Best Multimodal Models for ExecuTorch
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Compare the Top Multimodal Models that integrate with ExecuTorch as of August 2026
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This a list of Multimodal Models that integrate with ExecuTorch. Use the filters on the left to add additional filters for products that have integrations with ExecuTorch. View the products that work with ExecuTorch in the table below.
What are Multimodal Models for ExecuTorch?
Multimodal models are artificial intelligence models capable of understanding, processing, and generating multiple types of data—including text, images, audio, video, code, and other structured or unstructured inputs—within a single unified system. These models combine information across modalities to perform tasks such as visual question answering, image generation, speech recognition, video understanding, document analysis, code generation, and conversational AI. Many multimodal models support advanced capabilities such as tool use, reasoning, AI agents, and long-context processing, enabling more natural and context-aware interactions. They are commonly available through APIs, cloud AI platforms, and open-source frameworks for use in enterprise applications, creative workflows, robotics, healthcare, education, and software development. By integrating multiple forms of information into a single model, multimodal models enable more capable, flexible, and human-like AI systems. Compare and read user reviews of the best Multimodal Models for ExecuTorch currently available using the table below. This list is updated regularly.
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1
Muse Glimmer
Meta
Muse Glimmer is a 30-billion-parameter open-weights model from Meta Superintelligence Labs, optimized for always-on local agent workflows. Small enough to run on a Mac or PC with a single consumer GPU, it is designed for local agents, function calling, coding, and LLM-as-a-judge evaluation without depending on cloud infrastructure or network access. The model combines long-horizon execution, precise tool calling, multimodal understanding, long-context memory, and instruction following. It can complete end-to-end agentic tasks, sustain multi-step reasoning across extended workflows, recover from failed or unexpected tool calls, and accept interleaved text and images through a dedicated perception encoder for interpreting screenshots, charts, and documents. Muse Glimmer works with OpenClaw and other agentic orchestration patterns, supports controllable reasoning effort, and is trained on data from more than 100 languages.Starting Price: Free -
2
Llama 3.2
Meta
The open-source AI model you can fine-tune, distill and deploy anywhere is now available in more versions. Choose from 1B, 3B, 11B or 90B, or continue building with Llama 3.1. Llama 3.2 is a collection of large language models (LLMs) pretrained and fine-tuned in 1B and 3B sizes that are multilingual text only, and 11B and 90B sizes that take both text and image inputs and output text. Develop highly performative and efficient applications from our latest release. Use our 1B or 3B models for on device applications such as summarizing a discussion from your phone or calling on-device tools like calendar. Use our 11B or 90B models for image use cases such as transforming an existing image into something new or getting more information from an image of your surroundings.Starting Price: Free -
3
LLaVA
LLaVA
LLaVA (Large Language-and-Vision Assistant) is an innovative multimodal model that integrates a vision encoder with the Vicuna language model to facilitate comprehensive visual and language understanding. Through end-to-end training, LLaVA exhibits impressive chat capabilities, emulating the multimodal functionalities of models like GPT-4. Notably, LLaVA-1.5 has achieved state-of-the-art performance across 11 benchmarks, utilizing publicly available data and completing training in approximately one day on a single 8-A100 node, surpassing methods that rely on billion-scale datasets. The development of LLaVA involved the creation of a multimodal instruction-following dataset, generated using language-only GPT-4. This dataset comprises 158,000 unique language-image instruction-following samples, including conversations, detailed descriptions, and complex reasoning tasks. This data has been instrumental in training LLaVA to perform a wide array of visual and language tasks effectively.Starting Price: Free
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