Gemini 3 is Now Available as an OCR Model in Tensorlake

Source: Dev.to

Google’s Gemini model has been great at document parsing since the 2.5 Flash release. The latest Gemini 3 pushes the envelope further, achieving the lowest edit distance (0.115) on OmniDocBench compared to GPT‑5.1 (0.147) and Claude Sonnet 4.5.

Starting today, you can use Gemini 3 as an OCR engine with Tensorlake’s Document Ingestion API. Ingest documents in bulk, convert them to Markdown, classify pages, or extract structured data using a JSON schema. Tensorlake handles queuing, rate‑limit management, and webhooks for processed documents.

We put Gemini 3 to the test inside Tensorlake, and the results on “hostile” document layouts were immediate.

Case Study 1: Table Structure Recognition

Document: Google 2024 Environmental Report

Financial and scientific reports often use visual cues—indentation, floating columns, symbols—to convey meaning. We fed the complex “Water Use” table from the appendix into Gemini 3.

The Challenge

The table is semi‑wireless: some rows have separating lines, columns lack clear boundaries, and the right‑most column is disconnected from the main block.

Gemini 3 Result: Visual Understanding

Gemini 3 perfectly understood the table. Below is a screenshot from the Tensorlake Cloud Dashboard.

Case Study 2: VQA + Structured Output

Document: House Floor Plans

We tested whether Gemini 3 could parse visual symbols on construction documents by integrating it into Tensorlake’s Structured Extraction pipeline.

The Input

A raw PDF of a house plan and a Pydantic schema defining the required fields, e.g.:

class KitchenOutlets(BaseModel):
    kitchen_outlets: int  # Number of standard and GFI electrical outlets, as noted by the legend icon labeled "outlet"

The kitchen + dining nook area:

The circle with two lines represents outlets, as shown in the legend:

The Challenge

There is no textual label “Outlet” on the diagram; the model must identify the circle‑and‑line icon defined in the legend, constrain its search to the kitchen area, and aggregate the count into the JSON structure.

The Result

Gemini 3 correctly interpreted the visual diagram and returned a valid JSON object with 6 outlets, distinguishing them from nearby data ports and switches.

Tensorlake blends specialized OCR models and vision‑language models into convenient APIs. While you could call the Gemini API directly, you would need to rebuild many production‑ready components. Gemini 3 is now fully integrated with Tensorlake DocAI APIs for reading, classifying, and extracting information from documents.

Tensorlake solves the two biggest headaches of building Document Ingestion APIs with VLMs

• Bulk Ingestion & Rate Limits – Gemini 3 can struggle with spiky traffic; sending 10 000 documents at once may trigger quota errors. Tensorlake manages the queue, handles back‑off and retries automatically, and prevents 429 errors.
• Chunking Large Files – Tensorlake automatically splits large documents into 25‑page chunks, ensuring Gemini stays within its 64 k token output limit.

When to use (and NOT use) Gemini 3

Use Gemini 3 when:

• Complex visual reasoning is required – e.g., correlating a chart’s color legend to a data table or counting symbols on a blueprint.

Do NOT use Gemini 3 when:

• Bounding boxes are needed for citation – Gemini 3 does not perform layout detection of objects.
• Strict text‑style or font detection is required – visual nuances like strikethroughs, underlines, or specific font colors are ignored.

For those tasks, consider Tensorlake’s specialized models such as Model03.

How to use Gemini 3 with Tensorlake

Playground

Gemini 3 is available today in the Tensorlake Playground for experimentation:

HTTP API / SDK

from tensorlake.documentai import DocumentAI, ParsingOptions

client = DocumentAI()

parse_id = client.read(
    file_url="https://tlake.link/docs/real-estate-agreement",
    parsing_options=ParsingOptions(
        ocr_model="gemini3"
    )
)

result = client.result(parse_id)

What’s Next

Document ingestion presents many edge cases. We aim to keep users equipped with state‑of‑the‑art models, allowing them to solve use cases quickly by adjusting OCR pipeline components with minimal effort.