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Which AI models lead coding, agent and work tests, with a source for every number
DataPython · HTMLGitHub @IbrahimSobhOnlineOwner unconfirmed
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Weekly comparison of frontier AI models on coding, agentic and work benchmarks, with a source link for every number
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Read the full README ↗Website: ibrahimsobh.github.io/ai-benchmark-tracker
A weekly report comparing frontier AI models on coding, agentic and work benchmarks. Every number keeps its source link, publisher date and trust label.
pip install -r requirements.txt
python tracker.py all # collect + report
Output:
data/raw/<date>/: every raw page/file, as fetched (local only, never committed: see .gitignore)data/clean/<date>.csv: one table, all sources, same columns (public; GDPx trimmed to the top 3)data/alerts/<date>.json: zero-row, missing-source and old-data alertsreports/<date>-report.md: the tables file. The script rebuilds it on every run.reports/<date>-insights.md: the summary and insights, written by the AI from the tables file
(rules in prompts/analyze.md). The script never touches this file.docs/: the website, built by python build_site.py and served by GitHub Pages. Its look follows
Coding Agents Digest: same colours, dark/light themes,
self-hosted Newsreader and Schibsted Grotesk fonts (SIL OFL, docs/assets/fonts/) and card tooltips.
Lab colours and logos are in bench/labs.py; logo sources and licences are in docs/assets/logos/CREDITS.md. Charts (rank grid
and score-with-margin charts, bench/charts.py) are drawn from data/clean/ by the script, not by the AI.A scheduled Claude task runs every week and follows prompts/weekly_task.md: collect, write the
insights, build the site, commit and push. GitHub Pages then publishes docs/. Insights are published
without manual review; the site footer says they are AI-written.
The task can also run at any time ("Run now" on the routine's claude.ai page). Each run date gets its
own entry; a second run on the same day replaces that day's entry. The "Change" column compares with
the earlier run closest to 7 days back (baseline_csv in tracker.py), so extra manual runs never
shorten the week-to-week comparison.
| Benchmark | Publisher | Method | Margin | Trust |
|---|---|---|---|---|
| SWE Atlas v1.1 (Overall + 3 parts) | Scale | HTML, embedded JSON | ±CI | independent |
| SWE-Bench Pro (Public; CSV only) | Scale | HTML, embedded JSON | ±CI | independent |
| SWE-Bench Pro V2 (Full, Hard) | Scale | HTML, embedded JSON | ±CI | independent |
| MCP-Atlas | Scale (live board) | HTML, embedded JSON | ±CI | independent |
| Terminal-Bench 4.0 | Artificial Analysis | HTML, embedded JSON | none published | independent |
| GDPval-AA v2.1 | Artificial Analysis | HTML, embedded JSON | 95% CI | independent |
| GDPx v1.0 | SemiAnalysis | site's own JSON + GDPx's published formula | none published | independent |
| Agent Arena, WebDev Arena | Arena | official Hugging Face dataset (CC-BY-4.0) | 95% CI | crowd-preference |
No source needs Playwright. arena.ai pages sit behind a Cloudflare bot check, so the Arena collector uses Arena's official dataset instead of the website.
bench/names.py splits each raw name into a canonical model name and a setup
(effort, harness). Fix mismatches in model_aliases.json; the report re-applies names
to past weeks too. The report lists names seen at only one publisher, to spot missing aliases.
TOP_N_OVERRIDE in tracker.py).
Because this repo is public, data/clean/ also keeps only the GDPx top 3.