data/README.md

BenchPress Score Matrix (benchpress/data/llm_benchmark_data.json)

Sparse model × benchmark score matrix for BenchPress experiments. Schema: models[], benchmarks[], scores[{model_id, benchmark_id, score, reference_url}].

License

The BenchPress score-matrix dataset files in this directory are released under the Community Data License Agreement - Permissive - Version 2.0 (CDLA-Permissive-2.0). See LICENSE-CDLA-2.0.md.

The repository's top-level MIT license applies to the BenchPress code and documentation, not to this dataset license grant.

benchmark_cost_evidence.json is a separate evidence file for public, mechanically extractable benchmark cost signals such as prompt/completion token tables. It is not part of the score matrix schema; see benchmark_cost_evidence.README.md.

Benchmark-level cost dictionaries inside llm_benchmark_data.json store the current normalized token/dollar evidence attached to each benchmark. They should point back to a raw source in benchmark_cost_evidence.json when possible.

Score collection conventions

When a source reports multiple numbers for the same (model, benchmark), use this preference order — top wins:

1. No tools / no search / no code execution When a vendor or leaderboard distinguishes "no tools" vs "tool-use / code-exec / search-augmented" numbers, always pick the no-tools number. BenchPress measures raw model capability, not agentic harness performance.

   • Example: AIME 2025, Gemini 3 Flash → 95.2 (no tools), not 99.7 (with code execution).
   • Example: SWE-bench Verified, GPT-5.2 → use the bare-model number, not the agent-with-Codex score.

2. Reasoning / thinking ON when the model has a default reasoning mode that the vendor highlights as the headline number (Gemini 3 Pro Thinking, Claude Sonnet 4.5 Thinking, etc.). Pick the "Thinking" / "Extra-high reasoning" / "high effort" headline variant — it's what the vendor reports as the model's official benchmark.

3. Single-attempt (pass@1, maj@1) over self-consistency / pass@k aggregations.

Source URL preference order

Pick the most authoritative URL that actually contains the number you recorded:

1. Vendor model page — e.g. deepmind.google/models/gemini/flash/, anthropic.com/news/claude-..., openai.com/index/.... Highest authority for vendor-reported numbers.

2. Official benchmark leaderboard — e.g. arcprize.org/arc-agi/2/, swebench.com, epoch.ai/benchmarks/frontiermath, matharena.ai/competition_tables/<series>--<comp_id>, lmarena.ai, tbench.ai/leaderboard/.... Use when the vendor page doesn't list the number.

3. Aggregator — vellum.ai/blog/..., artificialanalysis.ai, livebench.ai. Use only as last resort. Aggregators sometimes lag or misattribute numbers.

4. Avoid — Medium posts, Reddit, screenshots, "preliminary review" blogs.

URL gotchas

• matharena.ai: the / root is JS-rendered. Use the data-endpoint pattern https://matharena.ai/competition_tables/<series>--<comp_id>. Examples:

  • aime--aime_2025, aime--aime_2024
  • hmmt--hmmt_feb_2025, hmmt--hmmt_nov_2025
  • brumo--brumo_2025, cmimc--cmimc_2025, smt--smt_2025
  • matharena_apex--matharena_apex_2025

• Codeforces ratings: many "rating" numbers appear in vendor blog posts, not on Codeforces itself. Cite the vendor source.

• MMLU vs MMLU-Pro vs MMMLU: these are three separate benchmarks. Don't conflate. MMMLU is the multilingual MMLU; MMLU-Pro is the harder reformulation.

Audit & fix workflow

See ../../others/score_audit_menu.md for the active checklist of (model, benchmark) pairs flagged by the score audit. Fix loop:

1. Pick a [ ] row from the menu.
2. Verify against the highest-priority source per the rules above.
3. Update score and reference_url in this JSON.
4. Tick [x] in the menu and note the new value + source.