Microsoft

Florence-2

About

Florence-2 is a 232M open-weight model from Microsoft. Vision foundation model for captioning, OCR, detection, and region tasks. It accepts text and images. License is MIT. Released June 15, 2024.

Parameters
232M
Published parameter count.
License
MIT
License on the weights.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$0.10
Cached input / 1M	$0.03
Output / 1M	$0.30

Compare

Florence-2 is one of the catalog's OCR models. The table is each model's published price, size, and context.

Model	What it does	Price	Parameters	Context
Florence-2	Vision foundation model for captioning, OCR, detection, and region tasks.	$0.10 / 1M input	232M	—
PP-OCRv6	PaddleOCR PP-OCRv6 medium text detection and recognition; scene OCR JSONL on image chat; text returns plain text; document_url fan-out via text.	$0.01 / 1M input	20M	—
DeepSeek-OCR-2	DeepSeek OCR 2 and markdown extraction.	$0.25 / 1M input	3.4B	32K
dots.mocr	Multilingual document layout parsing and markdown OCR.	$0.20 / 1M input	3B	32K

Benchmarks

Published scores for Florence-2, from the Florence-2 model card.

Evals

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	COCO caption CIDEr: 140.
Cost and performance	Lower listed rate, mid-pack latency

Examples

Dense region captions
Florence-2 names every region in a vegetables-on-counter shot.
{"regions":[{"label":"a tomato","box_xyxy":[58,140,224,396]}, ...]}
Dashboard caption
`more_detailed_caption` describes a shadcn dashboard screenshot.
{"caption":"A modern analytics dashboard with KPI cards, ..."}

Methods

Methods this model serves. Payload shapes are in the docs.

Method	Returns
caption	A short caption
detailed_caption	A longer caption
more_detailed_caption	A detailed caption
ocr	Lines of text
ocr_with_region	Text with regions
od	Boxes around objects
dense_region_caption	A caption for each region
region_proposal	Region boxes

Estimate cost

Input tokensOutput tokens

$0.40

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

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="microsoft/florence-2-base-ft",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

print(response.choices[0].message.content)