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Leaderboard

This page is generated deterministically from the committed benchmark result records under benchmarks/results/. It does not run the suite; it aggregates the *.result.json records the runs already wrote. Regenerate it with cargo run -p reticle-bench -- leaderboard. The record format is the API: to add a row, run the suite and open a pull request with your records (see Submitting a run).

It aggregates 284 committed result record(s) into 4 row(s), one per backend / model / quantization triple and suite_version, so a model’s runs against different suite versions stay separate rows and no row blends two suite denominators. The numbers are exactly what the committed records say and grow as more runs are committed.

How to read a row

  • Kind labels a row as a bare model (a model driven through Reticle’s own propose-verify-correct loop), an agent system (a system that brings its own loop and scaffold, such as Claude Code), or a multi-agent system. A bare-model row and an agent-system row measure different things and are not comparable head to head (see the methodology).
  • Quantization is carried where the backend reports one (for example Q4_K_M on a local GGUF model), so a small quantized local model is never conflated with a full-precision or frontier one.
  • PARTIAL marks a row that has no result in one or more tiers, so it did not span the full difficulty range and its denominator is not comparable to a full-tier row.
  • Each Tier cell is passed/total, and Overall is passed/total (rate) over every committed record for that row.

Rankings

KindModelBackendQuantizationSuiteTier 1Tier 2Tier 3Tier 4Tier 5Overall
agent systemclaude-sonnet-5claude-code-0.7.05/58/826/284/75/548/53 (91%)
agent systemclaude-sonnet-5claude-code-adhoc8/910/1138/417/119/972/81 (89%)
bare modelgpt-oss:16kollamaMXFP40.4.09/911/1119/345/118/1052/75 (69%)
bare modelqwen2.5-coder:16kollamaQ4_K_M0.4.06/98/116/343/116/1029/75 (39%)

The labeling rules above are the honest account preserved from the benchmark methodology: a machinery baseline, a local model, and an agent system are always distinguishable, and a partial run is never published as a full-suite score.