EXAONE Deep Description
EXAONE Deep represents a collection of advanced language models that are enhanced for reasoning, created by LG AI Research, and come in sizes of 2.4 billion, 7.8 billion, and 32 billion parameters. These models excel in a variety of reasoning challenges, particularly in areas such as mathematics and coding assessments. Significantly, the EXAONE Deep 2.4B model outshines other models of its size, while the 7.8B variant outperforms both open-weight models of similar dimensions and the proprietary reasoning model known as OpenAI o1-mini. Furthermore, the EXAONE Deep 32B model competes effectively with top-tier open-weight models in the field. The accompanying repository offers extensive documentation that includes performance assessments, quick-start guides for leveraging EXAONE Deep models with the Transformers library, detailed explanations of quantized EXAONE Deep weights formatted in AWQ and GGUF, as well as guidance on how to run these models locally through platforms like llama.cpp and Ollama. Additionally, this resource serves to enhance user understanding and accessibility to the capabilities of EXAONE Deep models.
EXAONE Deep Alternatives
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Most AI video tools hand you a black box: closed weights, a subscription, and no way to see what is happening under the hood. LTX takes the opposite approach. Built by Lightricks, LTX is an open foundation model that generates and simulates across video, audio, and the physical world, and it puts the weights, the code, and the control in your hands.
At the center of the model is LTX-2.5, a 22B-parameter dual-stream diffusion transformer that produces native 4K video at up to 50 frames per second, with audio and video generated together in a single pass rather than stitched together afterward. Artificial Analysis, an independent benchmarking group, currently ranks LTX among the top three AI video models in the world.
You choose how you want to use it. Download the open weights and run LTX-2.5 on your own hardware. License the model for on-premise deployment backed by enterprise support. Or build directly on LTX Studio, the production suite that turns the model into a full creative workflow. Companies like ElevenLabs, Asteria Film Co., Magnopus, and NVIDIA already rely on LTX for their own work.
LTX is not built for one-off social clips. It is infrastructure for teams that generate motion, audio, and physical environments as part of their own products and pipelines.
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MiniMax M3
MiniMax M3 is a frontier open-weight AI model built for coding, agentic work, multimodal understanding, and ultra-long-context tasks. The model supports up to a 1 million token context window, allowing it to work across large codebases, long documents, logs, project histories, and complex task environments. MiniMax M3 introduces MiniMax Sparse Attention, a sparse attention architecture designed to make long-context processing more efficient. The model is natively multimodal, with training that supports deeper semantic fusion across text, image, and video inputs. It is designed to support software engineering tasks, repository analysis, terminal-style work, browser-style retrieval, tool use, and autonomous workflows. MiniMax M3 has a mixture-of-experts architecture with hundreds of billions of total parameters and a smaller activated parameter count for more efficient inference. Developers can use it for AI coding assistants, workflow automation, research agents, document analysis, visual reasoning, and enterprise AI systems. Its long-context capability makes it especially useful when tasks require many files, references, instructions, or interaction histories to stay available at once. MiniMax M3 helps teams build more capable AI agents that can understand larger problems, work across multiple modalities, and execute complex tasks with stronger context awareness.
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Phi-4-reasoning
Phi-4-reasoning is an advanced transformer model featuring 14 billion parameters, specifically tailored for tackling intricate reasoning challenges, including mathematics, programming, algorithm development, and strategic planning. Through a meticulous process of supervised fine-tuning on select "teachable" prompts and reasoning examples created using o3-mini, it excels at generating thorough reasoning sequences that optimize computational resources during inference. By integrating outcome-driven reinforcement learning, Phi-4-reasoning is capable of producing extended reasoning paths. Its performance notably surpasses that of significantly larger open-weight models like DeepSeek-R1-Distill-Llama-70B and nears the capabilities of the comprehensive DeepSeek-R1 model across various reasoning applications. Designed for use in settings with limited computing power or high latency, Phi-4-reasoning is fine-tuned with synthetic data provided by DeepSeek-R1, ensuring it delivers precise and methodical problem-solving. This model's ability to handle complex tasks with efficiency makes it a valuable tool in numerous computational contexts.
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Integrations
Company Details
Company:
LG
Year Founded:
1947
Headquarters:
South Korea
Website:
github.com/LG-AI-EXAONE/EXAONE-Deep
Media
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support
EXAONE Deep Features and Options
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