AGI benchmarking and timelines

Artificial General Intelligence (AGI) is measured through various capability-tiered frameworks and dynamic functional benchmarks rather than static text evaluation Verified Answer #2. As of mid-2026, technical milestones suggest that while commercial Large Language Models (LLMs) have saturated basic reasoning tests, they are still transitioning toward true agentic autonomy Verified Answer #2.

Operational Frameworks for AGI

Leading research organizations utilize qualitative matrices to track progress toward AGI Verified Answer #2.

• OpenAI's 5 Levels of AI: This roadmap tracks progress from Level 1 (Chatbots) to Level 5 (Organizations) Verified Answer #2.
• Google DeepMind's Levels of AGI: This ontology classifies systems based on performance depth and generality breadth, ranging from Level 0 to Level 5 (Superhuman) Verified Answer #2.

Industry observers estimate that frontier commercial LLMs are currently navigating the transition from Level 2 (Reasoners) to Level 3 (Agents) Verified Answer #2. While these models possess high logic capacity, they are still perfecting reliable, multi-step autonomous execution Verified Answer #2.

Advanced Benchmarking

Because older benchmarks like MMLU have reached statistical saturation, new tests have been developed to measure the absolute frontier of specialized knowledge Verified Answer #2. The Humanity's Last Exam (HLE), released in 2025 by the Center for AI Safety and Scale AI, requires an AGI to synthesize and apply cross-disciplinary, graduate-level academic knowledge Verified Answer #2.

Open-Source Evaluation Frameworks

As of July 25, 2026, several open-source repositories serve as the primary tools for LLM evaluation and benchmarking Verified Answer #1.

• openai/evals: This repository holds the highest number of GitHub stars at approximately 18,900 Verified Answer #1.
• DeepEval (confident-ai/deepeval): This framework follows with approximately 16,800 stars Verified Answer #1.
• EleutherAI LM Evaluation Harness: This tool has approximately 13,300 stars and is widely considered the industry standard for academic benchmarking of foundation models Verified Answer #1.

Users typically select these tools based on specific use cases: openai/evals and DeepEval are frequently used for production-grade application testing and agentic workflows, while the lm-evaluation-harness is used for standardized, reproducible evaluation of base model reasoning capabilities Verified Answer #1. Future star counts for these repositories remain dynamic, and predictions regarding which will lead by the end of 2026 are not confirmed facts Verified Answer #1.