AI Models
7 articles exploring AI Models. Expert analysis and insights from our editorial team.
Latest in AI Models
The Million-Token Context Window: What Can You Actually Do?
Million-token context windows let you feed entire codebases, legal contracts, and hours of video to an LLM in one pass—but advertised limits routinely overstate practical capability. Here's what the benchmarks, failure modes, and real deployment patterns actually show.
Claude's Web Search Changes Everything for AI Research
Anthropic's web search integration removes the static knowledge ceiling from Claude, enabling real-time retrieval directly inside the reasoning loop—with verifiable citations, domain filtering, and a new dynamic filtering layer that cuts token use by 24% while improving accuracy by 11%.
DeepSeek V3/R1: How Chinese Engineers Matched GPT-4 for $6 Million
DeepSeek's V3 and R1 models match GPT-4-class performance using a fraction of the compute through architectural innovations in Mixture of Experts, attention compression, and reinforcement learning—demonstrating that training efficiency may matter more than raw hardware scale.
Gemini 2.0 Pro's 2 Million Token Context: What Can You Actually Do With It?
Google's Gemini 2.0 Pro Experimental ships with a 2 million token context window—the largest among production-accessible models. Here's what practitioners have discovered works, what doesn't, and what the hard limits are.
Gemini 3.1 Pro: Google's New Reasoning Model Explained
Gemini 3.1 Pro is Google's latest reasoning-focused AI model, achieving 77.1% on ARC-AGI-2 benchmarks—more than double the performance of its predecessor. Here's how it compares to Claude and GPT.
Kimi Claw: Moonshot AI's Answer to Claude and ChatGPT
Moonshot AI's Kimi series has emerged as China's leading open-source AI challenger, offering trillion-parameter models with advanced agentic capabilities at a fraction of Western competitors' costs.
Two Different Tricks for Fast LLM Inference: Speeding Up AI Responses
Speculative decoding and efficient memory management through PagedAttention are two proven techniques that accelerate LLM inference by 2-24x without sacrificing output quality, enabling production deployments at scale.