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OPG to USDT Converter
1 OPG = 0.113328 USDT · updated 06:22 UTC
Volume Bubbles Chart (1h)
Each 1-hour candle's volume is signed by its direction (up candles count as net buying, down candles as net selling) and drawn as a bubble at the closing price, green for buying and red for selling. Bubble size and colour intensity show how strong that flow is compared with the last 300 candles: the strongest 30% get full bubbles with their USD value, the rest stay as small dots. Unusually large bubbles flag aggressive buying or selling worth a closer look, often near breakouts, reversals or key levels.Seasonal Chart
Plots one line per calendar year showing the % change since the start of that year, with the current year highlighted against up to four previous years. Lining the years up on the same calendar shows whether the price tends to rise or fall in the same months, helping you spot recurring seasonal patterns. Treat it as context rather than a forecast: past seasonality does not guarantee future moves.OpenGradient Markets
OpenGradient Historical Data
About OpenGradient (OPG)
OpenGradient describes itself as decentralized infrastructure for secure and verifiable AI execution, agent and application deployment, and model hosting. Its official documentation lists a Python SDK, a decentralized model hub, confidential inference using trusted execution environments, and tools for deploying AI workflows. The Foundation’s tokenomics page assigns OPG roles in inference, governance, and ecosystem growth.
Verifiable AI execution
OpenGradient’s docs describe infrastructure intended to make AI execution and inference verifiable. Its product materials cover deploying models and workflows, with verification and integrity checks as part of the network design. These are technical goals and service descriptions; they do not mean every model output is factually correct or free from model error.
Developer tools
The official documentation lists a Python SDK for applications using verified AI infrastructure, along with tutorials and examples. It also describes agent and application deployment and model hosting. Developers should use the current SDK references for supported interfaces, authentication, and network endpoints because software interfaces can change between releases.
Confidential inference
OpenGradient’s docs describe confidential OHTTP inference using the OpenAI Python SDK, with prompts encrypted to an attested trusted execution environment and relayed through the Chat API. This is a specific privacy-oriented architecture. Its security properties depend on implementation, attestation, configuration, and the current service design.
Model hub and memory
The documentation describes a decentralized model hub where model architectures can be uploaded and browsed, as well as MemSync, a long-term memory layer. These products address different needs: model availability and application memory. Availability of a model or memory feature should be confirmed in the live product rather than assumed from a roadmap or landing page.
One-billion OPG allocation
OpenGradient Foundation’s tokenomics page lists a total supply of 1,000,000,000 OPG. The page allocates 40% to ecosystem, 15% to foundation, 15% to core contributors, 10% to investors and advisors, 10% to staking rewards, 6% to liquidity and launch, and 4% to airdrop. Vesting varies by category, with some allocations released at TGE and others subject to cliffs.
OpenGradient’s technical claims concern the system’s execution and verification design, while tokenomics describes how OPG is allocated. Confidential inference, a model hub, and MemSync are distinct products in the documentation. Availability of those services and the schedule for token release should be verified independently rather than inferred from the project’s broad description.
The Foundation page lists 400,000,000 OPG for ecosystem allocation and 150,000,000 for the foundation. It lists 100,000,000 for staking rewards, 60,000,000 for liquidity and launch, and 40,000,000 for the airdrop. The same page gives different TGE release and vesting terms, which should be considered alongside the headline percentages.
Last reviewed: Oct 9, 2026
OpenGradient (OPG) FAQ
What is OpenGradient (OPG)?
OpenGradient is decentralized infrastructure for secure and verifiable AI execution, model hosting, and agent or application deployment. Its documentation lists a Python SDK, decentralized model hub, and confidential inference tools. The Foundation’s tokenomics page describes OPG as the network token for inference, governance, and ecosystem growth.
What is OPG used for?
OpenGradient Foundation describes OPG as the native token powering the network, including verifiable AI inference, governance, and ecosystem growth. The technical docs separately explain products such as model hosting and confidential inference. The token’s stated role should not be taken as proof that every product feature has a live token fee.
What is verifiable AI inference?
OpenGradient’s materials describe infrastructure intended to let users verify aspects of AI execution and inference. Its documentation includes secure execution tools and confidential inference through an attested trusted execution environment. Verification can improve auditability, but it does not automatically guarantee that a model’s answer is true or unbiased.
Does OpenGradient support confidential inference?
Yes. OpenGradient’s documentation describes confidential OHTTP inference using the OpenAI Python SDK, with prompts encrypted to an attested trusted execution environment and relayed through the Chat API. The practical privacy guarantees depend on the current implementation and configuration, so developers should review the live technical guide.
What is OpenGradient’s model hub?
OpenGradient’s documentation describes a decentralized model hub where users can upload and browse models across supported architectures. It is part of the project’s broader AI infrastructure rather than a token allocation category. Developers should check the current product interface for availability, supported formats, and usage terms.
What is OPG’s total supply?
OpenGradient Foundation’s tokenomics page lists a total supply of 1,000,000,000 OPG. Its allocation assigns 40% to ecosystem, 15% to foundation, 15% to core contributors, 10% to investors and advisors, 10% to staking rewards, 6% to liquidity and launch, and 4% to airdrop. Circulating supply changes as vesting progresses.
How are OPG tokens vested?
The Foundation’s published schedule gives different release terms by category. Ecosystem tokens have a 60-month linear schedule, foundation tokens 48 months, core contributors and investors/advisors a 12-month cliff followed by 36 months linear, and staking rewards a 96-month linear schedule. Liquidity/launch and airdrop allocations are listed as fully unlocked at TGE.
What is OPG’s all-time high?
FMchart’s market all-time high for OPG is $0.4816 on Apr 22, 2026. This is a market-price record and does not measure AI inference quality, network usage, or developer adoption. Check the Price Statistics panel when market data is available and distinguish token price history from OpenGradient’s technical service documentation.
How has OPG performed historically?
Monthly Returns (USD) shows OPG's month-by-month changes in FMchart's composite history. Annual Price (USD) Change summarizes calendar years, and the composite series may start after OPG's first trading activity. The table is limited to the period covered by FMchart's composite market data.
What developer tools does OpenGradient provide?
OpenGradient’s official docs list a Python SDK, tutorials, a decentralized model hub, confidential inference, and MemSync for long-term memory. The docs also cover model hosting and workflow deployment. Developers should use current API and SDK references to confirm supported features and endpoints before building integrations.