Comprehensive comparison table of modern Large Language Models – from GPT-4 to Llama 3, with architecture details, benchmarks, and license information.
Model comparison is essential for choosing the right architecture for a use case. This database enables systematic comparisons by parameters, context window, costs, and benchmarks.
Practical tools for navigating the LLM ecosystem.
The LLM landscape is growing rapidly. A structured overview helps with model selection and shows architectural trends like MoE, Sparse Attention, and Dual-Mode models.
No models found. Try a different search or filter combination.
DeepSeek-R1 (Jan 2025) showed that Chain-of-Thought reasoning can emerge during GRPO training. All major labs now follow the reasoning-first approach.
Claude 4.5 (Nov 2025) introduces the "Effort" parameter: Users directly control thinking time and accuracy. Enables Dual-Mode (Fast + Deep) in one model.
Llama 4 + Claude 4.5 use Early Fusion: Text and Vision tokens together in the LLM. Enables true cross-modal reasoning, not just image→text.
DeepSeek-V3.2 (Dec 2025) deploys Sparse Attention in production: 60% memory savings, 4-5× faster at same quality up to 1M+ Token context.
New benchmarks (ThinkBench, ELAIPBench) show: Reasoning ability is separate from knowledge ability. Some models excel only in reasoning.
DeepSeek-V3.2 breaks the pricing model: 75% cheaper than Claude/GPT at comparable performance. Sparse Attention + MoE Routing enable cost reduction.