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Allganize is a leading enterprise AI platform specializing in Large Language Model (LLM) solutions to enhance business efficiency and growth. It offers a comprehensive suite of AI-powered tools, including an LLM App Builder, Cognitive Search, and AI Answer Bot, designed to automate processes, improve data handling, and optimize customer support.
Allganize is a leading enterprise AI platform specializing in Large Language Model (LLM) solutions to enhance business efficiency and growth. It offers a comprehensive suite of AI-powered tools, including an LLM App Builder, Cognitive Search, and AI Answer Bot, designed to automate processes, improve data handling, and optimize customer support.
Allganize is a leading enterprise AI platform specializing in Large Language Model (LLM) solutions to enhance business efficiency and growth. It offers a comprehensive suite of AI-powered tools, including an LLM App Builder, Cognitive Search, and AI Answer Bot, designed to automate processes, improve data handling, and optimize customer support.
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‑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.
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.
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 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 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.
Llama 3.2 is Meta’s multimodal and lightweight update to its Llama 3 line, released on September 25, 2024. The family includes 1B and 3B text-only models optimized for edge devices, as well as 11B and 90B Vision models capable of image understanding. It offers a 128K-token context window, Grouped-Query Attention for efficient inference, and opens up on-device, private AI with strong multilingual (e.g. Hindi, Spanish) support.
Llama 3.2 is Meta’s multimodal and lightweight update to its Llama 3 line, released on September 25, 2024. The family includes 1B and 3B text-only models optimized for edge devices, as well as 11B and 90B Vision models capable of image understanding. It offers a 128K-token context window, Grouped-Query Attention for efficient inference, and opens up on-device, private AI with strong multilingual (e.g. Hindi, Spanish) support.
Llama 3.2 is Meta’s multimodal and lightweight update to its Llama 3 line, released on September 25, 2024. The family includes 1B and 3B text-only models optimized for edge devices, as well as 11B and 90B Vision models capable of image understanding. It offers a 128K-token context window, Grouped-Query Attention for efficient inference, and opens up on-device, private AI with strong multilingual (e.g. Hindi, Spanish) support.
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 0528 is the May 28, 2025 update to DeepSeek’s flagship reasoning model. It brings significantly enhanced benchmark performance, deeper chain-of-thought reasoning (now using ~23K tokens per problem), reduced hallucinations, and support for JSON output, function calling, multi-round chat, and context caching.
DeepSeek R1 0528 is the May 28, 2025 update to DeepSeek’s flagship reasoning model. It brings significantly enhanced benchmark performance, deeper chain-of-thought reasoning (now using ~23K tokens per problem), reduced hallucinations, and support for JSON output, function calling, multi-round chat, and context caching.
DeepSeek R1 0528 is the May 28, 2025 update to DeepSeek’s flagship reasoning model. It brings significantly enhanced benchmark performance, deeper chain-of-thought reasoning (now using ~23K tokens per problem), reduced hallucinations, and support for JSON output, function calling, multi-round chat, and context caching.
Codestral 25.01 is Mistral AI’s upgraded code-generation model, released January 13, 2025. Featuring a more efficient architecture and improved tokenizer, it delivers code completion and intelligence about 2× faster than its predecessor, with support for fill-in-the-middle (FIM), code correction, test generation, and proficiency in over 80 programming languages, all within a 256K-token context window.
Codestral 25.01 is Mistral AI’s upgraded code-generation model, released January 13, 2025. Featuring a more efficient architecture and improved tokenizer, it delivers code completion and intelligence about 2× faster than its predecessor, with support for fill-in-the-middle (FIM), code correction, test generation, and proficiency in over 80 programming languages, all within a 256K-token context window.
Codestral 25.01 is Mistral AI’s upgraded code-generation model, released January 13, 2025. Featuring a more efficient architecture and improved tokenizer, it delivers code completion and intelligence about 2× faster than its predecessor, with support for fill-in-the-middle (FIM), code correction, test generation, and proficiency in over 80 programming languages, all within a 256K-token context window.
Mistral Nemotron is a preview large language model, jointly developed by Mistral AI and NVIDIA, released on June 11, 2025. Optimized by NVIDIA for inference using TensorRT-LLM and vLLM, it supports a massive 128K-token context window and is built for agentic workflows—excelling in instruction-following, function calling, and code generation—while delivering state-of-the-art performance across reasoning, math, coding, and multilingual benchmarks.
Mistral Nemotron is a preview large language model, jointly developed by Mistral AI and NVIDIA, released on June 11, 2025. Optimized by NVIDIA for inference using TensorRT-LLM and vLLM, it supports a massive 128K-token context window and is built for agentic workflows—excelling in instruction-following, function calling, and code generation—while delivering state-of-the-art performance across reasoning, math, coding, and multilingual benchmarks.
Mistral Nemotron is a preview large language model, jointly developed by Mistral AI and NVIDIA, released on June 11, 2025. Optimized by NVIDIA for inference using TensorRT-LLM and vLLM, it supports a massive 128K-token context window and is built for agentic workflows—excelling in instruction-following, function calling, and code generation—while delivering state-of-the-art performance across reasoning, math, coding, and multilingual benchmarks.
UsageGuard is an AI infrastructure platform designed to help businesses build, deploy, and monitor AI applications with confidence. It acts as a proxy service for Large Language Model (LLM) API calls, providing a unified endpoint that offers a suite of enterprise-grade features. Its core mission is to empower developers and enterprises with robust solutions for AI security, cost control, usage tracking, and comprehensive observability.
UsageGuard is an AI infrastructure platform designed to help businesses build, deploy, and monitor AI applications with confidence. It acts as a proxy service for Large Language Model (LLM) API calls, providing a unified endpoint that offers a suite of enterprise-grade features. Its core mission is to empower developers and enterprises with robust solutions for AI security, cost control, usage tracking, and comprehensive observability.
UsageGuard is an AI infrastructure platform designed to help businesses build, deploy, and monitor AI applications with confidence. It acts as a proxy service for Large Language Model (LLM) API calls, providing a unified endpoint that offers a suite of enterprise-grade features. Its core mission is to empower developers and enterprises with robust solutions for AI security, cost control, usage tracking, and comprehensive observability.
LLM Gateway is a unified API gateway designed to simplify working with large language models (LLMs) from multiple providers by offering a single, OpenAI-compatible endpoint. Whether using OpenAI, Anthropic, Google Vertex AI, or others, developers can route, monitor, and manage requests—all without altering existing code. Available as an open-source self-hosted option (MIT-licensed) or hosted service, it combines powerful features for analytics, cost optimization, and performance management—all under one roof.
LLM Gateway is a unified API gateway designed to simplify working with large language models (LLMs) from multiple providers by offering a single, OpenAI-compatible endpoint. Whether using OpenAI, Anthropic, Google Vertex AI, or others, developers can route, monitor, and manage requests—all without altering existing code. Available as an open-source self-hosted option (MIT-licensed) or hosted service, it combines powerful features for analytics, cost optimization, and performance management—all under one roof.
LLM Gateway is a unified API gateway designed to simplify working with large language models (LLMs) from multiple providers by offering a single, OpenAI-compatible endpoint. Whether using OpenAI, Anthropic, Google Vertex AI, or others, developers can route, monitor, and manage requests—all without altering existing code. Available as an open-source self-hosted option (MIT-licensed) or hosted service, it combines powerful features for analytics, cost optimization, and performance management—all under one roof.
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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