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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.


Claude 3 Opus is Anthropic’s flagship Claude 3 model, released March 4, 2024. It offers top-tier performance for deep reasoning, complex code, advanced math, and multimodal understanding—including charts and documents—supported by a 200K‑token context window (extendable to 1 million in select enterprise cases). It consistently outperforms GPT‑4 and Gemini Ultra on benchmark tests like MMLU, HumanEval, HellaSwag, and more.


Claude 3 Opus is Anthropic’s flagship Claude 3 model, released March 4, 2024. It offers top-tier performance for deep reasoning, complex code, advanced math, and multimodal understanding—including charts and documents—supported by a 200K‑token context window (extendable to 1 million in select enterprise cases). It consistently outperforms GPT‑4 and Gemini Ultra on benchmark tests like MMLU, HumanEval, HellaSwag, and more.


Claude 3 Opus is Anthropic’s flagship Claude 3 model, released March 4, 2024. It offers top-tier performance for deep reasoning, complex code, advanced math, and multimodal understanding—including charts and documents—supported by a 200K‑token context window (extendable to 1 million in select enterprise cases). It consistently outperforms GPT‑4 and Gemini Ultra on benchmark tests like MMLU, HumanEval, HellaSwag, and more.


Grok 3 is xAI’s newest flagship AI chatbot, released on February 17, 2025, running on the massive Colossus supercluster (~200,000 GPUs). It offers elite-level reasoning, chain-of-thought transparency (“Think” mode), advanced “Big Brain” deeper reasoning, multimodal support (text, images), and integrated real-time DeepSearch—positioning it as a top-tier competitor to GPT‑4o, Gemini, Claude, and DeepSeek V3 on benchmarks.


Grok 3 is xAI’s newest flagship AI chatbot, released on February 17, 2025, running on the massive Colossus supercluster (~200,000 GPUs). It offers elite-level reasoning, chain-of-thought transparency (“Think” mode), advanced “Big Brain” deeper reasoning, multimodal support (text, images), and integrated real-time DeepSearch—positioning it as a top-tier competitor to GPT‑4o, Gemini, Claude, and DeepSeek V3 on benchmarks.


Grok 3 is xAI’s newest flagship AI chatbot, released on February 17, 2025, running on the massive Colossus supercluster (~200,000 GPUs). It offers elite-level reasoning, chain-of-thought transparency (“Think” mode), advanced “Big Brain” deeper reasoning, multimodal support (text, images), and integrated real-time DeepSearch—positioning it as a top-tier competitor to GPT‑4o, Gemini, Claude, and DeepSeek V3 on benchmarks.


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 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 3.2 Vision is Meta’s first open-source multimodal Llama model series, released on September 25, 2024. Available in 11 B and 90 B parameter sizes, it merges advanced image understanding with a massive 128 K‑token text context. Optimized for vision reasoning, captioning, document QA, and visual math tasks, it outperforms many closed-source multimodal models.


Llama 3.2 Vision is Meta’s first open-source multimodal Llama model series, released on September 25, 2024. Available in 11 B and 90 B parameter sizes, it merges advanced image understanding with a massive 128 K‑token text context. Optimized for vision reasoning, captioning, document QA, and visual math tasks, it outperforms many closed-source multimodal models.


Llama 3.2 Vision is Meta’s first open-source multimodal Llama model series, released on September 25, 2024. Available in 11 B and 90 B parameter sizes, it merges advanced image understanding with a massive 128 K‑token text context. Optimized for vision reasoning, captioning, document QA, and visual math tasks, it outperforms many closed-source multimodal models.


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.


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 Document AI is Mistral AI’s enterprise-grade document processing platform, launched May 2025. It combines state-of-the-art OCR model mistral-ocr-latest with structured data extraction, document Q&A, and natural language understanding—delivering 99%+ OCR accuracy, support for over 40 languages and complex layouts (tables, forms, handwriting), and blazing-fast processing at up to 2,000 pages/min per GPU.

Mistral Document AI is Mistral AI’s enterprise-grade document processing platform, launched May 2025. It combines state-of-the-art OCR model mistral-ocr-latest with structured data extraction, document Q&A, and natural language understanding—delivering 99%+ OCR accuracy, support for over 40 languages and complex layouts (tables, forms, handwriting), and blazing-fast processing at up to 2,000 pages/min per GPU.

Mistral Document AI is Mistral AI’s enterprise-grade document processing platform, launched May 2025. It combines state-of-the-art OCR model mistral-ocr-latest with structured data extraction, document Q&A, and natural language understanding—delivering 99%+ OCR accuracy, support for over 40 languages and complex layouts (tables, forms, handwriting), and blazing-fast processing at up to 2,000 pages/min per GPU.

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.

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.


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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