AI directory
A builder-focused directory for comparing AI models, coding benchmarks, cost tradeoffs, open-source options, and local AI infrastructure.
Use this directory
Use this directory when model choice matters to product execution. It connects benchmark interpretation, coding performance, open-source model shifts, enterprise cost decisions, and hardware constraints so model comparisons lead to better engineering choices.
Page role
The canonical entry point for model comparison, benchmark interpretation, learning paths, and production tradeoffs.
SWE-bench, LiveCodeBench, and model leaderboards matter, but scaffolding and task mix change outcomes.
Premium reasoning models, value models, and open-source systems each fit different latency and budget constraints.
The same model behaves differently inside an IDE, terminal agent, API workflow, or local hardware setup.
Direct paths
Match SWE-bench, Aider, LiveCodeBench, and Terminal-Bench to real work.
Separate vendor claims, third-party tests, scaffolds, and reproducibility.
Connect benchmark scores to pricing, prompting, validation, and rollout.
Topic hub layer
Hub
Benchmarks, pricing, open-source tradeoffs, and coding capability analysis for builders choosing AI models.
This directory is the canonical entry point for this comparison layer.
Start here
A task-first guide to 2026 LLM benchmark scores for coding, math, and reasoning—covering SWE-bench, Aider, LiveCodeBench, Terminal-Bench, and model tradeoffs.
A transparent methodology for comparing AI model benchmarks without mixing vendor launch claims, third-party leaderboards, agent scaffolds, task difficulty, cost, and reliability.
GPT-5 transforms programming. Real-world benchmarks, pricing breakdown, common pitfalls, and 5 expert tips to boost your coding productivity with AI tools.
Two weeks after GPT-5 launch, enterprise adoption surged 8x while consumers fled. The untold story of OpenAI's pivot and what it means for AI's future.
Moonshot K2-Thinking uses 140M tokens per task. 2.5x more than rivals. Discover why this \"slow\" AI model beats GPT-5 and becomes #1 open-source AI despite $1,172 testing costs.
Supporting analysis
These articles deepen the directory without turning it into a thin generated list.
DeepSeek Harness is more than another model release. Here is what its open-source, plugin-first agent runtime means for developers building real coding workflows.
Official moves from OpenAI, Microsoft, Google, and Anthropic in March 2026 show the AI race shifting from pure model hype to distribution, work context, and enterprise adoption.
Google's March 2026 Pixel Drop suggests Gemini is moving beyond chat and becoming a cross-app action layer for mobile, which may matter more than another model benchmark.
OpenAI's March 2026 GPT-5.4, Codex Windows, and Codex Security releases point to a bigger shift: the company is building a full agent stack for professional work.
Apple's M5 chip delivers 4x GPU performance boost with enhanced Neural Engine. Discover how this breakthrough transforms AI development workflows for programmers.
Google's Gemini scored IMO gold medal. Learn to build advanced math reasoning apps with Gemini API - complete guide with code examples and implementation tips.