
$ 0.00
Custom
Proud of the love you're getting? Show off your AI Toolbook reviews—then invite more fans to share the love and build your credibility.
Add an AI Toolbook badge to your site—an easy way to drive followers, showcase updates, and collect reviews. It's like a mini 24/7 billboard for your AI.

GPT-4.1 Mini is a lightweight version of OpenAI’s advanced GPT-4.1 model, designed for efficiency, speed, and affordability without compromising much on performance. Tailored for developers and teams who need capable AI reasoning and natural language processing in smaller-scale or cost-sensitive applications, GPT-4.1 Mini brings the power of GPT-4.1 into a more accessible form factor. Perfect for chatbots, content suggestions, productivity tools, and streamlined AI experiences, this compact model still delivers impressive accuracy, fast responses, and a reliable understanding of nuanced prompts—all while using fewer resources.


GPT-4.1 Mini is a lightweight version of OpenAI’s advanced GPT-4.1 model, designed for efficiency, speed, and affordability without compromising much on performance. Tailored for developers and teams who need capable AI reasoning and natural language processing in smaller-scale or cost-sensitive applications, GPT-4.1 Mini brings the power of GPT-4.1 into a more accessible form factor. Perfect for chatbots, content suggestions, productivity tools, and streamlined AI experiences, this compact model still delivers impressive accuracy, fast responses, and a reliable understanding of nuanced prompts—all while using fewer resources.


GPT-4.1 Mini is a lightweight version of OpenAI’s advanced GPT-4.1 model, designed for efficiency, speed, and affordability without compromising much on performance. Tailored for developers and teams who need capable AI reasoning and natural language processing in smaller-scale or cost-sensitive applications, GPT-4.1 Mini brings the power of GPT-4.1 into a more accessible form factor. Perfect for chatbots, content suggestions, productivity tools, and streamlined AI experiences, this compact model still delivers impressive accuracy, fast responses, and a reliable understanding of nuanced prompts—all while using fewer resources.


GPT-4.1 Nano is OpenAI’s smallest and most efficient language model in the GPT-4.1 family, designed to deliver ultra-fast, ultra-cheap, and surprisingly capable natural language responses. Though compact in size, GPT-4.1 Nano handles lightweight NLP tasks with impressive speed and minimal resource consumption, making it perfect for mobile apps, edge computing, and large-scale deployments with cost sensitivity. It’s built for real-time applications and use cases where milliseconds matter, and budgets are tight—yet you still want a taste of OpenAI-grade intelligence.


GPT-4.1 Nano is OpenAI’s smallest and most efficient language model in the GPT-4.1 family, designed to deliver ultra-fast, ultra-cheap, and surprisingly capable natural language responses. Though compact in size, GPT-4.1 Nano handles lightweight NLP tasks with impressive speed and minimal resource consumption, making it perfect for mobile apps, edge computing, and large-scale deployments with cost sensitivity. It’s built for real-time applications and use cases where milliseconds matter, and budgets are tight—yet you still want a taste of OpenAI-grade intelligence.


GPT-4.1 Nano is OpenAI’s smallest and most efficient language model in the GPT-4.1 family, designed to deliver ultra-fast, ultra-cheap, and surprisingly capable natural language responses. Though compact in size, GPT-4.1 Nano handles lightweight NLP tasks with impressive speed and minimal resource consumption, making it perfect for mobile apps, edge computing, and large-scale deployments with cost sensitivity. It’s built for real-time applications and use cases where milliseconds matter, and budgets are tight—yet you still want a taste of OpenAI-grade intelligence.


Grok 2 is xAI’s second-generation chatbot that extends Grok’s capabilities to include real-time web access, multimodal output (text, vision, image generation via FLUX.1), and improved reasoning performance. It’s available to X Premium and Premium+ users and through xAI’s enterprise API.


Grok 2 is xAI’s second-generation chatbot that extends Grok’s capabilities to include real-time web access, multimodal output (text, vision, image generation via FLUX.1), and improved reasoning performance. It’s available to X Premium and Premium+ users and through xAI’s enterprise API.


Grok 2 is xAI’s second-generation chatbot that extends Grok’s capabilities to include real-time web access, multimodal output (text, vision, image generation via FLUX.1), and improved reasoning performance. It’s available to X Premium and Premium+ users and through xAI’s enterprise API.


DeepSeek V3 is the latest flagship Mixture‑of‑Experts (MoE) open‑source AI model from DeepSeek. It features 671 billion total parameters (with ~37 billion activated per token), supports up to 128K context length, and excels across reasoning, code generation, language, and multimodal tasks. On standard benchmarks, it rivals or exceeds proprietary models—including GPT‑4o and Claude 3.5—as a high-performance, cost-efficient alternative.


DeepSeek V3 is the latest flagship Mixture‑of‑Experts (MoE) open‑source AI model from DeepSeek. It features 671 billion total parameters (with ~37 billion activated per token), supports up to 128K context length, and excels across reasoning, code generation, language, and multimodal tasks. On standard benchmarks, it rivals or exceeds proprietary models—including GPT‑4o and Claude 3.5—as a high-performance, cost-efficient alternative.


DeepSeek V3 is the latest flagship Mixture‑of‑Experts (MoE) open‑source AI model from DeepSeek. It features 671 billion total parameters (with ~37 billion activated per token), supports up to 128K context length, and excels across reasoning, code generation, language, and multimodal tasks. On standard benchmarks, it rivals or exceeds proprietary models—including GPT‑4o and Claude 3.5—as a high-performance, cost-efficient alternative.


DeepSeek V3 (0324) is the latest open-source Mixture-of-Experts (MoE) language model from DeepSeek, featuring 671B parameters (37B active per token). Released in March 2025 under the MIT license, it builds on DeepSeek V3 with major enhancements in reasoning, coding, front-end generation, and Chinese proficiency. It maintains cost-efficiency and function-calling support.


DeepSeek V3 (0324) is the latest open-source Mixture-of-Experts (MoE) language model from DeepSeek, featuring 671B parameters (37B active per token). Released in March 2025 under the MIT license, it builds on DeepSeek V3 with major enhancements in reasoning, coding, front-end generation, and Chinese proficiency. It maintains cost-efficiency and function-calling support.


DeepSeek V3 (0324) is the latest open-source Mixture-of-Experts (MoE) language model from DeepSeek, featuring 671B parameters (37B active per token). Released in March 2025 under the MIT license, it builds on DeepSeek V3 with major enhancements in reasoning, coding, front-end generation, and Chinese proficiency. It maintains cost-efficiency and function-calling support.


Grok 3 Fast is xAI’s speed-optimized variant of their flagship Grok 3 model, offering identical output quality with lower latency. It leverages the same underlying architecture—including multimodal input, chain-of-thought reasoning, and large context—but serves through optimized infrastructure for real-time responsiveness. It supports up to 131,072 tokens of context.


Grok 3 Fast is xAI’s speed-optimized variant of their flagship Grok 3 model, offering identical output quality with lower latency. It leverages the same underlying architecture—including multimodal input, chain-of-thought reasoning, and large context—but serves through optimized infrastructure for real-time responsiveness. It supports up to 131,072 tokens of context.


Grok 3 Fast is xAI’s speed-optimized variant of their flagship Grok 3 model, offering identical output quality with lower latency. It leverages the same underlying architecture—including multimodal input, chain-of-thought reasoning, and large context—but serves through optimized infrastructure for real-time responsiveness. It supports up to 131,072 tokens of context.


Llama 4 Maverick is Meta’s powerful mid-sized model in the Llama 4 series, released April 5, 2025. Built with a mixture-of-experts (MoE) architecture featuring 17 B active parameters (out of 400 B total) and 128 experts, it supports a 1 million-token context window and native multimodality for text and image inputs. It ranks near the top of competitive benchmarks—surpassing GPT‑4o and Gemini 2.0 Flash in reasoning, coding, and visual tasks.


Llama 4 Maverick is Meta’s powerful mid-sized model in the Llama 4 series, released April 5, 2025. Built with a mixture-of-experts (MoE) architecture featuring 17 B active parameters (out of 400 B total) and 128 experts, it supports a 1 million-token context window and native multimodality for text and image inputs. It ranks near the top of competitive benchmarks—surpassing GPT‑4o and Gemini 2.0 Flash in reasoning, coding, and visual tasks.


Llama 4 Maverick is Meta’s powerful mid-sized model in the Llama 4 series, released April 5, 2025. Built with a mixture-of-experts (MoE) architecture featuring 17 B active parameters (out of 400 B total) and 128 experts, it supports a 1 million-token context window and native multimodality for text and image inputs. It ranks near the top of competitive benchmarks—surpassing GPT‑4o and Gemini 2.0 Flash in reasoning, coding, and visual tasks.


Llama 4 Behemoth is Meta’s ultimate “teacher” model within the Llama 4 series, currently in preview and training. Featuring an enormous 2 trillion total parameters with 288 billion active in a Mixture-of-Experts architecture (16 experts), it's designed to push the limits of multimodal reasoning, STEM, and long-context tasks. Initially slated for April 2025, its release has been postponed to fall 2025 or later due to internal performance and alignment concerns.


Llama 4 Behemoth is Meta’s ultimate “teacher” model within the Llama 4 series, currently in preview and training. Featuring an enormous 2 trillion total parameters with 288 billion active in a Mixture-of-Experts architecture (16 experts), it's designed to push the limits of multimodal reasoning, STEM, and long-context tasks. Initially slated for April 2025, its release has been postponed to fall 2025 or later due to internal performance and alignment concerns.


Llama 4 Behemoth is Meta’s ultimate “teacher” model within the Llama 4 series, currently in preview and training. Featuring an enormous 2 trillion total parameters with 288 billion active in a Mixture-of-Experts architecture (16 experts), it's designed to push the limits of multimodal reasoning, STEM, and long-context tasks. Initially slated for April 2025, its release has been postponed to fall 2025 or later due to internal performance and alignment concerns.


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 Distill Qwen‑32B is a 32-billion-parameter dense reasoning model released in early 2025. Distilled from the flagship DeepSeek R1 using Qwen 2.5‑32B as a base, it delivers state-of-the-art performance among dense LLMs—outperforming OpenAI’s o1‑mini on benchmarks like AIME, MATH‑500, GPQA Diamond, LiveCodeBench, and CodeForces rating.


DeepSeek R1 Distill Qwen‑32B is a 32-billion-parameter dense reasoning model released in early 2025. Distilled from the flagship DeepSeek R1 using Qwen 2.5‑32B as a base, it delivers state-of-the-art performance among dense LLMs—outperforming OpenAI’s o1‑mini on benchmarks like AIME, MATH‑500, GPQA Diamond, LiveCodeBench, and CodeForces rating.


DeepSeek R1 Distill Qwen‑32B is a 32-billion-parameter dense reasoning model released in early 2025. Distilled from the flagship DeepSeek R1 using Qwen 2.5‑32B as a base, it delivers state-of-the-art performance among dense LLMs—outperforming OpenAI’s o1‑mini on benchmarks like AIME, MATH‑500, GPQA Diamond, LiveCodeBench, and CodeForces rating.


Soket AI is an Indian deep-tech startup building sovereign, multilingual foundational AI models and real-time voice/speech APIs designed for Indic languages and global scale. By focusing on language diversity, cultural context and ethical AI, Soket AI aims to develop models that recognise and respond across many languages, while delivering enterprise-grade capabilities for sectors such as defence, healthcare, education and governance.


Soket AI is an Indian deep-tech startup building sovereign, multilingual foundational AI models and real-time voice/speech APIs designed for Indic languages and global scale. By focusing on language diversity, cultural context and ethical AI, Soket AI aims to develop models that recognise and respond across many languages, while delivering enterprise-grade capabilities for sectors such as defence, healthcare, education and governance.


Soket AI is an Indian deep-tech startup building sovereign, multilingual foundational AI models and real-time voice/speech APIs designed for Indic languages and global scale. By focusing on language diversity, cultural context and ethical AI, Soket AI aims to develop models that recognise and respond across many languages, while delivering enterprise-grade capabilities for sectors such as defence, healthcare, education and governance.


LangChain is a powerful open-source framework designed to help developers build context-aware applications that leverage large language models (LLMs). It allows users to connect language models to various data sources, APIs, and memory components, enabling intelligent, multi-step reasoning and decision-making processes. LangChain supports both Python and JavaScript, providing modular building blocks for developers to create chatbots, AI assistants, retrieval-augmented generation (RAG) systems, and agent-based tools. The framework is widely adopted across industries for its flexibility in connecting structured and unstructured data with LLMs.


LangChain is a powerful open-source framework designed to help developers build context-aware applications that leverage large language models (LLMs). It allows users to connect language models to various data sources, APIs, and memory components, enabling intelligent, multi-step reasoning and decision-making processes. LangChain supports both Python and JavaScript, providing modular building blocks for developers to create chatbots, AI assistants, retrieval-augmented generation (RAG) systems, and agent-based tools. The framework is widely adopted across industries for its flexibility in connecting structured and unstructured data with LLMs.


LangChain is a powerful open-source framework designed to help developers build context-aware applications that leverage large language models (LLMs). It allows users to connect language models to various data sources, APIs, and memory components, enabling intelligent, multi-step reasoning and decision-making processes. LangChain supports both Python and JavaScript, providing modular building blocks for developers to create chatbots, AI assistants, retrieval-augmented generation (RAG) systems, and agent-based tools. The framework is widely adopted across industries for its flexibility in connecting structured and unstructured data with LLMs.
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.
If you have any suggestions or questions, email us at hello@aitoolbook.ai