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Grok 3 is the latest flagship chatbot by Elon Musk’s xAI, described as "the world’s smartest AI." It was trained on a massive 200,000‑GPU supercomputer and offers tenfold more computing power than Grok 2. Equipped with two reasoning modes—Think and Big Brain—and featuring DeepSearch (a contextual web-and-X research tool), Grok 3 excels in math, science, coding, and truth-seeking tasks—all while offering fast, lively conversational style.


Grok 3 is the latest flagship chatbot by Elon Musk’s xAI, described as "the world’s smartest AI." It was trained on a massive 200,000‑GPU supercomputer and offers tenfold more computing power than Grok 2. Equipped with two reasoning modes—Think and Big Brain—and featuring DeepSearch (a contextual web-and-X research tool), Grok 3 excels in math, science, coding, and truth-seeking tasks—all while offering fast, lively conversational style.


Grok 3 is the latest flagship chatbot by Elon Musk’s xAI, described as "the world’s smartest AI." It was trained on a massive 200,000‑GPU supercomputer and offers tenfold more computing power than Grok 2. Equipped with two reasoning modes—Think and Big Brain—and featuring DeepSearch (a contextual web-and-X research tool), Grok 3 excels in math, science, coding, and truth-seeking tasks—all while offering fast, lively conversational style.


Gemini 2.5 Pro is Google DeepMind’s advanced hybrid-reasoning AI model, designed to think deeply before responding. With support for multimodal inputs—text, images, audio, video, and code—it offers lightning-fast inference performance, up to 2 million tokens of context, and top-tier results in math, science, and coding benchmarks.


Gemini 2.5 Pro is Google DeepMind’s advanced hybrid-reasoning AI model, designed to think deeply before responding. With support for multimodal inputs—text, images, audio, video, and code—it offers lightning-fast inference performance, up to 2 million tokens of context, and top-tier results in math, science, and coding benchmarks.


Gemini 2.5 Pro is Google DeepMind’s advanced hybrid-reasoning AI model, designed to think deeply before responding. With support for multimodal inputs—text, images, audio, video, and code—it offers lightning-fast inference performance, up to 2 million tokens of context, and top-tier results in math, science, and coding benchmarks.


Meta Llama 4 is the latest generation of Meta’s large language model series. It features a mixture-of-experts (MoE) architecture, making it both highly efficient and powerful. Llama 4 is natively multimodal—supporting text and image inputs—and offers three key variants: Scout (17B active parameters, 10 M token context), Maverick (17B active, 1 M token context), and Behemoth (288B active, 2 T total parameters; still in development). Designed for long-context reasoning, multilingual understanding, and open-weight availability (with license restrictions), Llama 4 excels in benchmarks and versatility.


Meta Llama 4 is the latest generation of Meta’s large language model series. It features a mixture-of-experts (MoE) architecture, making it both highly efficient and powerful. Llama 4 is natively multimodal—supporting text and image inputs—and offers three key variants: Scout (17B active parameters, 10 M token context), Maverick (17B active, 1 M token context), and Behemoth (288B active, 2 T total parameters; still in development). Designed for long-context reasoning, multilingual understanding, and open-weight availability (with license restrictions), Llama 4 excels in benchmarks and versatility.


Meta Llama 4 is the latest generation of Meta’s large language model series. It features a mixture-of-experts (MoE) architecture, making it both highly efficient and powerful. Llama 4 is natively multimodal—supporting text and image inputs—and offers three key variants: Scout (17B active parameters, 10 M token context), Maverick (17B active, 1 M token context), and Behemoth (288B active, 2 T total parameters; still in development). Designed for long-context reasoning, multilingual understanding, and open-weight availability (with license restrictions), Llama 4 excels in benchmarks and versatility.


DeepSeek‑R1 is the flagship reasoning-oriented AI model from Chinese startup DeepSeek. It’s an open-source, mixture-of-experts (MoE) model combining model weights clarity and chain-of-thought reasoning trained primarily through reinforcement learning. R1 delivers top-tier benchmark performance—on par with or surpassing OpenAI o1 in math, coding, and reasoning—while being significantly more cost-efficient.


DeepSeek‑R1 is the flagship reasoning-oriented AI model from Chinese startup DeepSeek. It’s an open-source, mixture-of-experts (MoE) model combining model weights clarity and chain-of-thought reasoning trained primarily through reinforcement learning. R1 delivers top-tier benchmark performance—on par with or surpassing OpenAI o1 in math, coding, and reasoning—while being significantly more cost-efficient.


DeepSeek‑R1 is the flagship reasoning-oriented AI model from Chinese startup DeepSeek. It’s an open-source, mixture-of-experts (MoE) model combining model weights clarity and chain-of-thought reasoning trained primarily through reinforcement learning. R1 delivers top-tier benchmark performance—on par with or surpassing OpenAI o1 in math, coding, and reasoning—while being significantly more cost-efficient.


Llama 4 Scout is Meta’s compact and high-performance entry in the Llama 4 family, released April 5, 2025. Built on a mixture-of-experts (MoE) architecture with 17B active parameters (109B total) and a staggering 10‑million-token context window, it delivers top-tier speed and long-context reasoning while fitting on a single Nvidia H100 GPU. It outperforms models like Google's Gemma 3, Gemini 2.0 Flash‑Lite, and Mistral 3.1 across benchmarks.


Llama 4 Scout is Meta’s compact and high-performance entry in the Llama 4 family, released April 5, 2025. Built on a mixture-of-experts (MoE) architecture with 17B active parameters (109B total) and a staggering 10‑million-token context window, it delivers top-tier speed and long-context reasoning while fitting on a single Nvidia H100 GPU. It outperforms models like Google's Gemma 3, Gemini 2.0 Flash‑Lite, and Mistral 3.1 across benchmarks.


Llama 4 Scout is Meta’s compact and high-performance entry in the Llama 4 family, released April 5, 2025. Built on a mixture-of-experts (MoE) architecture with 17B active parameters (109B total) and a staggering 10‑million-token context window, it delivers top-tier speed and long-context reasoning while fitting on a single Nvidia H100 GPU. It outperforms models like Google's Gemma 3, Gemini 2.0 Flash‑Lite, and Mistral 3.1 across benchmarks.

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.


Llama 3.3 is Meta’s instruction-tuned, text-only large language model released on December 6, 2024, available in a 70B-parameter size. It matches the performance of much larger models using significantly fewer parameters, is multilingual across eight key languages, and supports a massive 128,000-token context window—ideal for handling long-form documents, codebases, and detailed reasoning tasks.


Llama 3.3 is Meta’s instruction-tuned, text-only large language model released on December 6, 2024, available in a 70B-parameter size. It matches the performance of much larger models using significantly fewer parameters, is multilingual across eight key languages, and supports a massive 128,000-token context window—ideal for handling long-form documents, codebases, and detailed reasoning tasks.


Llama 3.3 is Meta’s instruction-tuned, text-only large language model released on December 6, 2024, available in a 70B-parameter size. It matches the performance of much larger models using significantly fewer parameters, is multilingual across eight key languages, and supports a massive 128,000-token context window—ideal for handling long-form documents, codebases, and detailed reasoning tasks.

Mistral Large 2 is the second-generation flagship model from Mistral AI, released in July 2024. Also referenced as mistral-large-2407, it’s a 123 B-parameter dense LLM with a 128 K-token context window, supporting dozens of languages and 80+ coding languages. It excels in reasoning, code generation, mathematics, instruction-following, and function calling—designed for high throughput on single-node setups.

Mistral Large 2 is the second-generation flagship model from Mistral AI, released in July 2024. Also referenced as mistral-large-2407, it’s a 123 B-parameter dense LLM with a 128 K-token context window, supporting dozens of languages and 80+ coding languages. It excels in reasoning, code generation, mathematics, instruction-following, and function calling—designed for high throughput on single-node setups.

Mistral Large 2 is the second-generation flagship model from Mistral AI, released in July 2024. Also referenced as mistral-large-2407, it’s a 123 B-parameter dense LLM with a 128 K-token context window, supporting dozens of languages and 80+ coding languages. It excels in reasoning, code generation, mathematics, instruction-following, and function calling—designed for high throughput on single-node setups.

Qwen Chat is Alibaba Cloud’s conversational AI assistant built on the Qwen series (e.g., Qwen‑7B‑Chat, Qwen1.5‑7B‑Chat, Qwen‑VL, Qwen‑Audio, and Qwen2.5‑Omni). It supports text, vision, audio, and video understanding, plus image and document processing, web search integration, and image generation—all through a unified chat interface.

Qwen Chat is Alibaba Cloud’s conversational AI assistant built on the Qwen series (e.g., Qwen‑7B‑Chat, Qwen1.5‑7B‑Chat, Qwen‑VL, Qwen‑Audio, and Qwen2.5‑Omni). It supports text, vision, audio, and video understanding, plus image and document processing, web search integration, and image generation—all through a unified chat interface.

Qwen Chat is Alibaba Cloud’s conversational AI assistant built on the Qwen series (e.g., Qwen‑7B‑Chat, Qwen1.5‑7B‑Chat, Qwen‑VL, Qwen‑Audio, and Qwen2.5‑Omni). It supports text, vision, audio, and video understanding, plus image and document processing, web search integration, and image generation—all through a unified chat interface.

Claude Code is an agentic coding assistant developed by Anthropic. Living in your terminal (or IDE), it comprehends your entire codebase and executes routine tasks—like writing code, debugging, explaining logic, and managing Git workflows—all via natural language commands .

Claude Code is an agentic coding assistant developed by Anthropic. Living in your terminal (or IDE), it comprehends your entire codebase and executes routine tasks—like writing code, debugging, explaining logic, and managing Git workflows—all via natural language commands .

Claude Code is an agentic coding assistant developed by Anthropic. Living in your terminal (or IDE), it comprehends your entire codebase and executes routine tasks—like writing code, debugging, explaining logic, and managing Git workflows—all via natural language commands .

"Thinking-Claude" is an innovative approach or methodology for interacting with the Claude AI. It emphasizes encouraging and revealing Claude's comprehensive thinking process and detailed inner monologue during everyday tasks and conversations. It's not a separate software tool or a new AI model, but rather a specific way of engaging with the existing Claude AI to gain deeper insights into its reasoning.

"Thinking-Claude" is an innovative approach or methodology for interacting with the Claude AI. It emphasizes encouraging and revealing Claude's comprehensive thinking process and detailed inner monologue during everyday tasks and conversations. It's not a separate software tool or a new AI model, but rather a specific way of engaging with the existing Claude AI to gain deeper insights into its reasoning.

"Thinking-Claude" is an innovative approach or methodology for interacting with the Claude AI. It emphasizes encouraging and revealing Claude's comprehensive thinking process and detailed inner monologue during everyday tasks and conversations. It's not a separate software tool or a new AI model, but rather a specific way of engaging with the existing Claude AI to gain deeper insights into its reasoning.
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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