OpenVision PriceVISIONInactive

What is OpenVision (VISION)?

OpenVision (VISION) is a decentralized AI protocol focused on advancing machine perception through real-world video and edge compute. According to the project's own description, it is "training the world's Vision Language Model using your camera & GPU," powered by the Modular Compute Protocol (MCP), and designed to be decentralized, permissionless, and oriented toward real-world AI. The protocol operates on the Ethereum network and aims to coordinate distributed cameras and GPUs to train Vision Language Models (VLMs). By turning homes, vehicles, and devices into intelligent data nodes, OpenVision seeks to create a censorship-resistant and globally scalable visual intelligence network. According to CoinMarketCap, VISION has a total supply, max supply, and circulating supply of 1,000,000,000 tokens. CoinMarketCap lists OpenVision on a preview page, indicating a lower listing tier. Detailed public information on certain operational aspects remains limited.

Key Specifications & Tokenomics

• Name: OpenVision
• Ticker / Symbol: VISION
• Blockchain / Network: Ethereum
• Category: Decentralized AI Protocol, Vision Language Models, Edge Compute
• Max Supply: 1,000,000,000 VISION
• Total Supply: 1,000,000,000 VISION
• Circulating Supply: 1,000,000,000 VISION
• Circulating Supply Percentage: 100%
• Launch Date: June 17, 2025 (CoinMarketCap listing date: June 19, 2025)
• Official Website: https://openvisionresearch.com/
• Whitepaper: https://openvisionresearch.com/whitepaper
• Explorer: etherscan.io
• Contract Address: 0xe6f98920852A360497dBcc8ec895F1bB1F7c8Df4
• X (Twitter): https://twitter.com/useopenvision

Who created OpenVision?

The OpenVision name is shared by several unrelated entities. The VISION token project and the UCSC-VLAA research framework of the same name are distinct; the research framework's authors are Xianhang Li, Yanqing Liu, Haoqin Tu, Hongru Zhu, and Cihang Xie, but the references do not confirm that this team issues the VISION token. This academic framework is documented across GitHub, arXiv, Hugging Face, and a dedicated project page. The materials also reference several distinct organizations using the "Open Vision" name, including Open Vision Engineering, Inc., Open Vision Technology, LLC., and Open Vision Co. Ltd., among others. Information regarding the specific founding team behind the VISION token itself is limited in the available references.

How does OpenVision work?

OpenVision operates through a Modular Compute Protocol (MCP) that powers a distributed training system called VisionDisTrO. This system coordinates training jobs across thousands of distributed cameras and GPUs, enabling the creation of Vision Language Models such as Vision3. According to the project's own description, OpenVision is built on Ethereum and turns homes, vehicles, and devices into intelligent data nodes. Participants contribute camera and GPU resources to train the network's Vision Language Model and earn VISION tokens in return. The research framework behind OpenVision includes vision encoders ranging from 5.9 million to 632.1 million parameters, supporting deployment from edge devices to high-capacity servers. The training approach emphasizes efficiency through progressive resolution training strategies and flexible patch-size configurations.

What makes OpenVision unique and valuable?

OpenVision's primary distinction lies in its decentralized, permissionless approach to training Vision Language Models using distributed cameras and GPUs. Unlike proprietary vision encoders such as OpenAI's CLIP or SigLIP, the OpenVision research framework is presented as an open alternative. According to the research paper, OpenVision is a fully-open family of vision encoders whose datasets, training recipes, and model checkpoints are public. This fosters reproducibility and transparency. The framework supports a wide range of model scales, from tiny 5.9 million parameter models suitable for edge deployment to huge 632.1 million parameter models for high-capacity servers. According to the research paper, its progressive multi-stage resolution training is reported to be 2×–3× faster than proprietary counterparts. It also supports flexible patch-size configurations for adaptive encoding. The protocol's goal of building a censorship-resistant and globally scalable visual intelligence network represents a novel intersection of decentralized physical infrastructure and AI model training.

What is OpenVision used for?

The VISION token is used to incentivize participation in the OpenVision network. According to the project description, users earn VISION by contributing camera and GPU resources to train the world's Vision Language Model. The protocol's infrastructure, including VisionDisTrO and the Modular Compute Protocol, coordinates these distributed resources to produce Vision Language Models like Vision3. The resulting visual intelligence network is intended for applications requiring machine perception through real-world video and edge compute. The research framework associated with OpenVision provides vision encoders for multimodal learning, supporting tasks such as image captioning, visual question answering, OCR, and chart understanding. These encoders are designed to be integrated into multimodal frameworks like LLaVA.

How Is the OpenVision ecosystem developing?

The OpenVision research framework has followed a structured release timeline. In May 2025, the initial OpenVision models and training code were released. September 2025 saw the release of OpenVision 2, a simplified generative-only version that removed the text encoder and contrastive loss while retaining only the captioning objective. In January 2026, OpenVision 3, a unified visual encoder for both understanding and generation, was released; according to the repository, full training code is to be released soon. August 2026 brought the release of OpenVision 2 caption text decoders, enabling full image-to-caption models. The GitHub repository contains the training and fine-tuning code for the OpenVision framework. The ecosystem also includes model collections on Hugging Face and project pages documenting the technical progress. The protocol itself lists on major market data platforms with a preview listing tier. The original OpenVision paper was accepted at ICCV 2025, and OpenVision 2 at CVPR 2026.

How to mine OpenVision?

OpenVision does not use traditional proof-of-work mining. Instead, participants contribute computational resources, specifically cameras and GPUs, to the network's distributed training system. The Modular Compute Protocol coordinates training jobs across these distributed devices, and contributors earn VISION tokens for their participation. This model resembles a decentralized physical infrastructure network where real-world devices become data nodes. The research framework supports training on Google Cloud TPU and various encoder architectures, with efficient training strategies including model sharding. VISION is not earned through traditional proof-of-work mining. According to the project description, participants contribute camera and GPU resources to the network's distributed training system, coordinated by the Modular Compute Protocol, and earn VISION tokens for their participation.

How to keep your OpenVision Coin safe?

As an Ethereum-based token, VISION can be stored in wallets that support ERC-20 assets. Users should consider using hardware wallets for enhanced security when holding significant amounts. VISION is an ERC-20 token on Ethereum with the contract address 0xe6f98920852A360497dBcc8ec895F1bB1F7c8Df4; always verify this address before interacting with the token. Keeping private keys and seed phrases offline and never sharing them with anyone is essential. Users should be cautious of phishing attempts and only interact with official links provided by the project, such as the official website and X account. Regular monitoring of the project's official channels for updates and announcements is also recommended. Phantom labels VISION as an unverified token and reports liquidity below $0.0001; tokenomics and market data also differ across platforms, so readers should verify the contract address and treat the figures with caution.