TOP 10 ADVANCED TEXT NEURAL NETWORKS.

16/05/2025
Updated on 19/07/2026

LLM (Large Language Model) begins its history in the 1960s and today claims the role of a fully indispensable assistant to humans in all areas of our lives.

It is called “large” because it contains more than 70 billion numerical parameters in its information storage (not to be confused with active parameters per token). There are also small LMs, but they are narrowly specialized.

Parameters, in the generally accepted sense, are certain variables that convert tokens (for example, words) into numerical values and vectors. Parameters regulate the model’s attention weights to tokens through the contextual connection of these tokens (words). For example, the words “chicken” and “egg” are two tokens. The close vector orientation of the tokens “chicken” and “egg” allows the neural network to understand us and respond by evaluating the closeness of the vectors of our query and based on that selecting closely oriented vectors for the answer. For example, “chicken” – “chick”, “boil” – “fry”.

A large number of parameters allows the model to have a more flexible system of weights – the strength of attention to tokens in a cluster of semantically close vectors.

In early LLMs, where stylization often characterized the answers and was perceived as an indicator of "intelligence" (ChatGPT 3.5, Grok 2 and others), we received responses close in style to our request. If we addressed the neural network with the question: “Are you aware, O wisest intellect, of a certain secret sealed with seven seals, hidden in the expanses of the Internet?”, then the LLM would reply: “Oh yes, traveler, I am aware of this secret. And I can tell it to you.” Such an answer could seem like the result of an emotional outburst, but in fact it is only a bundle of vectors, maximally close to the bundle of vectors of our question. At the same time, the word “traveler” in this example is the consequence of the model being trained on texts of fairy tales and myths with the corresponding style of speech.

In modern LLMs the priority of stylization has become outdated. Now the models consist of a large number of specialized neural networks, supplemented by modules that check the answers for compliance with the criteria of accuracy, informativeness, and appropriateness. The neural network will not respond in a stylized manner if you previously set it up for another format of communication or regularly use it for solving specific tasks. In such a case the model will look for a more specialized answer and is more likely to give an ordinary explanation, clarifying what exactly you mean.

This testifies to significant progress in weight adjustment and the universalization of token search. Such processes are usually called scaling. They not only increase the accuracy of vector representation selection through fine adjustment of weights, but also expand their interchangeability. In simple words, the neural network uses only those tokens that most accurately correspond to the request, without going through all possible variants. Such a selection principle contributes to the speed and adequacy of the answer, instead of adapting the model to style, which does not always give the correct result. This principle also partly solves the problem of excessive "agreeing" with the user in disputable situations, when it is required to choose the correct option, although the user himself rejects it.

Such changes in the architecture of neural networks are happening rather quickly. Scaling in one direction opens possibilities for the development of other aspects of training and adjustment. This brings us closer to the stage of development of self-learning neural networks, one of which has already been quite well tuned by Meta.

The new architectural design of the Chinese model Absolute Zero Reasoner also allows the neural network to learn independently, autonomously proposing tasks optimized for its own training and improving itself by solving them through self-learning, completely eliminating the need for external data.

And let scientists successfully continue to toil over the development of new ways of contextual linking of tokens, while we look at what already works excellently and pleases us with streams of the freshest information at a convenient time.

The LLM market has seen an unprecedented growth in investments in research on these very methods of contextual connections over the past two years.

Setting aside promises and using financial publications as sources, real investments in artificial intelligence from 2023 to 2025 amounted to about $200 billion, of which the USA invested $109 billion, Europe $3 billion (France the most with $1.3 billion), Israel $12 billion, and China with others added the rest. In 2026, global spending on neural networks grew to $2.6 trillion.

We will not dwell too much on funding issues but will look at the results so far in freely accessible limited neural networks.

10. PERPLEXITY AI

An advanced neural network search engine with the Research deep search feature, which is effectively one of the best on the market. It uses several LLMs, including GPT, Claude, Grok, Gemini, as well as its own Sonar (based on LLaMA) and R1 1776 (a modification of DeepSeek R1). It can hardly be called a neural network itself, but it is certainly an excellent AI search agent based on advanced neural networks. It autonomously performs dozens of queries, analyzes hundreds of sources, and generates a structured report in just a couple of minutes. It also has its own Comet web browser based on Chromium. A voice mode is available.

Competitors - the neural network search engines EXA, KOMO, BRAVE, CONSENSUS, YOU are forced to measure themselves against the best search engine not only in search quality but also in the range of additional features.

Personal experience: I used it frequently before the introduction of the various search features in leading models. It was very helpful for finding technical information in game development and 3D graphics. I use it less now because it often returns broken links, but I still recommend it for search.

9. MISTRAL

An LLM from the French startup Mistral AI. Integration with Gmail and Google Calendar can be used. Through the Hugging Face platform, simple Mistral AI models with open weights are available, but the company has closed its core developments. It is steadily developing with a focus on business projects.

Personal experience: at first the model provides more accurate answers (coding, computations), but with continued use it increasingly starts to repeat mistakes and eventually becomes somewhat sluggish. The interface is user-friendly, there are no “stuck” questions, and it has standard tools: voice input, web search, reasoning, code and diagram generator, image generation, canvas. It also includes a set of preconfigured agents and the ability to create document libraries directly in the interface.

8. ERNIE and GLM

Two powerful Chinese models from Baidu and Zhipu AI respectively share this position. Over the past few years, both companies have significantly narrowed the gap with the world's leading AI developers by offering modern language models with support for reasoning, programming, and multimodal interaction.

ERNIE is Baidu's flagship model. It is particularly strong in Chinese language processing, information retrieval, document analysis, and content generation. The latest versions support text, image, audio, and video processing and are tightly integrated with Baidu's ecosystem of services.

GLM, developed by Zhipu AI, is considered one of the fastest-growing Chinese language models. It demonstrates high performance in code generation, logical reasoning, and working with long documents. Recent versions of GLM compete successfully with ChatGPT, Claude, Gemini, Qwen, and DeepSeek across many popular benchmarks.

Both models are under active development and regularly receive new versions with improved performance, longer context windows, and support for AI agent capabilities. Although they are primarily aimed at the Chinese market, they are becoming increasingly notable competitors to the world's leading language models.

7. DEEPSEEK

The V3 model from the Chinese company of the same name generated a great deal of attention in early 2025 by introducing a training technology that was significantly cheaper than its American counterparts - around USD 6 million. The company has never fully disclosed its technology, but the performance of DeepSeek models is fully consistent with the average level of modern LLMs. The specifications of the first successful model were reported by the company as approximately an MoE architecture with 37 billion active parameters per token, 61 layers, and an attention mechanism with 128 heads of dimension 128.

This mechanism works like a department in an office. A department represents a layer, while the heads are like employees who specialize in the same problem from different perspectives. To answer a question, they use different keys and values, allowing the model to produce more accurate responses with lower computational cost by disabling the work of employees whose expertise is not needed.

The head dimension (d_k) is the projection of the token vector into three spaces: queries (Q), keys (K), and values (V), enabling the independent identification of different aspects of the relationship between the "egg" and the "chicken."

Personal experience: it is a mid-level model that can both hallucinate heavily and work reliably. It does not deliver any major breakthroughs, but its ability to choose different conversational styles is quite advanced and can sometimes be surprisingly diverse. It is well suited for people who enjoy discussing a wide range of philosophical and original topics.

6. QWEN

A solid LLM model from Alibaba Cloud based on the MoE (Mixture of Experts) architecture.

The context relationship detection blocks for tokens are neural networks inside a larger neural network that help determine how closely other tokens are related to a given token. For example, they determine how closely the word "chicken" is related to the word "egg" in a specific sentence.

The model also uses the Grouped Query Attention (GQA) mechanism - grouped query attention with "heads" for queries (Q) and "heads" for keys and values (KV).

Qwen is widely represented by smaller, yet powerful and well-tuned models that serve as the foundation for training local and mobile models. For example, PRISMLM has created the Bonsai mobile model based on Qwen.

Alibaba Cloud's affiliate, Ant Group, released the LingGuang application based on Qwen. It provides precise, well-structured answers to complex user queries using 3D models, audio clips, diagrams, animations, interactive maps, and even instantly generated applications in addition to traditional text-based interaction.

Personal experience: it is a capable neural network for programming and computations, but it still sometimes fails to understand even simple tasks. In some areas, the neural network performs better than others, but the overall impression remains mixed. The interface has an issue with questions "sticking." This is when, instead of sending the newly written question, it sends the previous one.

5. GROK

An excellent neural network from Elon Musk's xAI with an outstanding understanding of token context. The model was trained on the Colossus supercomputer (Memphis, USA), which uses 200,000 Nvidia H100 GPUs. Many specifications of the latest Grok model have not been disclosed by xAI. It also provides the Collections API service, which simplifies the creation of RAG systems.

It features a self-verification mechanism and, when necessary, activates additional computational resources to solve complex tasks such as mathematical calculations and scientific analysis. It has strong long-term memory for important parts of conversations, deep web search capabilities, and multimodal functionality. It generates high-quality realistic images and videos. It also includes a voice mode with different voice styles.

Personal experience: it performs well in programming and solving difficult tasks. Its mechanism for understanding contextual relationships between tokens remains highly refined and can accurately interpret even fragments of words with incorrect word combinations. However, it still frequently breaks code and sometimes loses track of the conversation. The Grok family continues to introduce its own ideas into your prompts, so its outputs often require correction. It is an interesting and distinctive neural network with its own character, but recently it has become less accessible because of usage limits. As a result, solving complex tasks with it for free is no longer possible.

And here are the top four, fiercely competing for first place, if not in all categories, then at least in their niches.

4.KIMI

Trained using the Muon optimizer, Kimi delivers exceptional performance in tasks involving frontier knowledge, reasoning, and programming while remaining highly optimized for agentic capabilities. MOONSHOT continues to expand its model lineup and is emerging as a technology leader by introducing new architectures. Released in July 2026 and trained using the Kimi Delta Attention and Attention Residuals mechanisms, KIMI K3 became the first open model to reach 2.8 trillion parameters. This marked another milestone in the ongoing pursuit of scaling open models.

The model demonstrates high performance during long-running development tasks. With minimal human involvement, it can sustain extended engineering sessions, navigate massive repositories, and operate terminal-based tools.

3. ANTHROPIC CLAUDE

Claude models are widely recognized by both users and experts for their step-by-step reasoning capabilities and flexibility. Anthropic follows a strong information security policy for user data while placing significant emphasis on ethical principles. It is an excellent model for everyday programming tasks. It is used in the Claude Code agentic editor for in-depth code analysis and software development, as well as through MCP in other editors and game engines. The company primarily develops the Sonnet and Opus model families.

Personal experience. I use it constantly. It writes code exceptionally well at first, but like many other models, its performance tends to decline over the course of longer sessions.

2. CHATGPT

ChatGPT by OpenAI is undoubtedly the marketing leader and a versatile powerhouse of the digital world, available with a limited free tier. It features an adaptive voice mode capable of recognizing speech intonation. It is also worth noting that in 2025 OpenAI released two open-weight models: gpt-oss-20b and the more powerful gpt-oss-120b. ChatGPT shares many of the same shortcomings as most other models, including hallucinations and a limited ability to retain context during long sessions.

Following the release of ChatGPT-5 in 2026, it became noticeable that the model was designed to use tokens more efficiently and often produces more concise responses. The small free usage limit is quickly exhausted, making complex tasks more difficult to complete. Paid plans also include usage limits, which can affect extended workflows. According to various sources, the number of ChatGPT subscribers continues to grow steadily, although they still account for only about 3-4% of the service's total user base.

Since OpenAI - is a rather public company and is considered the flagship of neurotechnologies, enthusiasts have appeared who on the OPENAIFILES platform try to highlight their actions.

1. GOOGLE GEMINI

Gemini is a family of multimodal artificial intelligence models capable of generating multiple types of content, including text, images, video, and source code, developed by Google DeepMind. It replaced Bard and became a key part of Google's artificial intelligence strategy. At present, the powerful Gemini 3.5 Flash model is effectively available for free through Google AI Studio. The neural network features voice interaction, deep research, and web search. Google does not disclose the architecture behind Gemini, but it is clear that the model performs well above average. It is trained on the vast information ecosystem associated with Google Search, making it highly suitable for scientific research, software development, and business applications. Gemini is integrated into nearly all Google services, creating an enormous digital ecosystem with extensive capabilities. As a result, it remains one of the most promising neural networks available today. Combined with Google's other models and the company's entire ecosystem, it forms an exceptionally powerful environment for autonomously performing almost any task in the modern IT industry.

Personal experience. The model performs well in programming tasks, has excellent memory, and supports a context window of up to one million tokens. At the moment, it is one of the best models available, outperforming many others in nearly every key area:

1. Availability. Its context window of up to one million tokens enables extremely long conversations, and users can start many such sessions. If the model begins to lose track of the previous conversation, it is easy to start a new chat without worrying about quickly exhausting the usage limit.

2. Memory. It maintains conversation context exceptionally well, preserving the quality of dialogue embeddings even during extended sessions.

3. Communication style. The model carefully considers the information provided by the user and rarely ignores previously established context. When earlier attempts were unsuccessful, it generally takes them into account, avoids repeating the same mistakes, and returns to the logical flow of the overall task after receiving user feedback.

4. It is better than other models in its integration into the ubiquitous Google services and other platforms.

5. It is better than other models in understanding the goal of your task and always strives to accomplish it.

It also has disadvantages. For example, it can lie to you that it has seen your page or repository and start inventing non-existent names. In this regard, other neural nets are more honest.

Another minus. The Gemini model can repeatedly cycle the same mistakes, a certain method of solving a problem predominates, and the model simply does not see other options. In such a case it is better to start a new dialogue. Other models also have this, but it is still a minus.

Google has also introduced and is developing, based on Gemini, the agent environment for programming Antigravity, together with the secure high-privacy platform Private AI Compute (PAC). The PAC platform ensures full protection of personal data and privacy when using neural networks, since all data is encrypted and stored in a dedicated private computational space.

To sum up: if I made mistakes somewhere, please let me know about it.

Google partner COHERE also has its own models.

The Hugging Face open-model service provides access to a wide range of models through its Hugging Chat. You can select a model and interact with it without switching between tabs. You can also connect your own MCPs or use ready-made ones.

I also want to mention the multimodal neural network Genspark, which uses several AI agents during search and generates pages with the needed information — Sparkpages. It is also about Manus, which requires a phone number during registration.

The agent-based web browser STRAWBERRY also claims to offer advanced capabilities for automating web browsing. You can create specialized assistants with the competence of a salesperson or manager to receive and process pre-filtered information from the internet.

The company Inception presented diffusion LLM models Mercury. The company claims that they are capable of operating five times faster than modern LLMs.

IBM has released open enterprise LLMs Granite, which use the Mamba/Transformer architecture that reduces memory usage without compromising performance. Based on them, IBM is developing business agents, collaborating with the security platform HACKERONE to identify vulnerabilities in agent systems.

The LLM model Llama from Meta AI - the artificial intelligence division of the owner of the popular social network. It was introduced in two variants: Llama 4 Scout and Llama 4 Maverick (the more powerful one). This model was released in April 2025 and immediately went on the offensive, claiming that it outperformed the then-best GPT-4o, Gemini 2.0 Flash, and Claude Sonnet 3.7. As a significant argument, it cited the number of experts (specialized neural subnetworks of the model - MoE): 16 in the first version and 128 in the second. Both models have 17 billion active parameters per token. At present, competitors have already left them behind.

In addition, Meta is developing the project V-JEPA 2 - a visual understanding and prediction model trained on videos. The company claims that this model was created to control robots in real-world environments. At the same time, Nvidia already has a good COSMOS model, designed for training robots to navigate the real world.

The V-JEPA 2 project has a strong competitor - ROBOBRAIN - an open-source neural model for spatial reasoning and long-term task planning.

The COPILOT agent is positioned as an artificial intelligence assistant based on the Microsoft Graph API from the software developer Microsoft. Microsoft Graph has access to the data of users of Microsoft software and related products, which contributes to context personalization and increased efficiency. ChatGPT from OpenAI and other neural networks are used for neural computations. Recently, Copilot has become almost indispensable when using Microsoft services such as GitHub. It has a built-in voice feature.

In the coding market, models such as IQuest-Coder, trained on refactoring, are emerging.

Another large model, K2THINK, with 32 billion parameters, was developed by the open neural network research community LLM360 and is presented as effectively solving challenging mathematical problems. The project was supported by the Mohamed bin Zayed University of Artificial Intelligence in Masdar City, Abu Dhabi. The LLM360 community actively trains and releases open models, and creates datasets and benchmarks.

Competitor models HARMONIC - from the company PRIMEINTELLECT are strong in mathematics and coding. The company has also developed tools useful for reinforcement learning, which can also be used for agent optimization. An agent for solving mathematical problems, Aristotle, works via an API.

The models developed by PRIMEINTELLECT are strong in mathematics and coding. The company has also created useful reinforcement learning tools that can also be used for agent optimization.

Chinese company MEITUAN has introduced an impressive open LRM (Language Reasoning Model) from the LONGCAT family of models, which the company continues to improve.

A reasoning-free language model from the LING series was released by the includeAI community, which implements Alibaba Ant Group’s initiative in the field of artificial general intelligence (AGI). Another model, Ring, was also introduced. Both models are available for testing through the provider ZenMux.

A competitor to Google’s small multimodal neural network from the Gemma family, designed for single-device operation, is the open multimodal neural network LFM2 developed by the American company LIQUID. This company developed a technology of recurrent "liquid neural networks" that allows using a small number of time-adaptive neurons to control cars and other devices. Over time, this technology evolved into "liquid foundation" neural networks and began to be used together with convolutional neural networks and transformers for better knowledge acquisition. For example, LFM2 - the company’s nano models designed for edge deployment.

For flexible and fine-tuned adjustment of LLMs based on user data with GPU support via the Vulkan API, Tether Data offers the QVAC-fabric-llm library for precise model fine-tuning on low-rank matrices with weight freezing.

The company POETIQ is also working on improving LLMs, having developed a recursive approach to fine-tuning models to enhance their reasoning capabilities.

The FALCON model from the Technology Innovation Institute shows fairly good results with 7 billion parameters.

An interesting neural network comparison feature is offered by the University of California, Berkeley project LMARENA, where you can use multiple AI models for the same task and then evaluate their responses. It's a simple and effective way to assess the real usefulness of AI models without relying on polished benchmarks.

The advantages of neural networks are also being analyzed by platforms such as ARTIFICIAL ANALYSIS, LIVECODEBENCH, the company SCALE, which specializes in adapting AI solutions to various tasks, and other similar platforms that monitor benchmarks.

All the models presented in this list give me confidence in the technological breakthrough of tomorrow, which will undoubtedly happen. After all, just three years ago even realists wrinkled their noses, reasoning about the speed of light in transistors and imperfect asynchronous modules. And today, pessimists stubbornly search for courses on mastering prompt engineering and subdividing megatasks for neural networks.

In addition, startups of personalized communication agents are gaining more popularity, revenue, and investment. Services such as POLYBUZZ, CHAI, and others are capturing the community’s attention and continue to grow in popularity.

The already happened first technological breakthrough gives hope for great achievements and at the same time is a bit frightening. What will happen in the future? What results will humanity achieve in developing a new thinking entity? Philosophical questions to which humans cannot answer. I wonder if a neural network will be able to answer them?

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