Kimi K2 Review - Everything You Need to Know

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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

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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

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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 VL

DeepSeek VL is DeepSeek’s open-source vision-language model designed for real-world multimodal understanding. It employs a hybrid vision encoder (SigLIP‑L + SAM), processes high-resolution images (up to 1024×1024), and supports both base and chat variants across two sizes: 1.3B and 7B parameters. It excels on tasks like OCR, diagram reasoning, webpage parsing, and visual Q&A—while preserving strong language ability.

DeepSeek VL

DeepSeek VL is DeepSeek’s open-source vision-language model designed for real-world multimodal understanding. It employs a hybrid vision encoder (SigLIP‑L + SAM), processes high-resolution images (up to 1024×1024), and supports both base and chat variants across two sizes: 1.3B and 7B parameters. It excels on tasks like OCR, diagram reasoning, webpage parsing, and visual Q&A—while preserving strong language ability.

DeepSeek VL

DeepSeek VL is DeepSeek’s open-source vision-language model designed for real-world multimodal understanding. It employs a hybrid vision encoder (SigLIP‑L + SAM), processes high-resolution images (up to 1024×1024), and supports both base and chat variants across two sizes: 1.3B and 7B parameters. It excels on tasks like OCR, diagram reasoning, webpage parsing, and visual Q&A—while preserving strong language ability.

DeepSeek-Coder-V2

DeepSeek‑Coder V2 is an open-source, Mixture‑of‑Experts (MoE) code-focused variant of DeepSeek‑V2, purpose-built for code generation, completion, debugging, and mathematical reasoning. Trained with an additional 6 trillion tokens of code and text, it supports up to 338 programming languages and a massive 128K‑token context window, rivaling or exceeding commercial code models in performance.

DeepSeek-Coder-V2

DeepSeek‑Coder V2 is an open-source, Mixture‑of‑Experts (MoE) code-focused variant of DeepSeek‑V2, purpose-built for code generation, completion, debugging, and mathematical reasoning. Trained with an additional 6 trillion tokens of code and text, it supports up to 338 programming languages and a massive 128K‑token context window, rivaling or exceeding commercial code models in performance.

DeepSeek-Coder-V2

DeepSeek‑Coder V2 is an open-source, Mixture‑of‑Experts (MoE) code-focused variant of DeepSeek‑V2, purpose-built for code generation, completion, debugging, and mathematical reasoning. Trained with an additional 6 trillion tokens of code and text, it supports up to 338 programming languages and a massive 128K‑token context window, rivaling or exceeding commercial code models in performance.

grok-3-fast

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

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

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-latest

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-latest

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-latest

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.

Meta Llama 3.1

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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.

Meta Llama 3.1

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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.

Meta Llama 3.1

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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.

Meta Llama 3.3

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.

Meta Llama 3.3

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.

Meta Llama 3.3

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 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.