Top 10 AI Companies Developing Large Language Models For Crypto
Artificial intelligence and cryptocurrency are no longer developing in separate corners of the technology industry. As blockchain applications become more complicated, developers and users increasingly need AI systems that understand smart contracts, tokenomics, on-chain activity, decentralized finance and the peculiar language of Web3.
That is where crypto-specific large language models are beginning to make a difference. General-purpose models can explain what a blockchain is, but they can struggle when asked to interpret a complex transaction, assess a DeFi protocol or understand the relationship between wallet activity and market conditions. Crypto-focused models are being trained, fine-tuned or connected to datasets that give them a much stronger understanding of these environments.
The market is still relatively young, and not every project described as a “crypto AI company” is building a foundation model from scratch. Some are training their own models, while others are fine-tuning existing open-source models or building specialized infrastructure around LLMs. Even so, these companies and platforms are helping push the industry toward AI systems that can do more than simply talk about crypto.
ChainGPT
ChainGPT is among the clearest examples of a company building AI specifically around the needs of the Web3 industry. Its Web3 AI LLM is designed around blockchain data, smart contracts, DeFi, NFTs and tokenomics rather than relying solely on the broad knowledge found in general-purpose models.
One of the more interesting aspects of ChainGPT’s approach is its connection to live information. Its documentation says the model can work with on-chain data, market information, news and social feeds, allowing developers to build applications that need more current context than a static language model can provide.
That makes the technology useful for everything from crypto research and customer support to trading assistance and smart-contract analysis. ChainGPT has also made its model available through APIs and SDKs, giving other Web3 companies a way to add crypto-aware AI without having to develop an entire model stack themselves.
Fetch.ai
Fetch.ai has taken a slightly different route by focusing on AI models that can operate as part of autonomous agent systems. In February 2025, Fetch.ai introduced ASI-1 Mini, which it described as a Web3-native large language model built for agentic AI workflows.
The important distinction is that ASI-1 Mini is not simply positioned as another chatbot trained to answer questions about cryptocurrency. Fetch.ai designed it around reasoning, decision-making and autonomous workflows, with multiple reasoning modes intended to balance speed and depth.
That approach matters because the next phase of crypto AI is likely to involve agents that can actually interact with blockchain networks. Instead of asking an AI which token has the highest yield and then manually executing a transaction, users could eventually delegate parts of that process to autonomous systems. Models such as ASI-1 Mini are being developed with that broader vision in mind.
CryptoGPT
CryptoGPT is another project that has attempted to build AI infrastructure specifically for the cryptocurrency and blockchain sectors. Its stated focus extends beyond a simple AI chatbot to applications such as smart-contract generation, auditing, trading assistance and other Web3-related tools.
The project has also outlined plans for an AI-focused blockchain infrastructure through its AIVM, intended to support the execution, training and provision of computing resources for AI models on-chain.
There is an important distinction here, however. CryptoGPT’s own documentation says its AI models are not open source, while access is intended to be provided through APIs and SDKs. That makes its strategy closer to an AI infrastructure provider than an open research lab.
For crypto users, the attraction is fairly straightforward. Instead of forcing a general AI system to learn blockchain terminology from scratch, CryptoGPT is trying to bake that domain knowledge into the product.
BytomDAO
BytomDAO has taken one of the more direct approaches to creating a crypto-focused language model. Its CryptoGPT initiative is based on CryptoInstruct, a dataset containing millions of instruction examples designed around cryptocurrency-related information and tasks.
The project says it fine-tuned Llama 3 using the CryptoInstruct dataset to produce CryptoGPT models at different parameter scales. The idea behind this approach is particularly relevant to the crypto industry because blockchain information is highly specialized and constantly changing.
Rather than attempting to compete with the biggest general-purpose models across every possible subject, a specialized model can concentrate its capabilities on areas such as project information, blockchain concepts, and crypto-specific tasks.
This is one of the clearest examples of why domain-specific training could become important in Web3. A smaller model that understands crypto deeply can sometimes be more useful for a blockchain application than a much larger model that knows a little about everything.
IndexAI
IndexAI is another project built around the idea that language models should be able to interact directly with blockchain networks. The platform describes its foundational model as an open-source LLM trained on blockchain knowledge.
Its ambition goes beyond simply answering questions. IndexAI says its technology allows AI agents to read information across multiple blockchains and, with the appropriate capabilities, write to those networks by signing transactions, executing trades and performing swaps.
That distinction is significant. The real opportunity in crypto-specific LLMs may not be better conversations about Bitcoin or Ethereum. It may be the ability to turn natural-language instructions into safe, verifiable blockchain actions.
A user could eventually describe an objective in ordinary language while an AI system handles the underlying blockchain interactions. That requires considerably more domain awareness than a conventional chatbot.
DMind
DMind is positioning itself as an open-source AGI research organization focused specifically on digital finance. Its work includes large language models, datasets, benchmarks and tools aimed at financial and Web3 applications.
The company’s DMind-3 models are particularly interesting because they are designed around the realities of financial execution in Web3. DMind says the models are intended to address situations in which a single user action can pass through several smart contracts, trigger liquidations or expose funds to adversarial execution.
That is a different problem from simply generating a coherent answer. In decentralized finance, an AI system can potentially influence transactions involving real money. As a result, understanding the surrounding execution environment, risks and interactions between contracts becomes just as important as producing fluent text.
DMind’s open-source approach could also make its work useful to developers who want to inspect, adapt and build on specialized financial AI models rather than depend entirely on closed commercial systems.
Nexis Labs
Nexis Labs is working on the intersection of large language models, autonomous agents and blockchain execution through its Nex-T1 platform. The company describes Nex-T1 as an enterprise-grade AI system designed to connect advanced LLMs with actionable blockchain execution.
Its focus is particularly relevant to DeFi, where autonomous systems need to interpret market information and then potentially take action. Nexis Labs describes its work around multi-agent orchestration, autonomous DeFi trading and human-in-the-loop controls.
This highlights an important evolution in the crypto AI market. The model itself is only one piece of the puzzle. For an AI system to become genuinely useful in decentralized finance, it needs an orchestration layer, access to reliable blockchain information and safeguards around execution.
Nexis Labs is therefore approaching the problem from the agent side, where an LLM becomes part of a larger system capable of reasoning over financial information and interacting with blockchain infrastructure.
0G
0G is building a broader decentralized AI ecosystem that includes its own model infrastructure. Its platform currently lists models including 0GM-1.0-35B-A3B alongside other models available through its AI stack.
The company’s larger objective is to bring AI computation, storage and inference closer to blockchain infrastructure. It describes its network as supporting fully on-chain AI, verifiable computation and private inference.
That makes 0G relevant to the crypto-specific LLM conversation even though its ambition extends beyond one particular language model. The company is effectively working on the infrastructure required for AI models to function in decentralized applications.
This could prove just as important as the models themselves. Running sophisticated AI systems requires enormous computing and storage resources, while blockchain applications require transparency and verifiability. Bringing those two requirements together is one of the industry’s biggest technical challenges.
Nous Research
Nous Research sits somewhat differently from the other names on this list because its language models are not exclusively designed for cryptocurrency. Its importance comes from the company’s work on open-source AI and decentralized model training.
The lab develops open-weight language models, including its Hermes family, while also working on infrastructure for distributed training. Its research around decentralized AI has included the use of blockchain-based coordination and distributed computing.
The connection to crypto is therefore less about creating a “Bitcoin chatbot” and more about changing how powerful language models can be trained and operated. That distinction is becoming increasingly important as crypto projects explore decentralized alternatives to the centralized AI infrastructure dominated by a relatively small group of technology companies.
Nous Research’s work demonstrates that crypto-specific AI does not necessarily have to mean a model trained exclusively on crypto data. It can also mean building models and infrastructure that fit naturally into decentralized ecosystems.
Galadriel
Galadriel is approaching the relationship between LLMs and blockchain from another angle: making AI inference verifiable.
Its Sentience project allows developers to create autonomous AI agents whose LLM inferences can be verified through cryptographic proofs. The system uses trusted execution environments to process LLM requests and posts attestations on Solana, allowing applications to verify that an inference was executed as claimed.
While Galadriel is not simply building a proprietary crypto chatbot, its work addresses a major problem for blockchain-based AI. If an AI agent is making decisions that can move money or execute transactions, users need more than a model’s word that the process was handled correctly.
Verifiable AI could eventually become a critical part of crypto-native language models, particularly as autonomous agents gain greater control over wallets and financial transactions.
The Bigger Race Is About More Than Chatbots
The rise of crypto-specific LLMs points to a broader shift in the AI industry. General-purpose models have already become remarkably capable at explaining blockchain concepts, writing smart-contract code and summarizing crypto news. The next challenge is making AI understand the blockchain environment deeply enough to act within it safely.
That means having access to real-time on-chain information, understanding smart-contract interactions, recognizing financial risks and knowing when an action could have irreversible consequences.
The companies building in this space are approaching the problem from different directions. Some are training models on crypto-specific datasets. Others are building Web3-native LLMs, decentralized training networks, autonomous agents or verification systems that make AI activity more transparent.
The market is still early, and several projects will likely disappear while others evolve into much larger AI infrastructure companies. But the direction is becoming clearer. Crypto does not necessarily need another chatbot that can explain what Bitcoin is. What it needs are AI systems that understand how decentralized markets actually work.
If that happens, the most important crypto AI models may eventually be the ones users barely notice. They will sit behind wallets, exchanges, DeFi applications and autonomous agents, quietly interpreting blockchain data and helping turn natural-language instructions into actions on-chain.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
