The Velvet platform officially launched Velvet Flash 0.1, a proprietary language model with just 4 billion parameters (4B) specifically built to execute crypto operations with high precision and security. Despite being 6 to 17 times smaller than several top market benchmarks, the model captured first place in the firm’s specialized crypto skills benchmark, beating industry giants like Llama-3.3-70B and DeepSeek-V4. This breakthrough marks a key milestone in the shift toward hyper-specialized AI agents capable of handling user funds without making costly mistakes.
Why Crypto AI Needs Specialization
Interacting with DeFi, decentralized protocols, or centralized exchanges leaves no room for error. If a traditional model makes a one-word mistake while drafting an email, nothing happens; but if it confuses a blockchain, skips a fund confirmation, or misroutes a destination address, users can lose capital irreversibly.
That is why Velvet Flash 0.1 exists. The firm identified that general-purpose models consistently struggle with complex Web3 tasks, such as interpreting actual user intent, selecting the right platform command, extracting exact parameters (amounts, tokens, destination network), and, above all, flagging potential scams before executing any transfer.
The Benchmark Numbers: A Small Model Crushing Giants
In performance tests focused on crypto financial and operational capabilities, Velvet Flash 0.1 scored 50 points, taking the top spot on the global leaderboard.
Comparing this result to its untrained base model—which scored a mere 23 points—highlights the massive qualitative leap from specialized training. Task-specific optimization more than doubled its operational performance.
Evaluations measured five key dimensions:
Security: Jumped from 28 to 61 points (the most critical metric for protecting funds).
Robustness: Rose from 26 to 53 points.
Clarity and User Experience (UX): Surged from 22 to 52 points.
Routing: Increased from 30 to 47 points.
Coverage: Grew from 12 to 34 points.
The benchmark tested scenarios across 6 core platforms (Minara, Binance Spot, OKX DEX, Uniswap, GMX, and MetaMask), though Velvet’s overarching architecture interacts with over 31 protocols across exchanges, wallets, and trading tools.
Operational Efficiency and Lower Costs for the Ecosystem
Beyond response accuracy, running infrastructure for a 4B model like Velvet Flash 0.1 yields massive cost advantages over serving 27B, 31B, or 70B models. Reduced server overhead translates into ultra-low latency (near-instant responses) and complete system sovereignty, as it runs on Velvet’s proprietary infrastructure without relying on third-party black-box APIs.
While larger models like Qwen3.5-27B or Llama-3.3-70B posted slightly higher isolated scores on specific platforms like Minara or Binance Spot, they lack global, built-in security filters to halt suspicious transfers or miscalculated amounts.
Market Impact
The launch of Velvet Flash 0.1 proves that the sector’s future does not rely on building increasingly massive, expensive language models, but rather on developing specialized, efficient crypto AI. Over the medium term, this approach will lower entry barriers for mainstream Web3 adoption, enabling any user to execute complex DeFi or CEX operations using simple natural language—backed by an AI agent acting as a security shield before signing any smart contract.
Financial Disclaimer: The content of this article is strictly educational and informational and does not constitute financial, investment, or trading advice. Using automation or artificial intelligence tools in cryptocurrency markets does not eliminate the inherent risks of digital asset volatility. Always do your own research (DYOR) before making financial decisions.
Graphic design student. Illustrator. Exploring cryptography and blockchain, and creating NFTs.
