Discover the Best AI Resources

Ranks LLMs by real production token usage, not benchmarks. Aggregates traffic from millions of users hitting 400+ models through one API — sliced by model, lab market share, tool-call frequency, and image volume, updated weekly.

Turns a chat interface into a general-purpose assistant for writing, analysis, coding, image work, and web-backed research. Its value is breadth and accessibility; its risk is over-trusting fluent answers without checking sources.

Provides a chat and API assistant for writing, coding, research, document analysis, and structured work. It stands out for steerability and safety-oriented design, but still needs verification on high-stakes facts.

Turns text, images, voice, files, and live context into conversational help across web and mobile. Its edge is tight access to Google Search, Android, Workspace, and multimodal Gemini models; the tradeoff is ecosystem lock-in and uneven reliability.

Provides a consumer chat interface to xAI's Grok models, spanning reasoning, code, voice, image, and video workflows. Its edge is tight integration with xAI's model/API stack and optional real-time search.

Interleaves chain-of-thought reasoning with tool-using actions in one LLM loop: the model plans, queries a source like Wikipedia, then revises from results. Cuts hallucination versus reasoning-only prompting and beats trained agents on interactive tasks.

Gives developers direct REST access to Claude models for chat, coding, agents, batch jobs, token counting, and structured outputs. It is best for teams that want Anthropic's newest features without routing through a cloud marketplace.

Treats the interface between an LM agent and a computer as a design variable. A custom agent-computer interface (ACI) with concise file-edit, repo-navigation, and test commands plus compact feedback reaches 12.5% pass@1 on SWE-bench, 87.7% on HumanEvalFix.

Generates cinematic video from text and image prompts, with newer versions adding native audio and tighter creative controls. It is built for high-fidelity clips that can move from quick Gemini experiments to API and Flow workflows.

A graph-based RAG framework pairing a knowledge graph with vector retrieval and a dual-level (low/high) query mode. New documents merge into the graph via set operations instead of triggering a rebuild, cutting the cost of keeping the index current.