Best Open Source Multimodal Vision Models in 2025

AI models are not just about LLMs and generating text. Multimodal vision models—which understand and generate images, videos, and even audio alongside text—are enabling new AI applications.

At their core, multimodal vision models combine:

• A vision encoder to extract visual images or video features
• A language model to process and generate text
• A fusion mechanism to connect these modalities in useful ways
• Sometimes a decoder to generate new text, images, or other structured data outputs.

There are several different types of multimodal vision models: vision-language models (VLMs) that generate text based on images, vision-reasoning models that answer complex questions based on images, and more.

Running multimodal vision models in production and at scale remains a challenge. Enter Serverless GPUs: a cost-effective, scalable way to deploy and fine-tune LLMs without managing complex infrastructure.

In this blog post, we'll take a look at the best multimodal vision models available today, including: Qwen 2.5 VL 72B Instruct, Pixtral, Phi 4 Multimodal, Deepseek Janus Pro, and more. After comparing their compatabilities and ideal use cases for real-world AI applications, we'll also share how to deploy them using serverless GPUs optimized for real-time inference and training.

Gemma 3

Gemma 3 was released recently and introduced multimodality, supporting vision-language input and text outputs. It's pre-training and post-training processes were optimized using a combination of distillation, reinforcement learning, and model merging. Gemma 3's development incorporates a blend of distillation, reinforcement learning (RL), and model merging, refining both efficiency and accuracy.

Gemma can use images and videos as inputs, allowing it to analyze images, answer questions about an image, compare images, identify objects, and even reply about text within an image. Originally trained on 896×896 pixel images, Gemma 3 now utilizes a dynamic segmentation algorithm that enables processing of higher-resolution and non-square images, improving flexibility across diverse visual inputs.

• Model Developer: Google DeepMind
• Available Parameter Sizes: 4B, 12B, and 27B
• Languages Supported: Over 140 Languages
• Input Context Window: 128K
• License: Open weights and permit responsible commercial use

Qwen 2.5 VL

Qwen 2.5 VL is part of Alibaba Cloud's Qwen family of large language models, specifically designed to handle both visual and textual data. This multimodal model integrates a vision transformer with a language model, enabling advanced image and text understanding capabilities.

The model excels at object recognition, scene interpretation, and multimodal reasoning, making it suitable for visual question answering (VQA), image captioning, and content moderation.

Optimized for efficiency and accuracy, Qwen 2.5 VL is designed to perform well across cloud-based inference to on-device applications.

• Model Developer: Qwen
• Available Parameter Sizes: 7B and 72B parameters
• Languages Supported: English, Chinese
• Input Context Window: 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize YaRN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
• License: Apache 2.0

Qwen 2.5 VL 72B Instruct

The 72 billion parameter version offers enhanced performance in tasks requiring complex visual and linguistic comprehension.

Pixtral

Pixtral is Mistral AI's innovative multimodal model, seamlessly integrating visual and textual data processing. The initial release, Pixtral 12B, combines a 12-billion-parameter multimodal decoder with a 400-million-parameter vision encoder, enabling it to handle interleaved image and text data effectively. Subsequently, Pixtral Large was introduced, featuring a 124-billion-parameter model that integrates a 1-billion-parameter visual encoder coupled with Mistral Large 2, enhancing its performance, especially for long contexts and function calls.

• Model Developer: Mistral AI
• Available Parameter Sizes: 12B (Pixtral 12B) and 124B (aka Pixtral Large)
• Languages Supported: Dozens of languages
• Input Context Window: 128,000 tokens
• License: Pixtral 12B - Apache 2.0, Pixtral Large - Mistral Research License

Phi-4 Multimodal

Microsoft's Phi-4 Multimodal model represents a significant advancement in AI, seamlessly integrating vision, audio, and text processing within a unified framework. Unlike traditional approaches that rely on separate models for different modalities, Phi-4 eliminates these silos, enabling more efficient, flexible, and context-aware AI applications.

Phi-4 handles visual, auditory, and textual inputs within a single architecture, reducing the complexity of multimodal AI pipelines. By processing multiple data types together, Phi-4 excels in contextual reasoning, improving performance in image captioning, speech-to-text interactions, and multimodal content generation.

Optimized for low-latency inference, Phi-4 is suitable for real-time applications across various domains.

• Model Developer: Microsoft
• Available Parameter Sizes: 5.6B
• Languages Supported: 24 languages
• Input Context Window: 128,000 tokens
• License: MIT

DeepSeek Janus Series

Janus is a series of open-source, multimodal AI models developed by DeepSeek, designed to process and integrate both visual and textual information.

The Janus models utilize a unified transformer architecture with a decoupled visual encoding pathway. This design enhances flexibility and performance in tasks requiring understanding and generating content across multiple modalities. Each model in the series is designed to handle a sequence length of up to 4,096 tokens, facilitating complex and context-rich interactions.

• Model Developer: DeepSeek
• Available Parameter Sizes: Janus-Pro: 7B and 1B, Janus: 1.3B, JanusFlow: 1.3B
• Languages Supported: English, Chinese
• Input Context Window: Not disclosed. Possibly 4,096 tokens
• License: MIT License

The Janus models utilize a unified transformer architecture with a decoupled visual encoding pathway, enhancing flexibility and performance in tasks that require understanding and generating content across multiple modalities. Each of these models have a sequence length of 4096.

DeepSeek Janus-Pro 7B

Released in late January, DeepSeek Janus-Pro is the most advanced and capable model in the Janus series, scaling up to 7 billion parameters. It brings major improvements in both multimodal understanding and visual generation, thanks to an optimized training strategy, a significantly expanded dataset, and a larger model architecture compared to its predecessors.

For multimodal understanding, Janus-Pro utilizes the SigLIP-L vision encoder, which supports 384 × 384 image input, enabling high-fidelity visual processing. This allows the model to excel in image comprehension, reasoning, and cross-modal tasks such as visual question answering, caption generation, and scene interpretation.

Janus-Pro is built upon the DeepSeek-LLM-1.5B-base and DeepSeek-LLM-7B-base models, inheriting their strong textual reasoning capabilities while integrating high-quality vision encoding.

Llama 3.2

Meta’s Llama 3.2 represents a major leap in open-source AI, introducing multimodal capabilities that seamlessly integrate text and visual data processing. These models are designed to handle complex reasoning across multiple modalities, unlocking new possibilities for AI-driven augmented reality, visual search, document analysis, and content generation.

With a focus on efficiency and scalability, Llama 3.2 Vision models are optimized for a wide range of hardware, from high-performance GPUs to mobile devices, ensuring accessibility across different platforms. This versatility enables developers to build real-time AI applications, including intelligent assistants, creative design tools, and automated image understanding systems, without the constraints of proprietary models.

• Model Developer: Meta
• Available Parameter Sizes: 11B and 90B
• Languages Supported: 8 languages - English, German, Italian, Portuguese, Hindi, Spanish, and Thai
• Input Context Window: 128,000 tokens
• License: Allows for research and commercial use with specific terms and condition Llama 3.2 Community License

Fine-Tuning and Deploying Open Source Multimodal Vision Models with Serverless GPUs

Open source multimodal vision models like Gemma 3 and Qwen 2.5 VL provide powerful alternatives to proprietary options, offering flexibility and cost-effectiveness.

With Koyeb’s serverless GPUs, you can fine-tune and deploy these models with a single click. Get a dedicated inference endpoint running on high performance GPUs without managing any infrastructure.

• Sign up for Koyeb to get started deploying serverless inference endpoints today
• Deploy vLLM, Ollama, and other open-source models
• Read our documentation
• Explore the one-click deploy catalog

Curious about the best open source LLMs? Read our dedicated blog post to discover which model you'll want to use for your next AI application.