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Gemini 2.5 Flash‑Lite is Google's most cost-efficient and lowest-latency variant in the Gemini 2.5 family, currently available in preview. It’s designed for high-throughput tasks like classification, summarization, and translation, delivering exceptional performance—better than former Flash‑Lite versions—while offering developer control over reasoning depth via a “thinking budget” toggle .


Gemini 2.5 Flash‑Lite is Google's most cost-efficient and lowest-latency variant in the Gemini 2.5 family, currently available in preview. It’s designed for high-throughput tasks like classification, summarization, and translation, delivering exceptional performance—better than former Flash‑Lite versions—while offering developer control over reasoning depth via a “thinking budget” toggle .


Gemini 2.5 Flash‑Lite is Google's most cost-efficient and lowest-latency variant in the Gemini 2.5 family, currently available in preview. It’s designed for high-throughput tasks like classification, summarization, and translation, delivering exceptional performance—better than former Flash‑Lite versions—while offering developer control over reasoning depth via a “thinking budget” toggle .


Meta Llama 3 is Meta’s third-generation open-weight large language model family, released in April 2024 and enhanced in July 2024 with the 3.1 update. It spans three sizes—8B, 70B, and 405B parameters—each offering a 128K‑token context window. Llama 3 excels at reasoning, code generation, multilingual text, and instruction-following, and introduces multimodal vision (image understanding) capabilities in its 3.2 series. Robust safety mechanisms like Llama Guard 3, Code Shield, and CyberSec Eval 2 ensure responsible output.


Meta Llama 3 is Meta’s third-generation open-weight large language model family, released in April 2024 and enhanced in July 2024 with the 3.1 update. It spans three sizes—8B, 70B, and 405B parameters—each offering a 128K‑token context window. Llama 3 excels at reasoning, code generation, multilingual text, and instruction-following, and introduces multimodal vision (image understanding) capabilities in its 3.2 series. Robust safety mechanisms like Llama Guard 3, Code Shield, and CyberSec Eval 2 ensure responsible output.


Meta Llama 3 is Meta’s third-generation open-weight large language model family, released in April 2024 and enhanced in July 2024 with the 3.1 update. It spans three sizes—8B, 70B, and 405B parameters—each offering a 128K‑token context window. Llama 3 excels at reasoning, code generation, multilingual text, and instruction-following, and introduces multimodal vision (image understanding) capabilities in its 3.2 series. Robust safety mechanisms like Llama Guard 3, Code Shield, and CyberSec Eval 2 ensure responsible output.


DeepSeek V2 is an open-source, Mixture‑of‑Experts (MoE) language model developed by DeepSeek-AI, released in May 2024. It features a massive 236 B total parameters with approximately 21 B activated per token, supports up to 128 K token context, and adopts innovative MLA (Multi‑head Latent Attention) and sparse expert routing. DeepSeek V2 delivers top-tier performance on benchmarks while cutting training and inference costs significantly.


DeepSeek V2 is an open-source, Mixture‑of‑Experts (MoE) language model developed by DeepSeek-AI, released in May 2024. It features a massive 236 B total parameters with approximately 21 B activated per token, supports up to 128 K token context, and adopts innovative MLA (Multi‑head Latent Attention) and sparse expert routing. DeepSeek V2 delivers top-tier performance on benchmarks while cutting training and inference costs significantly.


DeepSeek V2 is an open-source, Mixture‑of‑Experts (MoE) language model developed by DeepSeek-AI, released in May 2024. It features a massive 236 B total parameters with approximately 21 B activated per token, supports up to 128 K token context, and adopts innovative MLA (Multi‑head Latent Attention) and sparse expert routing. DeepSeek V2 delivers top-tier performance on benchmarks while cutting training and inference costs significantly.


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 low-latency variant of their flagship Grok 3 model. It delivers identical output quality but responds faster by leveraging optimized serving infrastructure—ideal for real-time, speed-sensitive applications. It inherits the same multimodal, reasoning, and chain-of-thought capabilities as Grok 3, with a large context window of ~131K tokens.


Grok 3 Fast is xAI’s low-latency variant of their flagship Grok 3 model. It delivers identical output quality but responds faster by leveraging optimized serving infrastructure—ideal for real-time, speed-sensitive applications. It inherits the same multimodal, reasoning, and chain-of-thought capabilities as Grok 3, with a large context window of ~131K tokens.


Grok 3 Fast is xAI’s low-latency variant of their flagship Grok 3 model. It delivers identical output quality but responds faster by leveraging optimized serving infrastructure—ideal for real-time, speed-sensitive applications. It inherits the same multimodal, reasoning, and chain-of-thought capabilities as Grok 3, with a large context window of ~131K tokens.


Llama 3.1 is Meta’s most advanced open-source Llama 3 model, released on July 23, 2024. It comes in three sizes—8B, 70B, and 405B parameters—with an expanded 128K-token context window and improved multilingual and multimodal capabilities. It significantly outperforms Llama 3 and rivals proprietary models across benchmarks like GSM8K, MMLU, HumanEval, ARC, and tool-augmented reasoning tasks.


Llama 3.1 is Meta’s most advanced open-source Llama 3 model, released on July 23, 2024. It comes in three sizes—8B, 70B, and 405B parameters—with an expanded 128K-token context window and improved multilingual and multimodal capabilities. It significantly outperforms Llama 3 and rivals proprietary models across benchmarks like GSM8K, MMLU, HumanEval, ARC, and tool-augmented reasoning tasks.


Llama 3.1 is Meta’s most advanced open-source Llama 3 model, released on July 23, 2024. It comes in three sizes—8B, 70B, and 405B parameters—with an expanded 128K-token context window and improved multilingual and multimodal capabilities. It significantly outperforms Llama 3 and rivals proprietary models across benchmarks like GSM8K, MMLU, HumanEval, ARC, and tool-augmented 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.


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.


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 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.

Mistral Small 3.1 is the March 17, 2025 update to Mistral AI's open-source 24B-parameter small model. It offers instruction-following, multimodal vision understanding, and an expanded 128K-token context window, delivering performance on par with or better than GPT‑4o Mini, Gemma 3, and Claude 3.5 Haiku—all while maintaining fast inference speeds (~150 tokens/sec) and running on devices like an RTX 4090 or a 32 GB Mac.

Mistral Small 3.1 is the March 17, 2025 update to Mistral AI's open-source 24B-parameter small model. It offers instruction-following, multimodal vision understanding, and an expanded 128K-token context window, delivering performance on par with or better than GPT‑4o Mini, Gemma 3, and Claude 3.5 Haiku—all while maintaining fast inference speeds (~150 tokens/sec) and running on devices like an RTX 4090 or a 32 GB Mac.

Mistral Small 3.1 is the March 17, 2025 update to Mistral AI's open-source 24B-parameter small model. It offers instruction-following, multimodal vision understanding, and an expanded 128K-token context window, delivering performance on par with or better than GPT‑4o Mini, Gemma 3, and Claude 3.5 Haiku—all while maintaining fast inference speeds (~150 tokens/sec) and running on devices like an RTX 4090 or a 32 GB Mac.

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.

"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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