How to Track AI Model Releases Systematically

## TL;DR What to capture for each AI model release: benchmarks, context window, cost per million tokens, license, and changelog URL. ## Decision in 20 seconds **What to capture for each AI model release: benchmarks, context window, cost per million tokens, license, and changelog URL.** ## Who this is for Builders who want a repeatable, low-noise way to track AI updates and turn them into decisions. ## Key takeaways - Why systematic tracking matters - What to capture per release - Where to keep it - Benchmarks: what to watch out for ## Why systematic tracking matters New models ship weekly. Without a system, you end up with scattered browser tabs, outdated comparisons, and no clear picture of how the landscape has shifted since your last decision. ## What to capture per release For each model release worth tracking, record these fields: | Field | Why it matters | |-------|---------------| | **Model name + version** | Canonical reference | | **Benchmarks** | Which evals, scores, and who ran them | | **Context window** | Affects what you can build | | **Cost per 1M tokens** | Input and output costs for budget modeling | | **License** | Commercial use, fine-tuning rights, redistribution | | **Changelog URL** | Primary source for verification | | **Date** | Context for how current your comparison is | ## Where to keep it A simple spreadsheet or Notion database works well. The key is that it's structured and searchable—not a folder of PDFs and bookmarks. ## Benchmarks: what to watch out for Self-reported benchmarks run by the releasing company are weak evidence. Look for independent evaluations (e.g. LMSYS Chatbot Arena, third-party reproducibility). Note who ran the benchmark and on what eval set. ## Review cadence Update your model tracker when you shortlist a new model from your weekly radar scan. Do a quarterly review to archive stale entries and update cost figures (prices drop frequently). ## Quotable summary Track AI model releases systematically: capture name/version, benchmarks (with source), context window, cost/1M tokens, license, and changelog URL. Keep it in a structured table, not scattered bookmarks. Review quarterly. ## Related reading - [RadarAI comparisons](/en/compare) - [RadarAI reviews](/en/reviews) - [Methodology: how RadarAI curates and links sources](/en/methodology) - [More evergreen guides](/en/articles) ## FAQ **Should I track every model?** No. Track models that are plausible for your use case given context window, cost, and license constraints. Everything else can stay in your radar history.