Multimodal LLMs: Visual Understanding and Cross-Modal Generation
Introduction
This comprehensive guide explores the best Multimodal LLMs for 2026. We cover core concepts, technical fundamentals, and detailed analysis of multiple top tools, helping you choose the most suitable solution. The guide includes feature comparisons, use cases, selection criteria, and frequently asked questions to provide complete decision-making support.
What Are Multimodal LLMs
Multimodal LLMs (LLM for Multimodal) are large language models that process not only text but also integrate images, audio, or video, supporting cross-modal tasks such as image description or video understanding. These models are typically trained on multimodal data, capable of understanding relationships between different modalities, supporting cross-modal tasks such as visual question answering, multimedia content generation, and augmented reality applications. Leading multimodal LLMs include OpenAI's GPT-5.1 Thinking, Google's Gemini 3 Pro, Anthropic's Claude Opus 4.5, Alibaba's Qwen2.5-VL-72B, and DeepSeek's DeepSeek-V3.2.
The core value of multimodal LLMs lies in improving visual understanding capabilities and enhancing cross-modal content generation efficiency, enabling users to understand and create multimedia content more accurately. Whether it's visual question answering, multimedia content generation, or augmented reality applications, multimodal LLMs play crucial roles.
The main difference between multimodal LLMs and general-purpose LLMs or AI reasoning LLMs is that general-purpose LLMs like GPT, Claude, Gemini primarily handle text tasks, AI reasoning LLMs like AI reasoning LLMs focus on logical reasoning tasks, while multimodal LLMs are specifically optimized for cross-modal tasks, excelling in visual understanding, image description, video analysis, and other multimodal-related tasks.
How Multimodal LLMs Work
Modern multimodal LLM technology is based on deep learning and Transformer architecture, specifically trained on multimodal data, using cross-modal understanding and generation algorithms to understand relationships between text, images, audio, and video, performing cross-modal understanding and generation. The technology employs neural networks trained on vast multimodal datasets that learn relationships between different modalities and conversion patterns. Compared to traditional single-modal models requiring separate processing for each modality, multimodal LLMs show significant improvements in visual understanding, cross-modal reasoning, and multimedia content generation, making professional multimodal applications accessible to more users.
- Visual understanding: Understanding image content, recognizing objects, analyzing scenes, and extracting visual information for comprehensive image comprehension.
- Cross-modal fusion: Integrating text, images, audio, and video, understanding relationships between different modalities, and enabling unified multimodal processing.
- Multimedia generation: Generating images from text, descriptions from images, videos from text, and other cross-modal content generation capabilities.
- Unified multimodal architecture: Some models use unified architecture to process all modalities, simplifying model structure and improving processing efficiency.
Different multimodal LLMs use different architectures, optimized for their specific use cases. Multimodal LLMs typically use visual encoders, text encoders, and cross-modal fusion layers to achieve multimodal understanding. Main benchmarks include MMMU (Massive Multi-discipline Multimodal Understanding), MMBench (multimodal evaluation), and SEED-Bench (multimodal LLM benchmark), helping users understand different models' actual performance on multimodal tasks. These developments not only improve multimodal understanding efficiency and accuracy but also provide more possibilities for multimedia applications, making multimodal LLMs more widespread.
Best Multimodal LLMs 2026
Multimodal LLMs are large language models that integrate text, images, audio, and video, accessible via API. Many multimodal applications are built on these models. These models excel in visual understanding, cross-modal reasoning, multimedia content generation, and other multimodal-related tasks, demonstrating outstanding performance in benchmarks such as MMMU, MMBench, and SEED-Bench.
1. GPT-5.1 Thinking: Multimodal Understanding Leader
GPT-5.1 Thinking GPT-5.1 Thinking is OpenAI's top-tier multimodal LLM, demonstrating exceptional performance in multimodal benchmarks. The model achieves 85.4% on MMMU, approximately 87% on MMBench, and 82.5% on SEED-Bench, with an average score of 85.6%, ranking first. Core features include thinking capabilities, advanced visual understanding, cross-modal fusion, and multimedia generation. Ideal for complex visual understanding, cross-modal reasoning, and multimedia content generation scenarios.
2. Gemini 3 Pro: Unified Multimodal Architecture
Gemini 3 Pro Gemini 3 Pro is Google DeepMind's multimodal LLM, using unified multimodal architecture, achieving 81.0% on MMMU, 86.5% on MMBench, and 85.3% on SEED-Bench, with an average score of 84.2%, ranking second. Core features include unified multimodal architecture, large context, cross-modal understanding, and multimedia generation. Ideal for unified multimodal understanding, cross-modal reasoning, and multimedia content generation scenarios.
3. Claude 4.5 Opus Thinking: Thinking Multimodal
Claude 4.5 Opus Thinking Claude 4.5 Opus Thinking is Anthropic's multimodal LLM, excelling in thinking capabilities. The model achieves 74.0% on MMMU, approximately 85% on MMBench, and 87.2% on SEED-Bench, with an average score of 82.1%, ranking third. Core features include thinking capabilities, advanced visual understanding, cross-modal fusion, and logical analysis. Ideal for deep visual understanding, cross-modal reasoning, and complex multimedia analysis scenarios.
4. Qwen2.5-VL-72B: Chinese Multimodal Optimization
Qwen2.5-VL-72B Qwen2.5-VL-72B is Alibaba's multimodal LLM, excelling in Chinese multimodal scenarios. The model achieves 88.3% on MMBench, approximately 80% on SEED-Bench, and 76.5% on MMMU, with an average score of 79.8%, ranking fourth. Core features include Chinese multimodal optimization, advanced visual understanding, cross-modal fusion, and high cost-effectiveness. Ideal for Chinese multimodal understanding, Chinese visual question answering, and Chinese multimedia content generation. Its open-source version makes it ideal for customized development.
5. DeepSeek-V3.2 Thinking: Visual Reasoning Enhanced
DeepSeek-V3.2 Thinking DeepSeek-V3.2 Thinking is DeepSeek's multimodal LLM, excelling in visual reasoning. The model achieves approximately 83% on MMBench, 79.3% on SEED-Bench, and approximately 70% on MMMU, with an average score of 77.4%, ranking fifth. Core features include thinking capabilities, visual reasoning enhancement, Chinese multimodal support, and logical analysis. Ideal for visual reasoning, Chinese multimodal understanding, and real-time visual analysis scenarios.
Other Multimodal LLMs
Beyond the main multimodal LLMs above, many other excellent multimodal LLMs excel in specific multimodal scenarios:
GPT-5.2 (OpenAI): OpenAI's multimodal model, achieving 80.0% on MMMU and approximately 81% on SEED-Bench, excelling in multimodal tasks.
GPT-5 High (OpenAI): OpenAI's advanced multimodal model, achieving approximately 85% on MMBench and 79.2% on MMMU.
Gemini 3 Pro Preview (Google): Google's multimodal preview model, achieving 84.7% on SEED-Bench and approximately 80% on MMMU.
Gemini 2.5 Pro (Google): Google's multimodal model, achieving approximately 84% on MMBench and 78.6% on SEED-Bench.
Claude Opus 4.5 (Anthropic): Anthropic's multimodal model, achieving 85.4% on SEED-Bench and approximately 72% on MMMU.
Claude 4 Sonnet (Anthropic): Anthropic's multimodal model, achieving approximately 82% on MMBench and 79.0% on SEED-Bench.
Qwen2.5-VL-32B (Alibaba): Alibaba's medium-sized multimodal model, achieving 85.7% on MMBench and 78.1% on SEED-Bench.
DeepSeek R1 (DeepSeek): DeepSeek's reasoning multimodal model, achieving 73.1% on SEED-Bench and 80.6% on MMBench.
Multimodal LLM Comparison: Choose the Best for You
Below is a detailed comparison of leading multimodal LLMs to help you quickly understand each model's benchmark performance, core features, and applicable scenarios:
Use Cases: Visual Understanding and Generation
Multimodal LLMs have very wide application scenarios, covering multiple cross-modal fields from visual understanding to multimedia content generation.
Visual Question Answering
Multimodal LLMs excel in visual question answering, understanding image content and answering related questions. Users ask questions in natural language, and models automatically analyze image content, providing accurate answers. This significantly lowers visual understanding barriers, improving efficiency and accuracy.
Multimedia Content Generation
Multimodal LLMs have unique advantages in multimedia content generation, generating images from text, descriptions from images, and summaries from videos. Models with thinking capabilities enable complex multimedia analysis and generation. This is significant for improving content creation efficiency and multimedia content quality.
Augmented Reality Applications
Multimodal LLMs demonstrate powerful capabilities in augmented reality applications, understanding real-world scenes, recognizing objects, and analyzing environments. AR application developers receive powerful visual understanding support, improving AR application intelligence and user experience. This is significant for advancing AR technology development and application innovation.
Image Understanding and Analysis
Multimodal LLMs excel in image understanding and analysis, recognizing objects, analyzing scenes, and understanding image content. Users quickly understand image content, obtaining detailed image analysis results. This is significant for improving image processing efficiency and image understanding accuracy.
Video Analysis and Understanding
Multimodal LLMs have unique advantages in video analysis and understanding, understanding video content, analyzing video scenes, and generating video summaries. Users quickly understand video content, obtaining video summaries and analysis results. This is significant for improving video processing efficiency and video understanding capabilities.
How to Choose a Multimodal LLM
Select the right multimodal LLM based on your multimodal task type, benchmark performance, visual understanding requirements, and cost budget to maximize your visual understanding capabilities and multimedia creation efficiency.
1. Evaluate Multimodal Task Type
Choose models based on task type: visual question answering requires image understanding and reasoning; multimedia content generation needs cross-modal creation capabilities; augmented reality benefits from real-time multimodal processing; image understanding focuses on visual analysis. Select models providing corresponding multimodal capabilities based on task type.
2. Consider Benchmark Performance
Reference benchmark results: MMMU tests expert-level multimodal understanding across diverse domains; MMBench evaluates multimodal evaluation capabilities; SEED-Bench tests comprehensive multimodal LLM capabilities. Consider performance across benchmarks based on project needs: high scores indicate strong capabilities in specific areas.
3. Evaluate Visual Understanding Requirements
If advanced visual understanding is needed, prioritize models with strong visual understanding capabilities that can understand complex images, recognize multiple objects, and analyze scene content. If cross-modal fusion is needed, prioritize models with unified multimodal architecture seamlessly processing text, images, audio, and video. For fast visual analysis scenarios, choose models optimized for specific languages or use cases.
4. Consider Language and Cost
If Chinese multimodal understanding is needed, prioritize models optimized for Chinese with better performance for Chinese content. For English or other languages, choose models with strong multilingual capabilities. Choose plans based on usage frequency and budget: free versions suit small-scale use; paid versions suit large-scale use with higher limits and advanced features.
5. Test and Compare
Try 2-3 models first, testing performance in actual multimodal scenarios, comparing visual understanding quality, response speed, and accuracy. Compare different models' performance in visual question answering, multimedia content generation, augmented reality, and other tasks. Continuously assess and optimize model selection based on project needs. Multimodal LLMs should serve as collaborative partners, handling complex multimodal work, enabling users to focus on creativity and decision-making.
Conclusion
Multimodal LLMs are enhancing visual understanding and multimedia content generation, providing users with exceptional cross-modal assistance capabilities and efficiency improvements. Tools like GPT-5.1 Thinking, Gemini 3 Pro, and Claude 4.5 Opus Thinking enable seamless integration of text, image, and video processing, enabling users to work with diverse content types more effectively.
Choose the right model based on your multimodal needs: GPT-5.1 Thinking and Gemini 3 Pro for visual question answering, Claude 4.5 Opus Thinking for multimedia content generation, Qwen2.5-VL-72B and DeepSeek-V3.2 Thinking for Chinese multimodal understanding. Evaluate content types, accuracy requirements, language needs, and budget constraints to select the most suitable multimodal LLM solution.
Multimodal LLMs serve as collaborative partners, handling complex multimodal work, enabling users to focus on creativity and decision-making. The best approach is human-AI collaboration: AI manages cross-modal processing and content generation, while users provide creative direction, quality control, and strategic interpretation, maximizing both processing efficiency and content quality.
Frequently Asked Questions
References
- MMMU Benchmark. (2026). MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark. Retrieved from https://mmmu-benchmark.github.io/ - Massive multi-discipline multimodal understanding and reasoning benchmark for assessing expert-level multimodal understanding capabilities.
- MMBench. (2026). MMBench: Comprehensive Multimodal Evaluation Benchmark. Retrieved from https://mmbench.opencompass.org.cn/leaderboard - Comprehensive multimodal evaluation benchmark covering dimensions like perception and reasoning.
- SEED-Bench. (2026). SEED-Bench: Benchmarking Multimodal Large Language Models. Retrieved from https://huggingface.co/spaces/AILab-CVC/SEED-Bench_Leaderboard - Multimodal large language model benchmark using multiple-choice questions to evaluate image and video understanding capabilities.