LLMs & Models Articles
Browse 572 articles about LLMs & Models.
How to Use GLM 5.2 for Agentic Workflows: Agent Harness, Chrome Extensions, and Game Clones
GLM 5.2 excels at coding agents, Chrome extensions, and long-context tasks at a fraction of frontier model costs. Here's how to use it effectively.
Open-Weight AI Models vs Closed Frontier Models: How to Choose for Your Agent Stack
GLM 5.2, Qwen, and DeepSeek are catching up to Claude and GPT. Learn when open-weight models win and when frontier models are worth the cost.
What Is Claude Sonnet 5? Anthropic's Most Agentic Sonnet Model Explained
Claude Sonnet 5 is Anthropic's most agentic Sonnet yet—faster and cheaper than Opus 4.8 while matching it on most tasks. Here's what changed.
What Is GLM 5.2? The Open-Weight Model With 1M Token Context and Frontier-Level Coding
GLM 5.2 is ZAI's 753B open-weight model with 1M token context, MCP support, and agentic coding at 1/5th the cost of frontier models.
Claude Sonnet 5 Token Efficiency Problem: Why It Can Cost More Than Opus 4.8
Claude Sonnet 5 uses 30% more tokens than previous models. Learn why this happens and how to manage costs in agentic AI workflows.
Claude Sonnet 5 vs Opus 4.8: Which Model Should You Use for Agentic Work?
Claude Sonnet 5 is cheaper but uses more tokens than Opus 4.8. Here's how to choose the right model for your agentic workflows and budget.
What Is Claude Sonnet 5? Anthropic's Most Agentic Sonnet Model Explained
Claude Sonnet 5 is Anthropic's most agentic Sonnet yet. Learn how it compares to Opus 4.8, its pricing, and when to use it in your AI workflows.
What Is GPT-5.6? OpenAI's Three-Model Tier System Explained
GPT-5.6 comes in three tiers: Soul, Terra, and Luna. Learn what each model is designed for, how they're priced, and who gets access first.
What Is Seed Audio 1.0? ByteDance's Audio Scene Generator for AI Workflows
Seed Audio 1.0 generates full audio scenes with dialogue, ambient sound, and effects. Learn how it works and how to use it in AI video workflows.
Confidence-Scheduled Verification: How DeepSpark Cuts Wasted GPU Compute in AI Agents
DeepSpark's confidence-scheduled verifier skips low-probability tokens under load, saving GPU resources and speeding up production AI agent inference.
What Is DeepSpark? DeepSeek's Speculative Decoding Method That Makes Every LLM Faster
DeepSpark is DeepSeek's open-source speculative decoding system delivering 50–400% faster inference without retraining. Here's how it works.
What Is GLM 5.2? The Open-Weight Model Beating GPT 5.5 on Design and Coding Benchmarks
GLM 5.2 from ZAI offers a 1M token context window, MIT license, and frontier-level coding performance at a fraction of the cost of closed models.
Speculative Decoding Explained: How Draft Models Make AI Agents Faster
Speculative decoding uses a small draft model to guess tokens and a large model to verify them. Learn how it cuts AI agent latency without losing quality.
What Is DeepSpark? How DeepSeek Made Every LLM 50–400% Faster Without Retraining
DeepSpark is DeepSeek's speculative decoding method that speeds up LLM inference 50–400% with no retraining. Learn how it works and why it matters.
Self-Scaffolding AI Models: How Ornith 1.0 Writes Its Own Agent Harness
Ornith 1.0 generates custom harnesses for each task instead of relying on human-written scaffolds. Learn how self-scaffolding works and why it matters.
What Is Sakana Fugu? The Multi-Model Orchestrator That Routes Prompts Automatically
Sakana Fugu is an orchestrator model that routes prompts to the best AI model automatically. Learn how it works and when to use Fugu vs Fugu Ultra.
GPT-5.6 Soul, Terra, and Luna: What the Three Model Tiers Mean for Builders
OpenAI previewed GPT-5.6 in three tiers: Soul for power, Terra for balance, and Luna for speed. Here's what each tier delivers and who should use which.
What Is Sakana Fugu? The Multi-Model Orchestrator Explained
Sakana Fugu is an AI orchestrator that routes prompts to the best model automatically. Learn how it works, its two tiers, and real benchmark results.
GLM 5.2 Architecture Deep Dive: Index Share, Sparse Attention, and Multi-Token Prediction
GLM 5.2 achieves 2.9x fewer compute operations at 1M token context using Index Share sparse attention. Here's the technical breakdown for AI builders.
What Is GLM 5.2? The Open-Weight Model With Frontier-Level Coding and 1M Token Context
GLM 5.2 is a 744B MoE open-weight model with a 1M token context window, sparse attention, and pricing 10x cheaper than Claude. Here's what sets it apart.