Vision Models Compared

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What this article covers

• The most important local vision models side by side.
• LLaVA, BakLLaVA, MiniCPM-V, and Moondream in detail.
• Which model fits which image task.
• Hardware requirements and speed.
• Real-world examples for image analysis, OCR, and document processing.

Introduction: Understanding vision models

Vision models understand images. They describe what’s in a photo, extract text via OCR, analyze diagrams, and answer questions about images. Combined with language models, they enable multimodal AI: text plus image.

This article is for users who want to analyze images with local AI. For foundations, see Image Analysis and Ollama Vision.

Why do you need vision models?

Imagine you want to describe a photo, extract text from a screenshot, or analyze a diagram. Vision models can handle all of this locally, keeping images off the cloud.

Vision models explained briefly

Vision models connect an image encoder to a language model. They see an image and generate text about it. LLaVA is the most well-known, MiniCPM-V is the strongest for its size, and Moondream is the tiniest for edge devices.

The core concept is simple: image in, text out.

Who is this article for?

• Developers adding image understanding to applications.
• Analysts working with diagrams and screenshots.
• Document processors needing OCR plus reasoning.
• Privacy-conscious teams analyzing images locally.

Key terms

• Vision model - Image plus text. Useful for image understanding.
• Multimodal - Multiple modalities (text and image). Useful as a concept.
• OCR - Text extraction from images. Useful for documents.
• VLM - Vision Language Model. The technical term.
• Ollama - Model server. Useful for running models.
• Encoder - Image to vector. Useful for understanding how it works.

Models at a glance

Detailed comparison

1. LLaVA (Large Language and Vision Assistant)

Strengths:

• Proven track record, well-documented
• Available in multiple sizes (7B-34B)
• Good at descriptions and visual questions
• LLaVA-NeXT offers improved performance

Weaknesses:

• Not the latest architecture
• German support is decent but not exceptional

Recommendation: Use llava:13b for solid balance.

2. MiniCPM-V 2.6 (OpenBMB)

Strengths:

• Best quality at 8B scale
• Excellent OCR and document understanding
• Handles multiple images simultaneously
• Strong on diagrams and tables

Weaknesses:

• Less widely known
• Fewer community fine-tunes

Recommendation: Use minicpm-v:8b for best efficiency.

3. BakLLaVA

Strengths:

• Mistral foundation ensures good German support
• Fast for the quality it delivers
• Solid general-purpose image analysis

Weaknesses:

• Only 7B available
• Not as capable as MiniCPM-V

Recommendation: Use bakllava:7b for German language image work.

4. Moondream

Strengths:

• Tiny footprint (1.8B, ~2GB VRAM)
• Fast on edge hardware and IoT
• Good for simple image descriptions

Weaknesses:

• Limited reasoning capabilities
• No complex analysis

Recommendation: Use moondream:1.8b for edge devices and quick tests.

5. Qwen2-VL

Strengths:

• Excellent multilingual support
• Strong on Chinese and Japanese images
• High-quality OCR
• Video understanding (Qwen2.5-VL)

Weaknesses:

• Larger models require significant VRAM
• Alibaba-based

Recommendation: Use qwen2-vl:7b for multilingual image work.

Task comparison

Practical example: Describing an image

import requests
import base64

# Load image
with open("foto.jpg", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

# Ollama Vision API
response = requests.post("http://ollama:11434/api/chat", json={
    "model": "minicpm-v:8b",
    "messages": [
        {
            "role": "user",
            "content": "Describe this image in detail.",
            "images": [image_b64]
        }
    ],
    "stream": False
})

print(response.json()["message"]["content"])

Practical example: OCR from a screenshot

# Analyze screenshot
response = requests.post("http://ollama:11434/api/chat", json={
    "model": "minicpm-v:8b",
    "messages": [
        {
            "role": "user",
            "content": "Extract all text from this screenshot. Return the text in a structured format.",
            "images": [screenshot_b64]
        }
    ],
    "stream": False
})

Practical example: Analyzing a diagram

# Understand diagram
response = requests.post("http://ollama:11434/api/chat", json={
    "model": "minicpm-v:8b",
    "messages": [
        {
            "role": "user",
            "content": "Analyze this diagram. What does it show? What trends do you see?",
            "images": [diagram_b64]
        }
    ],
    "stream": False
})

Security considerations

• Images can contain sensitive data: Screenshots, documents, faces. Local processing protects your privacy. See Data Protection.
• OCR errors: Text extraction can be imperfect. Always validate results.
• Prompt injection: Images can contain hidden instructions. See Prompt Injection.

Common pitfalls

• Model too large: Running a 34B vision model on 8GB RAM will crash. Choose MiniCPM-V or Moondream instead.
• Wrong expectations: Vision models describe images, they don’t create them. For image generation, use Stable Diffusion.
• Weak OCR: Not all vision models excel at OCR. MiniCPM-V is specialized for it.
• Single image only: Not all models handle multiple images at once. MiniCPM-V does.
• Language support: German quality varies. Qwen2-VL and BakLLaVA perform well.

Further reading

• Image Analysis - Analyzing images with AI.
• Document Analysis - Understanding documents.
• Ollama Vision - Vision in Ollama.
• Ollama Vision API - API reference.
• Language Models - For text-only tasks.
• Multimodal AI - Overview.

Key takeaways:

• Vision models understand images: descriptions, OCR, analysis.
• minicpm-v:8b = best efficiency for local image analysis.
• llava:13b = proven, well-documented.
• moondream:1.8b = for edge devices and quick testing.
• qwen2-vl = for multilingual images.
• For OCR and documents: MiniCPM-V is specialized.

FAQ

Sources and further reading

• LLaVA - LLaVA project.
• MiniCPM-V - OpenBMB model.
• Moondream - Moondream.
• Qwen-VL - Alibaba Vision.
• Ollama - Model server.

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