Almanac

Learning path

Evaluation and Benchmarking in Modern AI

How do we know if an AI model is actually good? This path traces the ecosystem of evaluation and benchmarking — from the labs that set the standards, to the platforms that run the tests, to the frontier models being measured today. It's designed for readers who want to understand not just which models score highest, but how the field decides what "better" even means.

Start with the organizations shaping evaluation practice, move through the infrastructure that makes open benchmarking possible, and finish with the leading models whose releases have repeatedly reset the scoreboard.

Mixed level10 steps~52 min

10 steps

Begin →

  1. OpenAI

    Start here: OpenAI has driven many of the benchmark conventions the field now takes for granted, making it the natural anchor for understanding how evaluation culture developed.

    Read →BeginnerIn-depth

  2. Hugging Face

    Hugging Face is the central platform where open benchmarks are hosted, run, and compared — understanding it explains how independent evaluation actually happens in practice.

    Read →BeginnerIn-depth

  3. Anthropic

    Anthropic's approach to safety-focused evaluation adds a dimension beyond capability scores — a necessary counterpoint before looking at the models themselves.

    Read →BeginnerIn-depth

  4. Google DeepMind

    Google DeepMind rounds out the lab landscape with its own evaluation philosophy and benchmark contributions, completing the picture of who sets the standards.

    Read →BeginnerIn-depth

  5. Reinforcement Learning

    Reinforcement Learning is the training paradigm increasingly used to optimize for benchmark performance — understanding it explains why models improve on the metrics they do.

    Read →BeginnerIn-depth

  6. GRPO

    GRPO is a specific RL algorithm tied closely to recent benchmark-chasing training runs — a concrete example of how optimization choices shape evaluation outcomes.

    Read →BeginnerIn-depth

  7. GPT-5.5

    GPT-5.5 is one of the headline models whose release prompted fresh benchmark comparisons across the field — a live case study in how new releases shift the leaderboard.

    Read →BeginnerIn-depth

  8. Claude Opus 4.6

    Claude Opus 4.6 represents Anthropic's current benchmark entrant, useful for comparing how different labs' evaluation priorities show up in their flagship models.

    Read →BeginnerIn-depth

  9. DeepSeek V4

    DeepSeek V4 is the open-weight challenger whose benchmark results forced a reassessment of what closed frontier labs actually offer — a key data point in any honest evaluation survey.

    Read →BeginnerIn-depth

  10. Claude Code

    Claude Code closes the path with a domain-specific evaluation story — coding benchmarks are among the most rigorous and contested, making this a sharp illustration of benchmark design in action.

    Read →BeginnerIn-depth