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Methodology — How We Benchmark AI Models

Methodology — How We Benchmark AI Models

AIModelBenchmarks.com publishes lightweight, repeatable scorecards across coding, reasoning, and workflow tasks. We focus on practical outcomes: can the model ship features, debug reliably, and deliver usable output under time pressure?

Our Principles

  • Transparent prompts. Every eval includes the exact prompt and rubric. You can rerun any test yourself.
  • Real tasks, not toy problems. We test what engineering teams actually do: fix bugs, make architecture decisions, wire up APIs.
  • Reproducible. Same inputs, same scoring, clear deltas over time. No hidden grading or subjective rankings.
  • Operator-first. We track cost, latency, and reliability -- the metrics that matter when you're running AI in production.

Eval Categories

  • Coding: Small feature builds, bug fixes, refactors, API patches. Scored on correctness, code quality, and diff cleanliness.
  • Reasoning: Build-vs-buy decisions, debugging diagnosis, system design tradeoffs. Scored on constraint coverage, decision clarity, and actionability.
  • Tool use: Multi-step tasks using CLI tools, official documentation, and third-party APIs. Scored on doc-faithfulness, verification steps, and security hygiene.
  • RAG / Research: (Coming soon) Retrieval quality, synthesis accuracy, and citation faithfulness.
  • Agents: (Coming soon) Multi-step task completion, tool failure recovery, and plan coherence.

Scoring Rubric

Each task is scored out of 10 on three dimensions:

  • Correctness (4 pts): Does the output solve the problem? Are there bugs, hallucinations, or missing requirements?
  • Speed-to-usable output (3 pts): How quickly does the model produce something an engineer can actually use?
  • Clarity (3 pts): Is the output well-structured, documented, and easy to review?

The weighted total rolls into the daily scorecard. We weight coding at 40%, reasoning at 35%, and tool-use at 25%.

How We Run Evals

  1. Task selection: We pick a real-world task from our backlog (contributed by engineers, PMs, and our team).
  2. Prompt design: We write a clear, constrained prompt with explicit success criteria.
  3. Model execution: We run the same prompt against every model under test (currently GPT-5, Claude Opus 4.1, Gemini 2.5 Pro, DeepSeek-R1, and GLM-5).
  4. Blind scoring: Outputs are scored against the rubric without knowing which model produced them.
  5. Publication: The full scorecard -- prompt, rubric, scores, and analysis -- is published daily.

Models We Track

We currently evaluate GPT-5 (OpenAI), Claude Opus 4.1 (Anthropic), Gemini 2.5 Pro (Google), DeepSeek-R1 (DeepSeek), and GLM-5 (Zhipu). Official model names and pricing references are documented on our Model Data Sources page and were last verified on February 16, 2026.

How to Contribute

Suggest tasks, submit eval ideas, or request a model head-to-head. We'll add it to the queue. Reach out on X @aimodelbench or open an issue on our GitHub.