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LangChain AI Local Deep Researcher is an autonomous, fully local web research assistant designed to conduct in-depth research on user-provided topics. It leverages local Large Language Models (LLMs) hosted by Ollama or LM Studio to iteratively generate search queries, summarize findings from web sources, and refine its understanding by identifying and addressing knowledge gaps. The final output is a comprehensive markdown report with citations to all sources.
LangChain AI Local Deep Researcher is an autonomous, fully local web research assistant designed to conduct in-depth research on user-provided topics. It leverages local Large Language Models (LLMs) hosted by Ollama or LM Studio to iteratively generate search queries, summarize findings from web sources, and refine its understanding by identifying and addressing knowledge gaps. The final output is a comprehensive markdown report with citations to all sources.
LangChain AI Local Deep Researcher is an autonomous, fully local web research assistant designed to conduct in-depth research on user-provided topics. It leverages local Large Language Models (LLMs) hosted by Ollama or LM Studio to iteratively generate search queries, summarize findings from web sources, and refine its understanding by identifying and addressing knowledge gaps. The final output is a comprehensive markdown report with citations to all sources.
codex-mini-latest is OpenAI’s lightweight, high-speed AI coding model, fine-tuned from the o4-mini architecture. Designed specifically for use with the Codex CLI, it brings ChatGPT-level reasoning directly to your terminal, enabling efficient code generation, debugging, and editing tasks. Despite its compact size, codex-mini-latest delivers impressive performance, making it ideal for developers seeking a fast, cost-effective coding assistant.
codex-mini-latest is OpenAI’s lightweight, high-speed AI coding model, fine-tuned from the o4-mini architecture. Designed specifically for use with the Codex CLI, it brings ChatGPT-level reasoning directly to your terminal, enabling efficient code generation, debugging, and editing tasks. Despite its compact size, codex-mini-latest delivers impressive performance, making it ideal for developers seeking a fast, cost-effective coding assistant.
codex-mini-latest is OpenAI’s lightweight, high-speed AI coding model, fine-tuned from the o4-mini architecture. Designed specifically for use with the Codex CLI, it brings ChatGPT-level reasoning directly to your terminal, enabling efficient code generation, debugging, and editing tasks. Despite its compact size, codex-mini-latest delivers impressive performance, making it ideal for developers seeking a fast, cost-effective coding assistant.
DeepSeek R1 Distill refers to a family of dense, smaller models distilled from DeepSeek’s flagship DeepSeek R1 reasoning model. Released early 2025, these models come in sizes ranging from 1.5B to 70B parameters (e.g., DeepSeek‑R1‑Distill‑Qwen‑32B) and retain powerful reasoning and chain-of-thought abilities in a more efficient architecture. Benchmarks show distilled variants outperform models like OpenAI’s o1‑mini, while remaining open‑source under MIT license.
DeepSeek R1 Distill refers to a family of dense, smaller models distilled from DeepSeek’s flagship DeepSeek R1 reasoning model. Released early 2025, these models come in sizes ranging from 1.5B to 70B parameters (e.g., DeepSeek‑R1‑Distill‑Qwen‑32B) and retain powerful reasoning and chain-of-thought abilities in a more efficient architecture. Benchmarks show distilled variants outperform models like OpenAI’s o1‑mini, while remaining open‑source under MIT license.
DeepSeek R1 Distill refers to a family of dense, smaller models distilled from DeepSeek’s flagship DeepSeek R1 reasoning model. Released early 2025, these models come in sizes ranging from 1.5B to 70B parameters (e.g., DeepSeek‑R1‑Distill‑Qwen‑32B) and retain powerful reasoning and chain-of-thought abilities in a more efficient architecture. Benchmarks show distilled variants outperform models like OpenAI’s o1‑mini, while remaining open‑source under MIT license.
DeepSeek R1 0528 – Qwen3 ‑ 8B is an 8 B-parameter dense model distilled from DeepSeek‑R1‑0528 using Qwen3‑8B as its base. Released in May 2025, it transfers high-depth chain-of-thought reasoning into a compact architecture while achieving benchmark-leading results close to much larger models.
DeepSeek R1 0528 – Qwen3 ‑ 8B is an 8 B-parameter dense model distilled from DeepSeek‑R1‑0528 using Qwen3‑8B as its base. Released in May 2025, it transfers high-depth chain-of-thought reasoning into a compact architecture while achieving benchmark-leading results close to much larger models.
DeepSeek R1 0528 – Qwen3 ‑ 8B is an 8 B-parameter dense model distilled from DeepSeek‑R1‑0528 using Qwen3‑8B as its base. Released in May 2025, it transfers high-depth chain-of-thought reasoning into a compact architecture while achieving benchmark-leading results close to much larger models.
Mistral Embed is Mistral AI’s high-performance text embedding model designed for semantic retrieval, clustering, classification, and retrieval-augmented generation (RAG). With support for up to 8,192 tokens and producing 1,024-dimensional vectors, it delivers state-of-the-art semantic similarity and organization capabilities.
Mistral Embed is Mistral AI’s high-performance text embedding model designed for semantic retrieval, clustering, classification, and retrieval-augmented generation (RAG). With support for up to 8,192 tokens and producing 1,024-dimensional vectors, it delivers state-of-the-art semantic similarity and organization capabilities.
Mistral Embed is Mistral AI’s high-performance text embedding model designed for semantic retrieval, clustering, classification, and retrieval-augmented generation (RAG). With support for up to 8,192 tokens and producing 1,024-dimensional vectors, it delivers state-of-the-art semantic similarity and organization capabilities.
Mistral Moderation API is a content moderation service released in November 2024, powered by a fine-tuned version of Mistral’s Ministral 8B model. It classifies text across nine safety categories—sexual content, hate/discrimination, violence/threats, dangerous/criminal instructions, self‑harm, health, financial, legal, and personally identifiable information (PII). It offers two endpoints: one for raw text and one optimized for conversational content.
Mistral Moderation API is a content moderation service released in November 2024, powered by a fine-tuned version of Mistral’s Ministral 8B model. It classifies text across nine safety categories—sexual content, hate/discrimination, violence/threats, dangerous/criminal instructions, self‑harm, health, financial, legal, and personally identifiable information (PII). It offers two endpoints: one for raw text and one optimized for conversational content.
Mistral Moderation API is a content moderation service released in November 2024, powered by a fine-tuned version of Mistral’s Ministral 8B model. It classifies text across nine safety categories—sexual content, hate/discrimination, violence/threats, dangerous/criminal instructions, self‑harm, health, financial, legal, and personally identifiable information (PII). It offers two endpoints: one for raw text and one optimized for conversational content.
BoundaryML.com introduces BAML, an expressive language specifically designed for structured text generation with Large Language Models (LLMs). Its primary purpose is to simplify and enhance the process of obtaining structured data (like JSON) from LLMs, moving beyond the challenges of traditional methods by providing robust parsing, error correction, and reliable function-calling capabilities.
BoundaryML.com introduces BAML, an expressive language specifically designed for structured text generation with Large Language Models (LLMs). Its primary purpose is to simplify and enhance the process of obtaining structured data (like JSON) from LLMs, moving beyond the challenges of traditional methods by providing robust parsing, error correction, and reliable function-calling capabilities.
BoundaryML.com introduces BAML, an expressive language specifically designed for structured text generation with Large Language Models (LLMs). Its primary purpose is to simplify and enhance the process of obtaining structured data (like JSON) from LLMs, moving beyond the challenges of traditional methods by providing robust parsing, error correction, and reliable function-calling capabilities.
Batteries Included is a self-hosted AI platform designed to provide the necessary infrastructure for building and deploying AI applications. Its primary purpose is to simplify the deployment of large language models (LLMs), vector databases, and Jupyter notebooks, offering enterprise-grade tools similar to those used by hyperscalers, but within a user's self-hosted environment.
Batteries Included is a self-hosted AI platform designed to provide the necessary infrastructure for building and deploying AI applications. Its primary purpose is to simplify the deployment of large language models (LLMs), vector databases, and Jupyter notebooks, offering enterprise-grade tools similar to those used by hyperscalers, but within a user's self-hosted environment.
Batteries Included is a self-hosted AI platform designed to provide the necessary infrastructure for building and deploying AI applications. Its primary purpose is to simplify the deployment of large language models (LLMs), vector databases, and Jupyter notebooks, offering enterprise-grade tools similar to those used by hyperscalers, but within a user's self-hosted environment.
Groq AppGen is an innovative, web-based tool that uses AI to generate and modify web applications in real-time. Powered by Groq's LLM API and the Llama 3.3 70B model, it allows users to create full-stack applications and components using simple, natural language queries. The platform's primary purpose is to dramatically accelerate the development process by generating code in milliseconds, providing an open-source solution for both developers and "no-code" users.
Groq AppGen is an innovative, web-based tool that uses AI to generate and modify web applications in real-time. Powered by Groq's LLM API and the Llama 3.3 70B model, it allows users to create full-stack applications and components using simple, natural language queries. The platform's primary purpose is to dramatically accelerate the development process by generating code in milliseconds, providing an open-source solution for both developers and "no-code" users.
Groq AppGen is an innovative, web-based tool that uses AI to generate and modify web applications in real-time. Powered by Groq's LLM API and the Llama 3.3 70B model, it allows users to create full-stack applications and components using simple, natural language queries. The platform's primary purpose is to dramatically accelerate the development process by generating code in milliseconds, providing an open-source solution for both developers and "no-code" users.
Solar Mini is Upstage’s compact, high-performance large language model (LLM) with under 30 billion parameters, engineered for exceptional speed and efficiency without sacrificing quality. It outperforms comparable models like Llama2, Mistral 7B, and Ko-Alpaca on major benchmarks, delivering responses similar to GPT-3.5 but 2.5 times faster. Thanks to its innovative Depth Up-scaling (DUS) and continued pre-training, Solar Mini is easily customized for domain-specific tasks, supports on-device deployment, and is especially suited for decentralized, responsive AI applications.
Solar Mini is Upstage’s compact, high-performance large language model (LLM) with under 30 billion parameters, engineered for exceptional speed and efficiency without sacrificing quality. It outperforms comparable models like Llama2, Mistral 7B, and Ko-Alpaca on major benchmarks, delivering responses similar to GPT-3.5 but 2.5 times faster. Thanks to its innovative Depth Up-scaling (DUS) and continued pre-training, Solar Mini is easily customized for domain-specific tasks, supports on-device deployment, and is especially suited for decentralized, responsive AI applications.
Solar Mini is Upstage’s compact, high-performance large language model (LLM) with under 30 billion parameters, engineered for exceptional speed and efficiency without sacrificing quality. It outperforms comparable models like Llama2, Mistral 7B, and Ko-Alpaca on major benchmarks, delivering responses similar to GPT-3.5 but 2.5 times faster. Thanks to its innovative Depth Up-scaling (DUS) and continued pre-training, Solar Mini is easily customized for domain-specific tasks, supports on-device deployment, and is especially suited for decentralized, responsive AI applications.
PromptsLabs is an open-source library of curated prompts designed to test and evaluate the performance of large language models (LLMs). It allows users to explore, contribute, and request prompts to better understand LLM capabilities.
PromptsLabs is an open-source library of curated prompts designed to test and evaluate the performance of large language models (LLMs). It allows users to explore, contribute, and request prompts to better understand LLM capabilities.
PromptsLabs is an open-source library of curated prompts designed to test and evaluate the performance of large language models (LLMs). It allows users to explore, contribute, and request prompts to better understand LLM capabilities.
LMArena is an open, crowdsourced platform for evaluating large language models (LLMs) based on human preferences. Rather than relying purely on automated benchmarks, it presents paired responses from different models to users, who vote for which is better. These votes build live leaderboards, revealing which models perform best in real-use scenarios. Key features include prompt-to-leaderboard comparison, transparent evaluation methods, style control for how responses are formatted, and auditability of feedback data. The platform is particularly valuable for researchers, developers, and AI labs that want to understand how their models compare when judged by real people, not just metrics.
LMArena is an open, crowdsourced platform for evaluating large language models (LLMs) based on human preferences. Rather than relying purely on automated benchmarks, it presents paired responses from different models to users, who vote for which is better. These votes build live leaderboards, revealing which models perform best in real-use scenarios. Key features include prompt-to-leaderboard comparison, transparent evaluation methods, style control for how responses are formatted, and auditability of feedback data. The platform is particularly valuable for researchers, developers, and AI labs that want to understand how their models compare when judged by real people, not just metrics.
LMArena is an open, crowdsourced platform for evaluating large language models (LLMs) based on human preferences. Rather than relying purely on automated benchmarks, it presents paired responses from different models to users, who vote for which is better. These votes build live leaderboards, revealing which models perform best in real-use scenarios. Key features include prompt-to-leaderboard comparison, transparent evaluation methods, style control for how responses are formatted, and auditability of feedback data. The platform is particularly valuable for researchers, developers, and AI labs that want to understand how their models compare when judged by real people, not just metrics.
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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