 (0-1).

Output

Field	Type	Definition
id	string	The Task ID of the submitted task.
model	string	The name of the model used.
created	int	The timestamp (in epoch milliseconds) when the task was created.
choices	array of objects	Contains the model’s responses. Each object includes the index, message, and finish_reason.
usage	object	Token counts for prompt, completion, and total.

Those are the only fields you need for most integrations. Everything else remains identical across URL, Base64, and schema variants.

Common Errors

The VLM has a default starting rate limit of 1 request per second. You may see this error below if you submit higher than the rate limit.

To request a higher rate limit please contact us!

{
  "status_code": 429,
  "message": "Too Many Requests"
}

A positive Organization Credit balance is required to continue using Hive Models. Once you run out of credits requests will fail with the following error.

{
  "status_code":405,
  "message":"Your Organization is currently paused. Please check your account balance, our terms and conditions, or contact [email protected] for more information."
}

Pricing

Hive VLM is priced per input and output token. Because latency also scales with tokens, smaller images → fewer patches → faster responses.

For image tokenization, the following logic is used:

Start with the image’s aspect ratio and area.
1. Example: a 1,024 × 1,024 picture has ratio = 1.0 and area = 1M px.
Consider every logical way to slice the picture into tiles.
1. A “tile” is a square crop that the model turns into a fixed 256-token chunk.
2. We allow from 1 tile up to 6 tiles in total.
  So the grids considered are 1 × 1, 1 × 2, 2 × 1, 2 × 2, 1 × 3, 3 × 1 …
Rank those grids by two rules (in order):
1. Match the image's original aspect ratio.
  e.g., a 3 × 2 grid (ratio = 1.5) fits an 800 × 600 image (ratio ≈ 1.33) better than a 2 × 2 grid (ratio = 1.0).
2. If two grids tie on ratio, pick the one that uses more tiles.
Count how many tiles the chosen grid has, and multiply by 256 tokens per tile.
Finally, add 260 more tokens to images with greater than 1 patch.

Image resolution	Chosen grid	Tiles	Tokens
633x633, or smaller squares (fastest latency)	1x1	1	Only 260 tokens
1024x1024	2x2	4	(4x256) + 260 = 1284 tokens
800x600	3x2	6	(6x256) + 260 = 1796 tokens

Hive Vision Language Model (VLM)

About

How to Get Started

Querying Hive Vision Language Model

Performance Tips

🕑
Minimizing Latency

🔠
Maximizing OCR Accuracy

👍
For help achieving your specific use case, feel free to reach out to us!

Prompt + Image URL (quick start!)

Prompt + Image Base64

Prompt + Image URL with JSON Schema

Response Example

Parameter Reference

Input

Output

Common Errors

Pricing

About

How to Get Started

Querying Hive Vision Language Model

Performance Tips

🕑Minimizing Latency

🔠Maximizing OCR Accuracy

👍For help achieving your specific use case, feel free to reach out to us!

Prompt + Image URL (quick start!)

Prompt + Image Base64

Prompt + Image URL with JSON Schema

Response Example

Parameter Reference

Input

Output

Common Errors

Pricing

🕑
Minimizing Latency

🔠
Maximizing OCR Accuracy

👍
For help achieving your specific use case, feel free to reach out to us!