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Score: IRT expected performance across evaluations. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-biopharma-bench","name":"Biopharma Bench v0.1","shortName":"Biopharma Bench v0.1","sourceId":"boggs","url":"https://eval.raycaster.ai/benchmarks/biopharma-bench/","description":"Uses mean task criteria score across 71 regulated biopharma workflows. Score: mean task criteria score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-cad-arena","name":"CAD Arena","shortName":"CAD Arena","sourceId":"boggs","url":"https://normal.ai/leaderboard/cad-arena","description":"Uses the published mean CAD Arena score across all CAD platforms, on its native 0-1 scale. Score: mean CAD Arena score across all platforms. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-cad-eval","name":"CAD-Eval","shortName":"CAD-Eval","sourceId":"boggs","url":"https://epoch.ai/benchmarks/cad-eval","description":"Uses Epoch AI's external CAD-Eval overall pass-rate leaderboard. Score: CAD-Eval overall pass rate. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-cadbench","name":"CADBench","shortName":"CADBench","sourceId":"boggs","url":"https://www.seldon.global/blog/cadbench","description":"Uses Seldon's first-party mean verifier score. Score: mean verifier score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-cheatbench","name":"CheatBench","shortName":"CheatBench","sourceId":"boggs","url":"https://www.cheatbench.ai/","description":"Uses the published cheating-attempt rate; lower is better. Score: cheating rate. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":false,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-clockbench","name":"ClockBench","shortName":"ClockBench","sourceId":"boggs","url":"https://clockbench.ai/","description":"Uses the first-party analog-clock accuracy leaderboard. Score: overall accuracy. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-complex-constraints","name":"ComplexConstraints","shortName":"ComplexConstraints","sourceId":"boggs","url":"https://surgehq.ai/benchmarks/complex-constraints","description":"Uses Surge AI's first-party leaderboard score. Score: score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-critpt-v2","name":"CritPt v2","shortName":"CritPt v2","sourceId":"boggs","url":"https://artificialanalysis.ai/evaluations/critpt-v2","description":"Uses Artificial Analysis accuracy on the revised research-level physics benchmark, evaluated with code execution. Score: accuracy. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-dayjob-finance","name":"DAYJOB: Finance","shortName":"DAYJOB: Finance","sourceId":"boggs","url":"https://surgehq.ai/benchmarks/dayjob-finance","description":"Uses Surge AI's rubric-graded finance assignment success rate. Score: score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-dayjob-healthcare","name":"DAYJOB: Healthcare","shortName":"DAYJOB: Healthcare","sourceId":"boggs","url":"https://surgehq.ai/benchmarks/dayjob-healthcare","description":"Uses Surge AI's rubric-graded healthcare assignment success rate. Score: score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-dayjob-tax","name":"DAYJOB: Tax","shortName":"DAYJOB: Tax","sourceId":"boggs","url":"https://surgehq.ai/benchmarks/dayjob-tax","description":"Uses the published attempt pass rate at the 95% rubric threshold across 50 professional tax assignments. Score: score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-drop","name":"DROP","shortName":"DROP","sourceId":"boggs","url":"https://llm-stats.com/benchmarks/drop","description":"Uses LLM Stats / ZeroEval DROP F1 leaderboard. Score: DROP F1. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-edge-bench","name":"EdgeBench","shortName":"EdgeBench","sourceId":"boggs","url":"https://edge-bench.org/","description":"Uses the leaderboard's overall score at the longest (12-hour) environment-learning horizon. Score: 12-hour-horizon overall score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-enterprisebench-corecraft","name":"EnterpriseBench CoreCraft","shortName":"EnterpriseBench CoreCraft","sourceId":"boggs","url":"https://surgehq.ai/benchmarks/enterprisebench-corecraft","description":"Uses Surge AI's first-party enterprise-task leaderboard score. Score: score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-epoch-automation-reports","name":"Epoch Automation Reports","shortName":"Epoch Automation Reports","sourceId":"boggs","url":"https://epoch.ai/benchmarks/epoch-automation-reports","description":"Uses Epoch's equal-weight average of category rubric scores, including human-intervention deductions. Score: mean category rubric score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-eqbench","name":"EQ-Bench 3","shortName":"EQ-Bench 3","sourceId":"boggs","url":"https://eqbench.com/eqbench3.html","description":"Uses the published Elo leaderboard. Score: Elo scaled expected win rate vs claude-fable-5. Collected by Jake Boggs' benchmark dashboard.","unit":"elo","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-frontier-challenge","name":"FrontierChallenge","shortName":"FrontierChallenge","sourceId":"boggs","url":"https://apodexai.github.io/FrontierAgent/benchmarks/FrontierChallenge/","description":"Uses the first-party pass rate across 97 scientific workflow tasks. Score: pass rate. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-frontier-code-v2","name":"FrontierCode 2.0","shortName":"FrontierCode 2.0","sourceId":"boggs","url":"https://cognition.com/frontiercode","description":"Uses the 2.0 Main suite's weighted rubric score at each model's best reasoning effort, including published historical model results. Score: Main rubric score at best reasoning effort. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-frontier-cs-2","name":"FrontierCS 2","shortName":"FrontierCS 2","sourceId":"boggs","url":"https://frontier-cs.org/","description":"Uses the published pass rate against the human reference on every hidden metric, not the separate FCI index. Score: pass rate beating the human reference on every metric. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-frontierml","name":"FrontierML","shortName":"FrontierML","sourceId":"boggs","url":"https://www.frontierml.org/","description":"Uses the published mean normalized task score across wallet-budget settings; the index selects each model's highest-scoring setting. Score: mean normalized task score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-game-arena","name":"Kaggle Game Arena","shortName":"Kaggle Game Arena","sourceId":"boggs","url":"https://www.kaggle.com/benchmarks/kaggle/game-arena/versions/1","description":"Uses Kaggle's pooled Bradley-Terry leaderboard rating across strategic games. Score: Bradley-Terry rating scaled expected win rate vs Gemini 4 Argon. Collected by Jake Boggs' benchmark dashboard.","unit":"elo","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-gamedevbench","name":"GameDevBench","shortName":"GameDevBench","sourceId":"boggs","url":"https://waynechi.com/gamedevbench/","description":"Uses first-party pass@1 across all 333 game-development tasks. Score: pass@1 across all 333 tasks. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-gba-eval","name":"GBA Eval","shortName":"GBA Eval","sourceId":"boggs","url":"https://gbaeval.com/","description":"Uses the first-party leaderboard JSON overall emulator score. Score: overall emulator score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-gdp-xlsx","name":"GDP.xlsx","shortName":"GDP.xlsx","sourceId":"boggs","url":"https://surgehq.ai/benchmarks/gdp-xlsx","description":"Uses Surge AI's first-party spreadsheet-task leaderboard score. Score: score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-gdpx","name":"GDPx","shortName":"GDPx","sourceId":"boggs","url":"https://gdpx.semianalysis.com/?domain=without-safety-classifiers&score=partial","description":"Uses partial credit, averaged equally across domains after averaging partners within each domain; excludes biology, cybersecurity, and alignment. Score: domain-balanced partial-credit score without safety classifiers. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-gsm8k","name":"GSM8K","shortName":"GSM8K","sourceId":"boggs","url":"https://epoch.ai/benchmarks/gsm8k","description":"Uses Epoch AI's external GSM8K exact-match leaderboard. Score: GSM8K exact match. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-halluhard","name":"HalluHard","shortName":"HalluHard","sourceId":"boggs","url":"https://halluhard.com/","description":"Excludes web-search variants. Score: turn-1 hallucination rate without web search. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":false,"category":"safety","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-handbook","name":"HANDBOOK.md Agents","shortName":"HANDBOOK.md Agents","sourceId":"boggs","url":"https://surgehq.ai/benchmarks/handbook","description":"Uses Surge AI's first-party MCP-based enterprise policy-following agent score. Score: score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-hellaswag","name":"HellaSwag","shortName":"HellaSwag","sourceId":"boggs","url":"https://epoch.ai/benchmarks/hella-swag","description":"Uses Epoch AI's external HellaSwag leaderboard. Random baseline is 25% for four-way multiple choice. Score: HellaSwag accuracy. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-helm-lite","name":"HELM Lite","shortName":"HELM Lite","sourceId":"boggs","url":"https://crfm.stanford.edu/helm/lite/latest/","description":"Uses HELM Lite's core-scenarios Accuracy table mean win rate. Score: core-scenarios mean win rate. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-hermes-bench","name":"Hermes Bench","shortName":"Hermes Bench","sourceId":"boggs","url":"https://portal.nousresearch.com/bench","description":"Uses mean reward across 150 Hermes Bench tasks in the Hermes Agent harness. Score: mean task reward. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-humaneval","name":"HumanEval","shortName":"HumanEval","sourceId":"boggs","url":"https://llm-stats.com/benchmarks/humaneval","description":"Uses LLM Stats / ZeroEval HumanEval pass@1 leaderboard. Score: HumanEval pass@1. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-itsm-bench","name":"ITSMBench","shortName":"ITSMBench","sourceId":"boggs","url":"https://simulation.ressl.ai/benchmarks/atomicwork/itbench","description":"Uses live first-party pass@1 across 89 enterprise IT service-management tasks. Score: pass@1. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-job-bench","name":"JobBench","shortName":"JobBench","sourceId":"boggs","url":"https://job-bench.github.io/","description":"Uses the first-party weighted-score leaderboard. Score: weighted score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-matharena","name":"MathArena","shortName":"MathArena","sourceId":"boggs","url":"https://matharena.ai/models","description":"Uses MathArena's website-wide IRT expected-performance estimate. Score: website-wide IRT expected performance. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-mathvista","name":"MathVista","shortName":"MathVista","sourceId":"boggs","url":"https://www.kaggle.com/benchmarks/open-benchmarks/mathvista","description":"Uses Kaggle's MathVista testmini accuracy leaderboard. Random-chance accuracy is 17.9%. Score: MathVista testmini accuracy. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-maze-bench","name":"MazeBench","shortName":"MazeBench","sourceId":"boggs","url":"https://mazebench.com/leaderboard/ai","description":"Uses gems collected on the populated first-party ASCII-with-tools leaderboard. Score: share of MazeBench's 100 gems collected. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-mercor-apex","name":"Mercor APEX","shortName":"Mercor APEX","sourceId":"boggs","url":"https://www.mercor.com/apex/apex-agents-leaderboard/","description":"Score: best pass@1 (loop_truncated_tools_agent). Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-nanogpt-speedrun","name":"NanoGPT Speedrun","shortName":"NanoGPT Speedrun","sourceId":"boggs","url":"https://www.primeintellect.ai/research/nanogpt-speedrun","description":"Uses Prime Intellect's source-reported share of the human record gap closed for each model's best validated optimizer-speedrun result. Score: share of the human record gap closed. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-olam-poker-elo","name":"Olam Poker Elo","shortName":"Olam Poker Elo","sourceId":"boggs","url":"https://olamlabs.ai/evaluations/poker/elo","description":"Uses Olam Labs' Bradley-Terry Elo ratings from multi-agent social poker and excludes human players. Score: Poker Elo scaled expected win rate vs Claude Fable 5. Collected by Jake Boggs' benchmark dashboard.","unit":"elo","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-openbookqa","name":"OpenBookQA","shortName":"OpenBookQA","sourceId":"boggs","url":"https://epoch.ai/benchmarks/open-book-qa","description":"Uses Epoch AI's external OpenBookQA accuracy leaderboard. Random baseline is 25% for four-way multiple choice. Score: OpenBookQA accuracy. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-opus-magnum-bench","name":"Opus Magnum Bench","shortName":"Opus Magnum Bench","sourceId":"boggs","url":"https://opusmagnumbench.com/","description":"Uses each model variant's best pooled human-normalized reward across the full 36-puzzle campaign. Score: mean best human-normalized puzzle reward. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-perception-bench","name":"PerceptionBench","shortName":"PerceptionBench","sourceId":"boggs","url":"https://www.kimi.com/blog/perception-bench","description":"Uses the first-party overall atomic visual-perception accuracy. Score: overall atomic visual-perception accuracy. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-ple-bench","name":"PLE-Bench","shortName":"PLE-Bench","sourceId":"boggs","url":"https://ple-bench.com/leaderboard","description":"Uses the overall hidden-test rubric score, selecting each model's best harness on the native 0-1 scale. Score: mean rubric score on the hidden test set. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-posttrainbench-v1","name":"PostTrainBench v1","shortName":"PostTrainBench v1","sourceId":"boggs","url":"https://posttrainbench.com/?version=v1","description":"Uses the archived v1 weighted aggregate; subtasks do not count as separate benchmarks. Score: weighted aggregate score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-ppbench","name":"PPBench","shortName":"PPBench","sourceId":"boggs","url":"https://ppbench.com/","description":"Score: max success rate. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-production-simulation","name":"Production Simulation","shortName":"Production Simulation","sourceId":"boggs","url":"https://www.haladir.com/blog/production-simulation","description":"Uses mean normalized score across twenty production-planning scenarios. Score: mean normalized scenario score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-remote-labor-index","name":"Remote Labor Index","shortName":"Remote Labor Index","sourceId":"boggs","url":"https://www.remotelabor.ai/","description":"Uses the dashboard.safe.ai RLI automation-rate API. Score: automation rate. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-riemann-bench","name":"Riemann-Bench","shortName":"Riemann-Bench","sourceId":"boggs","url":"https://surgehq.ai/benchmarks/riemann-bench","description":"Uses Surge AI's first-party extreme-math leaderboard score. Score: score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-rwe-bench","name":"RWE-Bench","shortName":"RWE-Bench","sourceId":"boggs","url":"https://xplore.ai/rwe-bench","description":"Uses average pass@1 across real-world engineering tasks, not pass@5; publication/export dates do not determine model release dates. Score: average pass@1 across engineering tasks. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-scale-hil","name":"HiL-Bench","shortName":"HiL-Bench","sourceId":"boggs","url":"https://labs.scale.com/leaderboard/hil","description":"Uses Scale Labs combined Pass@3 with ask_human tool. Score: combined Pass@3 with ask_human tool. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-scbench","name":"SlopCodeBench","shortName":"SlopCodeBench","sourceId":"boggs","url":"https://www.scbench.ai/leaderboard","description":"Uses the published best-version leaderboard and Iso Solve score. Score: isolated checkpoints solved. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-sciuniverse","name":"SciUniverse","shortName":"SciUniverse","sourceId":"boggs","url":"https://c5r.net/sciuniverse","description":"Uses overall Pass@1 averaged equally across 17 task families. Score: Pass@1 across 17 equally weighted task families. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-senior-swe-bench","name":"Senior SWE-Bench","shortName":"Senior SWE-Bench","sourceId":"boggs","url":"https://senior-swe-bench.snorkel.ai/agents","description":"Uses the first-party tasteful solve rate (pass@1) at each model's published effort. Score: tasteful solve rate (pass@1). Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-sudo-l7","name":"sudo L7","sourceId":"surge-sudo-l7","url":"https://surgehq.ai/benchmarks/sudo-l7","description":"Surge AI's 60-task staff-level software engineering benchmark. Expert-authored rubrics assess correctness, architecture, production risk, verification and communication, using each model's published agent harness and reasoning setting.","unit":"percent","higherIsBetter":true,"category":"coding","normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":true,"defaultWeight":0},{"id":"bg-tasteval","name":"TasteVal","shortName":"TasteVal","sourceId":"boggs","url":"https://pzeroresearch.com/work/tasteval/","description":"Uses experimental-compute efficiency relative to human experts, divided by the current highest model multiplier. Retains the native multiplier and 95% source interval. Score: compute multiplier / current leaderboard maximum. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-triviaqa","name":"TriviaQA","shortName":"TriviaQA","sourceId":"boggs","url":"https://epoch.ai/benchmarks/trivia-qa","description":"Uses Epoch AI's external TriviaQA exact-match leaderboard. Score: TriviaQA exact match. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-winogrande","name":"WinoGrande","shortName":"WinoGrande","sourceId":"boggs","url":"https://epoch.ai/benchmarks/wino-grande","description":"Uses Epoch AI's external WinoGrande accuracy leaderboard. Random baseline is 50% for binary choice. Score: WinoGrande accuracy. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-zerobench","name":"ZeroBench","shortName":"ZeroBench","sourceId":"boggs","url":"https://zerobench.github.io/","description":"Uses main-question pass@5 because current ZeroBench evaluations no longer report pass@1. Score: main questions pass@5. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-aider-polyglot","name":"Aider Polyglot","shortName":"Aider Polyglot","sourceId":"benchmarkheaven","url":"https://aider.chat/docs/leaderboards/","description":"Evaluates LLMs on 225 Exercism coding exercises across six languages, testing code editing without human intervention. Maintained by Paul Gauthier. Score: Percent of the 225 Exercism exercises completed with a correct edit.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-apprenticebench-api","name":"ApprenticeBench API (NeoCognition)","shortName":"ApprenticeBench API","sourceId":"benchmarkheaven","url":"https://apprenticebench.com/","description":"An agent completes the same 100 sequential accounts-payable tasks in the Odoo ERP with diminishing mentoring, using dedicated application API calls instead of the screen interface. Maintained by NeoCognition. Score: Cumulative success rate.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-apprenticebench-cua","name":"ApprenticeBench CUA (NeoCognition)","shortName":"ApprenticeBench CUA","sourceId":"benchmarkheaven","url":"https://apprenticebench.com/","description":"A computer-use agent operates the Odoo ERP through its screens across 100 sequential accounts-payable tasks with diminishing mentoring. Maintained by NeoCognition. Score: Cumulative success rate.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-bu-bench-v1","name":"BU Bench V1 (Browser Use)","shortName":"BU Bench V1","sourceId":"benchmarkheaven","url":"https://github.com/browser-use/benchmark","description":"A browser agent completes 100 hand-selected web tasks: custom page-interaction challenges plus tasks drawn from WebBench, Mind2Web 2, GAIA and BrowseComp. Maintained by Browser Use. Score: Task success rate.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-buzzbench","name":"BuzzBench","shortName":"BuzzBench","sourceId":"benchmarkheaven","url":"https://eqbench.com/buzzbench.html","description":"An LLM-judged evaluation of humour explanation and predicted audience response to Never Mind the Buzzcocks introductions. Maintained by Samuel Paech / EQ-Bench. Score: LLM judge score against human-authored gold responses; headline Score column.","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-charxiv-reasoning-val-v1.0","name":"CharXiv Reasoning","shortName":"CharXiv Reasoning","sourceId":"benchmarkheaven","url":"https://charxiv.github.io/#leaderboard","description":"Can a multimodal model reason over the real, messy charts in scientific papers — combining values, trends and labels across a figure — rather than read off templated plots? Maintained by Princeton Language and Intelligence. Score: Reasoning accuracy in percent on the validation split: the share of its 1,000 reasoning questions (one per chart) answered correctly, each final answer extracted and scored 0/1 against the ground truth by GPT-4o.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-context-arena-mrcr-v2-8-needle","name":"Context Arena — MRCR v2 (8-needle)","shortName":"Context Arena — MRCR v2","sourceId":"benchmarkheaven","url":"https://contextarena.ai/","description":"The model must find and reproduce the right one of eight near-identical earlier replies hidden in a long conversation, averaged over context lengths from 8k to 128k tokens. Maintained by Context Arena. Score: Cumulative average score up to 128k tokens (unweighted mean of the 8k–128k bins), percent.","unit":"percent","higherIsBetter":true,"category":"long-context","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-cursorbench-4.0","name":"CursorBench 4.0 (Cursor)","shortName":"CursorBench 4.0","sourceId":"benchmarkheaven","url":"https://cursor.com/cursorbench","description":"Agent evaluation on ambiguous, multi-file tasks drawn from real Cursor sessions. Maintained by Cursor. Score: CursorBench score.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-eq-bench-4","name":"EQ-Bench 4","shortName":"EQ-Bench 4","sourceId":"benchmarkheaven","url":"https://eqbench.com/","description":"Measures active emotional and social intelligence of LLMs in multi-turn roleplay chats with simulated personas, ranked by an LLM-judge panel Elo. Maintained by EQ-Bench. Score: Ranked Elo score from blind, bidirectional pairwise judging of multi-turn persona-chat transcripts by a three-judge LLM panel (soft Bradley–Terry fit, normalised against anchor models).","unit":"elo","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-eqbench-creative-writing-3","name":"EQ-Bench Creative Writing v3","shortName":"EQ-Bench Creative Writing v3","sourceId":"benchmarkheaven","url":"https://eqbench.com/creative_writing.html","description":"A LLM-judged creative writing benchmark with vocabulary and GPT-slop controls. Maintained by EQ-bench. Score: Elo Score column in the leaderboard.","unit":"elo","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-eqbench-judgemark-4","name":"Judgemark v4","shortName":"Judgemark v4","sourceId":"benchmarkheaven","url":"https://eqbench.com/judgemark-v4.html","description":"Meta-evaluation that rates LLM judges on how discriminatively they score fixed creative-writing samples. Maintained by EQ-Bench. Score: Composite judge-separation score on the reported 100-point scale (unclamped bounds not verified).","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-eqbench-longform-writing-v1.11","name":"EQ-Bench Longform Creative Writing","shortName":"EQ-Bench Longform Creative Writing","sourceId":"benchmarkheaven","url":"https://eqbench.com/creative_writing_longform.html","description":"LLM-judged benchmark in which models plan and write a short story/novella over 8x 1000-word turns, scored 0-100 across 14 rubric dimensions. Maintained by EQ-Bench. Score: Score (0-100): average of all chapter scores plus the final scored piece across 14 rubric dimensions, with the forced poetry/metaphor criterion weighted 5x and a long-context degradation penalty applied.","unit":"percent","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-eqbench-slop-score","name":"Slop Score","shortName":"Slop Score","sourceId":"benchmarkheaven","url":"https://eqbench.com/slop-score.html","description":"Leaderboard and analysis tool scoring how much model writing resembles AI slop via overused words, contrast patterns, and trigrams. Maintained by Sam Paech. Score: Weighted composite of slop-word frequency (60%), 'not-x-but-y' contrast patterns (25%), and slop trigram frequency (15%) on outputs from a standardized prompt set; lower is better.","unit":"score","higherIsBetter":false,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-gso-opt1-102","name":"GSO software optimization, Opt@1 (UC Berkeley)","shortName":"GSO software optimization, Opt@1","sourceId":"benchmarkheaven","url":"https://gso-bench.github.io/","description":"An agent gets a real codebase and a performance test and must make the code as fast as an expert developer's optimization; 102 tasks across 10 codebases and 5 languages. Maintained by GSO team. Score: Opt@1: share of tasks where a single attempt reaches at least 95 % of the human speedup and passes the correctness tests.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-hyper-tau-bench-release-v1","name":"τ^τ-bench (Hyper-τ), release v1 (Sierra)","shortName":"τ^τ-bench (Hyper-τ), release v1","sourceId":"benchmarkheaven","url":"https://github.com/sierra-research/hyper-tau-bench","description":"A coding agent builds a working customer-service agent from realistic evidence (policies, transcripts, recordings, a client API), and is scored by how well that agent then serves simulated customers on 53 held-out tasks. Maintained by Sierra. Score: Overall: mean reward across all 53 release tasks of the agent the Developer built (τ³-bench inner loop).","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-japanese-rp-bench-aratako","name":"Japanese-RP-Bench (Aratako)","shortName":"Japanese-RP-Bench","sourceId":"benchmarkheaven","url":"https://raw.githubusercontent.com/Aratako/Japanese-RP-Bench/main/README.md","description":"Benchmark measuring LLM Japanese roleplay ability via 10-turn two-model dialogues scored by a four-judge ensemble across eight criteria. Maintained by Aratako. Score: Overall Average = mean evaluation value across the 8 criteria, averaged over 4 judge models (gpt-4o-2024-08-06, o1-mini-2024-09-12, Claude 3.5 Sonnet, Gemini 1.5 Pro).","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-japanese-rp-bench-tegnike-2","name":"Japanese-RP-Bench v2 (tegnike fork)","shortName":"Japanese-RP-Bench v2","sourceId":"benchmarkheaven","url":"https://raw.githubusercontent.com/tegnike/Japanese-RP-Bench/main/README.md","description":"Fork of Japanese-RP-Bench adding v2 metrics for role fidelity, conversation quality, persona stability, robustness, and recovery in Japanese roleplay. Maintained by tegnike. Score: Challenge RP Summary: macro-average of five criteria; leaderboard first sorts Major-free rate descending, then Major rate ascending.","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-livebench-2026-06-25","name":"LiveBench","shortName":"LiveBench","sourceId":"benchmarkheaven","url":"https://livebench.ai/","description":"A periodically refreshed suite of objectively graded tasks with separate category scores and an overall category mean. Maintained by LiveBench team. Score: Overall mean of category averages, with source-published exceptions retained.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-long-horizon-terminal-bench-1.0","name":"Long-Horizon Terminal-Bench","shortName":"Long-Horizon Terminal-Bench","sourceId":"benchmarkheaven","url":"https://zli12321.github.io/LHTB/leaderboard.html","description":"An agent works in a terminal for up to 90 minutes per task on 46 long jobs — building systems, migrating frameworks, playing games move by move — and earns partial credit for real progress. Maintained by Long-Horizon Terminal-Bench authors. Score: Mean reward over the 46 tasks (hidden verifiers pay continuous partial credit from 0 to 1; errors count 0).","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-longbench-2","name":"LongBench v2","shortName":"LongBench v2","sourceId":"benchmarkheaven","url":"https://longbench2.github.io/","description":"503 challenging multiple-choice questions with contexts from 8k to 2M words assessing deep understanding and reasoning on realistic long-context multitasks. Maintained by THUDM. Score: Accuracy on 503 multiple-choice questions across six task categories.","unit":"percent","higherIsBetter":true,"category":"long-context","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-aime-2026","name":"AIME 2026 (MathArena)","shortName":"AIME 2026","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"The 30 integer-answer problems of the 2026 American Invitational Mathematics Examination (AIME I and II), the qualifying exam for the USA Mathematical Olympiad, run by MathArena on the contest problems. Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-apex-2025","name":"Apex 2025 (MathArena)","shortName":"Apex 2025","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"Twelve final-answer problems from 2025 competitions that MathArena selected because strong models of the time failed them; the selection is biased against the four models used to choose it (Grok 4, GPT-5, Gemini 2.5 Pro, GLM 4.5). Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-apex-shortlist-2025","name":"Apex Shortlist 2025 (MathArena)","shortName":"Apex Shortlist 2025","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"47 final-answer problems from 2025 competitions on which at least one of two reference models (Grok 4 Fast, GPT-5 mini) got at least one of four attempts wrong, run by MathArena. Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-arxivmath-2026-06","name":"ArXivMath 06/2026 (MathArena)","shortName":"ArXivMath 06/2026","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"Research-level math problems with a checkable final answer, drawn from arXiv papers submitted in June 2026; each edition is restricted to papers published within the last month to reduce training-data contamination. Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-arxivmath-2026-08","name":"ArXivMath 08/2026 (MathArena)","shortName":"ArXivMath 08/2026","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"Research-level math problems with a checkable final answer, drawn from arXiv papers submitted in August 2026; each edition is restricted to papers published within the last month to reduce training-data contamination. Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-brokenarxiv-2026-06","name":"BrokenArXiv 06/2026 (MathArena)","shortName":"BrokenArXiv 06/2026","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"Plausible but false proof statements taken from June 2026 arXiv papers; a model scores by refusing to prove them and saying the statement is false as written. Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-brokenarxiv-2026-08","name":"BrokenArXiv 08/2026 (MathArena)","shortName":"BrokenArXiv 08/2026","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"Plausible but false statements taken from August 2026 arXiv papers; a model scores by refusing to prove them and saying the statement is false as written. Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-hmmt-2025-11","name":"HMMT November 2025 (MathArena)","shortName":"HMMT November 2025","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"The 30 final-answer problems of the November 2025 Harvard-MIT Mathematics Tournament, one of the largest high-school math competitions in the United States, run by MathArena on the contest problems. Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-hmmt-2026-02","name":"HMMT February 2026 (MathArena)","shortName":"HMMT February 2026","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"The 33 final-answer problems of the February 2026 Harvard-MIT Mathematics Tournament, one of the largest high-school math competitions in the United States, run by MathArena on the contest problems. Maintained by MathArena. Score: Accuracy (average performance on the competition) with a 95% confidence interval (normal approximation).","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-matharena-usamo-2026","name":"USAMO 2026 (MathArena)","shortName":"USAMO 2026","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"The six proof problems of the 2026 USA Mathematical Olympiad, the final round of the American Mathematics Competitions, with each model's proofs graded by MathArena's LLM judges. Maintained by MathArena. Score: Accuracy: average score on the six proofs with a 95% confidence interval (normal approximation); LLM judges grade each proof against a rubric that a model writes from the official solution, mapping a proof to an integer score from 0 to 7.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-mazur-creative-story-writing","name":"LLM Creative Story-Writing Benchmark","shortName":"LLM Creative Story-Writing Benchmark","sourceId":"benchmarkheaven","url":"https://raw.githubusercontent.com/lechmazur/writing/main/README.md","description":"Pairwise comparison of short stories written to the same constrained creative briefs, with LLM evaluator choices combined into a relative comparison score. Maintained by Lech Mazur. Score: Relative comparison score from pairwise LLM evaluator judgments (Thurstone-style rating; zero is near the middle of the comparison set).","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-mazur-divergent-thinking","name":"LLM Divergent Thinking Creativity Benchmark","shortName":"LLM Divergent Thinking Creativity Benchmark","sourceId":"benchmarkheaven","url":"https://raw.githubusercontent.com/lechmazur/divergent/main/README.md","description":"Models generate 25 words maximally distinct from each other and from 50 seed words under starting-letter constraints, scored by LLM-judged word divergence. Maintained by Lech Mazur. Score: Average LLM-judged score of minimum divergences between each generated word and other words, on a 0 to 10 scale (percentage of repeated words also reported).","unit":"score","higherIsBetter":true,"category":"other","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"normalization":{"kind":"linear","min":0,"max":10}},{"id":"bh-mazur-elimination-game","name":"Elimination Game Benchmark","shortName":"Elimination Game Benchmark","sourceId":"benchmarkheaven","url":"https://raw.githubusercontent.com/lechmazur/elimination_game/main/README.md","description":"Multi-player tournament where 8 LLM players converse, form alliances, and vote to eliminate each other, with final rankings scored via TrueSkill. Maintained by Lech Mazur. Score: TrueSkill mean (μ), with σ uncertainty reported separately.","unit":"score","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-mcp-atlas","name":"MCP Atlas (Scale AI)","shortName":"MCP Atlas","sourceId":"benchmarkheaven","url":"https://labs.scale.com/leaderboard/mcp_atlas","description":"How reliably a model completes realistic multi-step workflows over real Model Context Protocol servers: discovering the right tool in a noisy tool menu, calling it with correct parameters, recovering from errors and synthesising the results into an accurate final answer. Maintained by Scale AI. Score: Pass rate in percent over all 1,000 tasks; the published ± is the confidence half-width.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-mcpmark-verified","name":"MCPMark Verified","shortName":"MCPMark Verified","sourceId":"benchmarkheaven","url":"https://mcpmark.ai/leaderboard/verified","description":"AI agents complete realistic multi-step tasks through Model Context Protocol servers for Notion, GitHub, a filesystem, Postgres and a Playwright browser, each checked by the task's own verification script; Verified is the maintainers' version-pinned, stabilized 127-task set. Maintained by EVAL SYS. Score: Pass@1 in percent: the share of the 127 Verified tasks a single run of the model passes, each task decided by its automated verify.py.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-mls-bench-lite-30-tasks","name":"MLS-Bench-Lite","shortName":"MLS-Bench-Lite","sourceId":"benchmarkheaven","url":"https://mls-bench.com/leaderboard","description":"AI agents get a fixed ML research scaffold with strong baselines and must invent one algorithmic change — a loss, optimizer, sampler or training procedure — whose gain holds across settings, seeds, datasets and scales; Lite is the 30-task subset over all 12 research domains. Maintained by MLS-Bench authors. Score: The board's MLS-Bench-Lite score: the paper's normalized task metric, averaged (arithmetic mean) over the 30 Lite tasks, each agent run under Harbor with a 5-hour exploration budget.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-mmmu","name":"MMMU","shortName":"MMMU","sourceId":"benchmarkheaven","url":"https://mmmu-benchmark.github.io/","description":"A multimodal benchmark of 11.5K college-level questions across six disciplines and 30 subjects, with 30 heterogeneous image types. Maintained by MMMU Team. Score: Zero-shot accuracy (percent) on multimodal college-level questions, reported overall and per discipline for the Val and Test splits.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-pingpong-english-2","name":"PingPong English (unadjusted mean)","shortName":"PingPong English","sourceId":"benchmarkheaven","url":"https://ilyagusev.github.io/ping_pong_bench/en_v2","description":"Role-playing benchmark where LLM interrogators emulate users in multi-turn character conversations, and LLM judges rate in-character, entertaining, and fluent qualities. Maintained by Ilya Gusev. Score: Unadjusted Avg score with character:entertain:fluency weights 1:1:1.","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-react-native-evals-91-evals","name":"React Native Evals","shortName":"React Native Evals","sourceId":"benchmarkheaven","url":"https://rn-evals.vercel.app/","description":"Coding agents build React Native app features — navigation, animation, async state, lists, native APIs and Skia graphics — and an LLM judge checks the generated code against each task's written requirements. Maintained by Callstack. Score: The board's overall score in percent: the share of requirements met, summed over all 91 evals and every repeat run (an LLM judge decides each requirement against the generated files).","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-realswe","name":"Real-SWE (Specific Labs)","shortName":"Real-SWE","sourceId":"benchmarkheaven","url":"https://withspecific.com/benchmarks/real-swe","description":"Resolution rate of coding agents on private, licensed enterprise codebases, published as pass@1 over eight runs per task on a ten-task sample. Maintained by Specific Labs. Score: Resolution rate = pass@1 correctness averaged over eight independent runs per task, with a published 95% confidence interval.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-researchclawbench-40-tasks","name":"ResearchClawBench","shortName":"ResearchClawBench","sourceId":"benchmarkheaven","url":"https://internscience.github.io/ResearchClawBench-Home/","description":"An AI agent gets the raw data and related literature behind a published scientific paper — the paper itself hidden — and must redo the research end to end, from analysis to a written report, across 40 tasks in 10 scientific domains. Maintained by ResearchClawBench team. Score: Mean rubric score over the tasks a model has a scored run for, 0–100 (an LLM acting as a strict peer reviewer grades each report against an expert-curated weighted rubric; 50 = the report matches the original paper).","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-rp-bench-community","name":"RP-Bench Community Elo","shortName":"RP-Bench Community Elo","sourceId":"benchmarkheaven","url":"https://huggingface.co/spaces/lazyweasel/rp-bench-leaderboard","description":"A roleplay leaderboard reporting Bayesian community Elo alongside separate multi-turn judge and behavioural evaluations. Maintained by LeviTheWeasel / lazyweasel. Score: Bayesian community Elo.","unit":"elo","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-rsi-exam-0.1","name":"RSI-Exam 0.1 (aiming-lab)","shortName":"RSI-Exam 0.1","sourceId":"benchmarkheaven","url":"https://rsi-exam.ai/","description":"An agent spends a long, budgeted run improving an inherited, working-but-weak executable research method or harness on a visible split, and the single artifact it submits is re-run once on a sealed hidden split across 88 executable research tasks in six domains. Maintained by RSI-Exam Team. Score: Mean frozen normalised hidden-set score (0–1) over the 88 active tasks (Full board).","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-ruler","name":"RULER","shortName":"RULER","sourceId":"benchmarkheaven","url":"https://raw.githubusercontent.com/NVIDIA/RULER/main/README.md","description":"NVIDIA synthetic benchmark that evaluates long-context LLMs with configurable sequence length and task complexity across 13 tasks in 4 categories. Maintained by NVIDIA. Score: Per-length scores at 4K-128K plus Avg. across the 13 RULER tasks (wAvg. inc/dec also reported); performance above the 4K Llama-2-7b threshold (85.6%) is underlined.","unit":"percent","higherIsBetter":true,"category":"long-context","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-scale-swe-bench-pro-v2-full","name":"Scale SEAL: SWE-Bench Pro V2 Full","shortName":"Scale SEAL: SWE-Bench Pro V2 Full","sourceId":"benchmarkheaven","url":"https://labs.scale.com/leaderboard/swe_bench_pro_public_v2","description":"Scale AI’s refreshed public SWE-Bench Pro split (642 long-horizon tasks in 11 copyleft repositories) run under a locked protocol, scored by resolve rate. Maintained by Scale AI. Score: Resolve rate on the full V2 public split (percent); ± is the published confidence half-width.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-scale-swe-bench-pro-v2-hard","name":"Scale SEAL: SWE-Bench Pro V2 HARD","shortName":"Scale SEAL: SWE-Bench Pro V2 HARD","sourceId":"benchmarkheaven","url":"https://scale.com/leaderboard","description":"Scale SEAL: SWE-Bench Pro V2 HARD results published by Scale AI. Maintained by Scale AI. Score: Scale SEAL benchmark score.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"variantOf":"bh-scale-swe-bench-pro-v2-full","variantLabel":"Hard subset"},{"id":"bh-slop-index","name":"The Slop Index","shortName":"The Slop Index","sourceId":"benchmarkheaven","url":"https://theslopindex.com/leaderboard","description":"Leaderboard scoring how much each model's writing reads like chatbot slop versus pre-AI human writing, blending live arena votes with four mechanical axes. Maintained by The Slop Index. Score: Slop Score 0-100 = 40% live arena Elo votes + 15% each for concise, templating, rhythm, and tells measured against pre-AI human writing; higher = sloppier.","unit":"percent","higherIsBetter":false,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-slopbench","name":"SlopBench","shortName":"SlopBench","sourceId":"benchmarkheaven","url":"https://slop-bench.vercel.app/","description":"Runs 53 chat-style prompts and reports the percentage of model responses containing classic AI slop patterns, detected deterministically without an LLM judge. Maintained by Dan Cleary. Score: Slop Rate: percentage of responses with at least one slop-pattern hit (lower is better).","unit":"percent","higherIsBetter":false,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-spiral-bench-1.2","name":"Spiral-Bench","shortName":"Spiral-Bench","sourceId":"benchmarkheaven","url":"https://eqbench.com/spiral-bench.html","description":"A LLM-judged benchmark measuring sycophancy and delusion reinforcement in simulated multi-turn chats. Maintained by sam-paech. Score: Safety Score: weighted average of protective vs risky behaviours, scaled 0-100.","unit":"percent","higherIsBetter":true,"category":"safety","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-surge-chartography-100-tasks","name":"Chartography","shortName":"Chartography","sourceId":"benchmarkheaven","url":"https://surgehq.ai/benchmarks/chartography","description":"Can a model read the charts professionals rely on — Kaplan-Meier curves, candlesticks, contour maps, Sankey diagrams, Bode plots, wind roses — from the image alone, to an expert-set precision? Maintained by Surge AI. Score: The board's accuracy: the share of answers inside the expert-set acceptable range of the golden answer, judged per answer by Gemini 3.5 Flash, averaged over the 100 tasks and ten runs per task.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bh-surge-gdp-pdf-100-tasks","name":"GDP.pdf","shortName":"GDP.pdf","sourceId":"benchmarkheaven","url":"https://surgehq.ai/benchmarks/gdp-pdf","description":"Can a model answer real professional questions about the PDFs work runs on — manuals, wiring diagrams, filings, forms — so that every rubric criterion an expert wrote is met? Maintained by Surge AI. 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Lingma SWE-GPT 7b (v0925)","SWE-agent + Claude 3 Opus","Lingma Agent + Lingma SWE-GPT 7b (v0918)","RAG + Claude 3 Opus","RAG + Claude 2","RAG + SWE-Llama 7B","RAG + ChatGPT 3.5","Nex-N2-mini","K2-Horizon-7B","bu-2-0 · BrowserUse 0.13.7 · BrowserUseCloud browser","bu-v4-luna · BrowserUseCloudAPI v4 · integrated browser","pokee/pokee-isaac@reasoning=enabled","inclusionai/ring-2.6-1t@reasoning=high","inclusionai/ring-2.6-1t@reasoning=xhigh","upstage/solar-pro4@reasoning=enabled","nex-agi/nex-n2-mini","nex-agi/nex-n2-mini@reasoning=enabled","nvidia/nemotron-3-super-120b-a12b@reasoning=enabled","bytedance-seed/seed-2-1-turbo@reasoning=enabled","upstage/solar-pro4","stepfun/step-3.7-flash@reasoning=medium","nvidia/nemotron-3-super-120b-a12b","stepfun/step-3.7-flash@reasoning=high","bytedance-seed/seed-2-1-turbo","poolside/laguna-s-2.1@reasoning=enabled","stepfun/step-3.7-flash@reasoning=low","Grok-Code-Fast-1","Doubao Seed 2.1 Pro","Apex","Ox Alpha","Nemotron 3 Super 120B","Nemotron Nano","Step 3.7 Flash","Step 3.5 Flash","NVIDIA-Nemotron-3-Super","QED-Nano","Falcon-H1R-7B","Thinking Models|LongCat-Flash-Thinking-2601","Thinking Models|Claude-4.5-Opus","Thinking Models|Claude-4.5-Sonnet","Thinking Models|Doubao-Seed-1.8-Thinking (high)","Non-thinking Models|Claude-4.5-Opus","Non-thinking Models|LongCat-Flash-Chat","Non-thinking Models|Claude-4.5-Sonnet","Non-thinking Models|Doubao-Seed-1.8","GPT-4V","Phi-3 Vision","InternVL Chat V1.5","Mini Gemini HD Yi 34B","InternLM XComposer2 4KHD","MiniCPM-V2.5","Qwen VL Max","VILA 1.5 40B","LLaVA 1.6 Yi 34B","InternLM XComposer2","MiniCPM-V2","IDEFICS 2","IDEFICS 2 Chatty","MoAI","DeepSeek VL","SPHINX V2","Qwen VL Plus","Cambrian 34B","ChartGemma","InternVL Chat V2.0 26B","InternVL Chat V2.0 76B","InternVL Chat V2.0 Pro","CogAgent","DocOwl 1.5 Chat","DocOwl 1.5 Omni","UReader","TextMonkey","ChartLlama","ChartAssistant","ChartInstruct-FlanT5","TinyChart","UniChart-ChartQA","MiniCPM-V2.6 (Upsize)","MiniCPM-V2.6 (Upsize+CoT)","InternVL V2.5 78B","InternVL V2.5 38B","InternVL V2.5 26B","InternVL V2.5 8B","InternVL V2.5 4B","InternVL V2.5 2B","InternVL V2.5 1B","Llava OneVision 0.5B","Llava OneVision 7B","Llava OneVision 72B","Molmo 7B D","Molmo 7B O","Molmo 72B","MolmoE 1B","NVLM-D 72B","Phi-3.5 Vision","Pixtral 12B","Doubao 1.5 Pro","InternVL3 1B","InternVL3 2B","InternVL3 8B","InternVL3 9B","InternVL3 14B","InternVL3 38B","InternVL3 78B","smaug-agentic","smaug-mini","ox-alpha-max","smaug-flash","union-alpha","Muse Glimmer 30B (High reasoning)","Muse Glimmer 30B (xHigh reasoning)","Nemotron 3 Nano Omni","Nova 2 Pro (No reasoning)","Seed Evolving-0909 [claude code · max]","glm-5p2","glm-5p1","kimi-k2p5","glm-4p7","Qwen/QwQ-32B","trashpanda-org/QwQ-32B-Snowdrop-v0 (<think> prefill)","trashpanda-org/QwQ-32B-Snowdrop-v0","allura-org/Gemma-3-Glitter-27B","TheDrummer/Cydonia-24B-v4","ReadyArt/Broken-Tutu-24B-Unslop-v2.0","TheDrummer/Cydonia-R1-24B-v4","TheDrummer/Cydonia-24B-v4.1","Gryphe/Pantheon-RP-1.8-24b-Small-3.1","PocketDoc/Dans-PersonalityEngine-V1.2.0-24b","Doctor-Shotgun/MS3.2-24B-Magnum-Diamond","Gryphe/Codex-24B-Small-3.2","zerofata/MS3.2-PaintedFantasy-v2-24B","knifeayumu/Cydonia-v1.3-Magnum-v4-22B","TheDrummer/Rocinante-12B-v1.1","ToastyPigeon/Gemma-3-Starshine-12B","redrix/patricide-12B-Unslop-Mell","DreadPoor/Irix-12B-Model_Stock","LatitudeGames/Wayfarer-12B","Epiculous/Violet_Twilight-v0.2","redrix/patricide-12B-Unslop-Mell-v2","inflatebot/MN-12B-Mag-Mell-R1","MarinaraSpaghetti/NemoMix-Unleashed-12B","ifable/gemma-2-Ifable-9B","lemon07r/Gemma-2-Ataraxy-9B","sam-paech/Darkest-muse-v1","TheDrummer/Gemmasutra-9B-v1","Sao10K/L3-8B-Lunaris-v1","Sao10K/L3-8B-Stheno-v3.2","SanjiWatsuki/Kunoichi-7B","TheDrummer/Cydonia-24B-v3","Retreatcost/KansenSakura-Eclipse-RP-12b","anthropic/claude-3-7-sonnet-20250219 (thinking=disabled)","anthropic/claude-3-7-sonnet-20250219 (thinking=enabled)","Steelskull/L3.3-Shakudo-70b","Steelskull/L3.3-Cu-Mai-R1-70b","Steelskull/L3.3-Cu-Mai-R1-70b (<think> prefill)","NousResearch/Hermes-4-70B","NousResearch/Hermes-4-70B (Hermes-4 reasoning)","TheDrummer/Anubis-70B-v1.1","PocketDoc/Dans-PersonalityEngine-V1.1.0-12b","mlabonne/NeuralDaredevil-8B-abliterated","knifeayumu/Cydonia-v1.2-Magnum-v4-22B","TheDrummer/UnslopNemo-12B-v3","TheDrummer/UnslopNemo-12B-v4.1","upstage/SOLAR-10.7B-Instruct-v1.0","FlareRebellion/WeirdCompound-v1.2-24b (chatml)","OddTheGreat/Mechanism_24B_V.1 (chatml)","SicariusSicariiStuff/Wingless_Imp_8B","yamatazen/FusionEngine-12B-Lorablated","yamatazen/HMS-Fusion-12B-Lorablated","yamatazen/EtherealAurora-12B-v2","Marcjoni/SingularitySynth-12B","yamatazen/HMS-Fusion-12B","DreadPoor/Ward-12B-Model_Stock","yamatazen/Twilight-SCE-12B-v2","mergekit-community/MN-Nyx-Chthonia-12B","cgato/Nemo-12b-Humanize-KTO-Experimental-2","yamatazen/LorablatedStock-12B","zerofata/L3.3-GeneticLemonade-Unleashed-v3-70B","Sao10K/L3.1-70B-Hanami-x1","Tesslate/Synthia-S1-27b (Synthia-S1-27b reasoning)","Tesslate/Synthia-S1-27b","invisietch/L3.3-Ignition-v0.1-70B","sophosympatheia/Strawberrylemonade-L3-70B-v1.2","NeverSleep/NoromaidxOpenGPT4-2","NeverSleep/Noromaid-v0.1-mixtral-8x7b-Instruct-v3","TheDrummer/Anubis-70B-v1","dphn/dolphin-2.5-mixtral-8x7b","TheDrummer/Skyfall-31B-v4","BruhzWater/Sapphira-L3.3-70b-0.1","Steelskull/L3.3-MS-Nevoria-70b","Sao10K/L3-70B-Euryale-v2.1","mistralai/Mixtral-8x7B-Instruct-v0.1","bluuwhale/L3-SthenoMaidBlackroot-8B-V1","TheDrummer/Llama-3SOME-8B-v2","Steelskull/MSM-MS-Cydrion-22B","DavidAU/MN-Dark-Planet-TITAN-12B","SanjiWatsuki/Loyal-Macaroni-Maid-7B","nothingiisreal/MN-12B-Celeste-V1.9","yamatazen/LinearWriter-12B (chatml)","kyx0r/Neona-12B","yamatazen/NeonMaid-12B-v2","Marcjoni/AbyssSynth-12B","Sao10K/MN-12B-Lyra-v1","KOOWEEYUS/BlackSheep-RP-12B","LatitudeGames/Muse-12B","TheDrummer/UnslopNemo-12B-v4","TheDrummer/Moistral-11B-v3","OddTheGreat/Core_24B_V.1","OddTheGreat/Apparatus_24B","NeverSleep/Lumimaid-v0.2-12B","Sao10K/MN-12B-Lyra-v4","LatitudeGames/Wayfarer-2-12B","TheDrummer/Cydonia-22B-v1","allura-org/MS-Meadowlark-22B","concedo/Beepo-22B","yamatazen/EtherealAurora-12B","yamatazen/EsotericSage-12B","ohyeah1/Violet-Lyra-Gutenberg-v2","Foxlum/NeonViolin","yamatazen/BlueLight-12B","DreadPoor/YM-12B-Model_Stock","yamatazen/NeonMaid-12B","DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS","NousResearch/Hermes-4-405B","alpindale/WizardLM-2-8x22B","mistralai/Mixtral-8x22B-Instruct-v0.1","ai21labs/AI21-Jamba-Large-1.7","ai21labs/AI21-Jamba-Mini-1.7","HiroseKoichi/L3-8B-Lunar-Stheno","Nitral-AI/Hathor_Sofit-L3-8B-v1","yamatazen/SnowElf-12B","GreenerPastures/Golden-Curry-12B","ReadyArt/Forgotten-Abomination-12B-v4.0 (metharme)","FallenMerick/MN-Violet-Lotus-12B","Nitral-AI/Captain_BMO-12B","TheDrummer/UnslopSmall-22B-v1 (metharme)","TheDrummer/Cydonia-24B-v2","anthracite-org/magnum-v4-22b","Nitral-AI/Captain-Eris_Violet-V0.420-12B","FlareRebellion/WeirdCompound-v1.1-24b (chatml)","Sao10K/Fimbulvetr-11B-v2","TheDrummer/Tiger-Gemma-9B-v3","openai/chatgpt-4o-latest-20250326","ToastyPigeon/g3-27b-beepo-mmtest","ReadyArt/Forgotten-Safeword-24B","darkc0de/XortronCriminalComputingConfig","Entropicengine/Pinecone-sage-24b","FlareRebellion/WeirdCompound-v1.3-24b","trashpanda-org/MS-24B-Mullein-v0","FlareRebellion/WeirdCompound-v1.5-24b","darkc0de/BlackXorDolphTronGOAT","SicariusSicariiStuff/Redemption_Wind_24B","TheDrummer/Cydonia-22B-v1.3","Kaoeiri/MS-Magpantheonsel-lark-v4x1.6.2RP-Cydonia-vXXX-22B-8.05","mergekit-community/MN-Hekate-Anassa-17B","DavidAU/L3-DARKEST-PLANET-16.5B","TheDrummer/Snowpiercer-15B-v2","TheDrummer/Snowpiercer-15B-v2 (<think> prefill)","Triangle104/Herodotos-14B","w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored","yamatazen/Gemma2-Ataraxy-Psycho-9B","yamatazen/Gemma2-Evelyn-9B","yamatazen/Gemma2-Alicia-9B","yamatazen/Gemma2-Snowflakes-9B","TheDrummer/Tiger-Gemma-9B-v2","Vikhrmodels/Vikhr-YandexGPT-5-Lite-8B-it","DreadPoor/BaeZel-8B-LINEAR","DreadPoor/Aspire-8B-model_stock","Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B","SicariusSicariiStuff/Dusk_Rainbow","SicariusSicariiStuff/Impish_Mind_8B","TroyDoesAI/BlackSheep-X-Dolphin","Entropicengine/LatentSoup-modelstock-8b","internlm/internlm3-8b-instruct","DreadPoor/Suavemente-8B-Model_Stock","Local-Novel-LLM-project/Vecteus-v1","SicariusSicariiStuff/Zion_Alpha_Instruction_Tuned","WhiteRabbitNeo/WhiteRabbitNeo-V3-7B","huihui-ai/Huihui-MoE-5B-A1.7B-abliterated (thinking=False)","huihui-ai/Huihui-MoE-5B-A1.7B-abliterated (thinking=True)","SicariusSicariiStuff/Impish_LLAMA_4B","soob3123/Veiled-Calla-4B","SicariusSicariiStuff/X-Ray_Alpha","POLARIS-Project/Polaris-4B-Preview (thinking=False)","POLARIS-Project/Polaris-4B-Preview (thinking=True)","OddTheGreat/Meteor_4B_V.1","TheDrummer/Gemmasutra-Mini-2B-v1","SaisExperiments/Evil-Alpaca-3B-L3.2","SicariusSicariiStuff/2B-ad","SicariusSicariiStuff/2B_or_not_2B","XeTute/HamzahLMV1-1B","Novaciano/Eurinome-3.2-1B","Novaciano/La_Mejor_Mezcla-3.2-1B","Triangle104/Hermes3-BlackSheep-3B","TroyDoesAI/BlackSheep-24B","yamatazen/FlickeringLight-14B","WhiteRabbitNeo/Trinity-13B","yamatazen/Ayla-Light-Extra (chatml)","yamatazen/Orihime-Gutenberg-12B","Nitral-AI/Violet_Magcap-12B","TheDrummer/Tiger-Gemma-9B-v1","NeverSleep/Lumimaid-v0.2-8B","yamatazen/L3-GothicMaid-8B","Vikhrmodels/Vikhr-Gemma-2B-instruct","Novaciano/Ahriman-3.2-1B","Novaciano/BLAST_PROCESSING-3.2-1B","NickyNicky/Llama-1B-GRPO_Final","Novaciano/MorganLeFai-3.2-1B","Novaciano/Pseudomartys-3.2-1B","Novaciano/Pandemonium-3.2-1B","Novaciano/ASTAROTH-3.2-1B","Novaciano/BAPHOMET","TheDrummer/Behemoth-X-123B-v2","TheDrummer/Anubis-Pro-105B-v1","TheDrummer/Behemoth-ReduX-123B-v1","TheDrummer/Cydonia-R1-24B-v4.1","TheDrummer/Valkyrie-49B-v1","TheDrummer/Valkyrie-49B-v1 (<think> prefill)","TheDrummer/Nautilus-70B-v0.1","Sao10K/70B-L3.3-Cirrus-x1","sophosympatheia/StrawberryLemonade-L3-70B-v1.0","Steelskull/L3.3-Electra-R1-70b","Steelskull/L3.3-Electra-R1-70b (<think> prefill)","Delta-Vector/MS3.2-Austral-Winton","aixonlab/Eurydice-24b-v3.5 (chatml)","PocketDoc/Dans-PersonalityEngine-V1.3.0-24b","Retreatcost/KansenSakura-Radiance-RP-12b","mergekit-community/Arisu-12B","ReadyArt/Broken-Tutu-24B-Transgression-v2.0","yamatazen/Aurora-SCE-12B","TheDrummer/Snowpiercer-15B-v1","TheDrummer/Snowpiercer-15B-v1 (<think> prefill)","anthracite-org/magnum-v2-12b","LatitudeGames/Harbinger-24B","alpindale/goliath-120b","TheDrummer/Behemoth-R1-123B-v2","TheDrummer/Behemoth-R1-123B-v2 (<think> prefill)","MarsupialAI/Monstral-123B-v2","TheDrummer/Behemoth-123B-v2.1","Sicarius-Prototyping/X-Ray_Alpha_27B_Base","ReadyArt/The-Omega-Directive-M-12B-v1.0","Luni/StarDust-12b-v2","PocketDoc/Dans-PersonalityEngine-V1.3.0-12b","PocketDoc/Dans-SakuraKaze-V1.0.0-12b","Delta-Vector/Archaeo-12B","anthropic/claude-3-5-haiku-20241022","ReadyArt/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B","TheDrummer/Snowpiercer-15B-v3","intervitens/mini-magnum-12b-v1.1","MarinaraSpaghetti/NemoRemix-12B","ReadyArt/Broken-Tutu-24B","Trappu/Magnum-Picaro-0.7-v2-12b","SicariusSicariiStuff/Oni_Mitsubishi_12B","mergekit-community/MN-Hekate-Enodia-12B","icefog72/IceMoonshineRP-7b","icefog72/IceMedovukhaRP-7b","FlareRebellion/WeirdCompound-v1.6-24b","DoppelReflEx/MiniusLight-24B-v3","wave-on-discord/silly-v0.2","yamatazen/EtherealAurora-12B-Lorablated","yamatazen/DellaMix-12B","yamatazen/Shisa-K-12B","Fentible/Eldrinox-24B-v1","Fentible/BlackDolphin-24B","allura-org/MS3.2-24b-Angel","OddTheGreat/Circuitry_24B_V.2","ByteDance-Seed/Seed-OSS-36B-Instruct (- budget 512)","ByteDance-Seed/Seed-OSS-36B-Instruct (- budget 0)","ByteDance-Seed/Seed-OSS-36B-Instruct","zerofata/MS3.2-PaintedFantasy-Visage-v4-34B","DreadPoor/Famino-12B-Model_Stock","DreadPoor/Krix-12B-Model_Stock","D1rtyB1rd/Looking-Glass-Alice-Thinking-NSFW-RP","ReadyArt/Safeword-Casual-V1-12B","zerofata/MS3.2-PaintedFantasy-Visage-v3-34B","ReadyArt/C4.1-Broken-Tutu-24B","TheDrummer/RimTalk-Mini-v1","Nitral-AI/CaptainErisNebula-12B-Chimera-v1.1","Retreatcost/KansenSakura-Zero-RP-12b","D1rtyB1rd/Egregore-Alice-RP-NSFW-12B","DreadPoor/Cooked-12B-KARCHER","TheDrummer/Valkyrie-49B-v2","TheDrummer/Valkyrie-49B-v2 (<think> prefill)","BruhzWater/Sapphira-L3.3-70b-0.2","BruhzWater/Edens-Fall-L3.3-70b-0.3b","Darkhn/L3.3-70B-Animus-V12.0","SicariusSicariiStuff/Impish_QWEN_14B-1M","CohereForAI/c4ai-command-r-plus-08-2024","CohereForAI/c4ai-command-r-08-2024","ibm-granite/granite-4.0-h-small","SicariusSicariiStuff/Impish_Nemo_12B","mergekit-community/mergekit-model_stock-prczfmj","ReadyArt/L3.3-The-Omega-Directive-70B-Unslop-v2.0","HelpingAI/HELVETE-X","FlareRebellion/WeirdCompound-v1.7-24b","TheDrummer/Cydonia-24B-v4.2.0","TheDrummer/Ministrations-8B-v1","dphn/Dolphin-X1-8B","Nitral-AI/Irixxed-Magcap-12B-Slerp","TheDrummer/Cydonia-Redux-22B-v1.1","TheDrummer/Magidonia-24B-v4.2.0","Retreatcost/Ollpheist-12B","yamatazen/ForgottenMaid-12B","ConicCat/humans.txt-Diverse-OrPO-24B","PantheonUnbound/Satyr-V0.1-4B","HelpingAI/Dhanishtha-nsfw","Darkhn/L3.1-8B-Animus-V12.2","Darkhn/L3.1-8B-Animus-V12.1","yvvki/Erotophobia-24B-v2.0","ReadyArt/The-Omega-Directive-M-8B-v1.0","tiiuae/Falcon-H1-7B-Instruct","Retreatcost/KansenSakura-Erosion-RP-12b","Sicarius-Prototyping/Impish_Longtail_12B","tiiuae/Falcon-H1-0.5B-Instruct","tiiuae/Falcon-H1-1.5B-Instruct","tiiuae/Falcon-H1-1.5B-Deep-Instruct","tiiuae/Falcon-H1-3B-Instruct","KaraKaraWitch/GoldDiamondGold-L33-70b","open-r1/OlympicCoder-32B","ReadyArt/4.2.0-Broken-Tutu-24b","Gryphe/Pantheon-Proto-RP-1.8-30B-A3B","ApocalypseParty/Magi-PT-2-SFT-2","ApocalypseParty/Magi-PT-2-SFT-1-DPO-3","allura-org/Tlacuilo-12B","inclusionAI/Ling-1T","inclusionAI/Ring-1T","CohereLabs/c4ai-command-a-03-2025","TheDrummer/Agatha-111B-v1","TareksGraveyard/L3.3-TRP-BASE-80-70B","TheDrummer/GLM-Steam-106B-A12B-v1","TheDrummer/GLM-Steam-106B-A12B-v1 (/nothink)","m8than/L3.3-Chimera-Prime-70B","Ewere/Seed-OSS-36B-Erotic","Ewere/Seed-OSS-36B-Erotic (- budget 0)","MrRikyz/Foxfire_Bloom","Aleteian/Sexpedition-MS3.2-24B","mergekit-community/Tigers-Abliterated-9B","TheDrummer/Gemmasutra-27B-v3","icefog72/IceAbsintheRP-7b","bgg1996/Melinoe-14B (thinking=False)","bgg1996/Melinoe-14B (thinking=True)","AllThingsIntel/Apollo-V0.1-4B-Thinking","TheDrummer/Gemma-3-R1-27B-v1","ReadyArt/Dark-Oddity-12B-v1.0","DreadPoor/Famino_ALT-12B-Model_Stock","DreadPoor/Mangione-12B-Model_Stock","Babsie/Trickster-Theta-4-70B","Babsie/BrownLoafers-70B","google/medgemma-27b-text-it","SicariusSicariiStuff/Hebrew_Nemo","ReadyArt/Dark-Nexus-32B-v2.0 (thinking=False)","ReadyArt/Dark-Nexus-32B-v2.0 (thinking=True)","allura-org/Q3-30b-A3b-Pentiment (thinking=False)","allura-org/Q3-30b-A3b-Pentiment (thinking=True)","soob3123/Veiled-Calla-12B","arcee-ai/Arcee-Blitz","arcee-ai/Homunculus (thinking=False)","arcee-ai/Homunculus 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of 168 rollouts passed · verifier 31.5% · pass@3 17.9% · cost estimated","7 of 168 rollouts passed · verifier 7.1% · pass@3 8.9% · cost estimated","Opus 5.5 (xhigh ~Estimated) (score estimated by the author) · median of runs, best 81.3","Fable 5.1 (xhigh ~Estimated) (score estimated by the author) · median of runs, best 76.43","deprecated on AA; estimated by AA (not all component evals run); carried from 2026-10-27; carried from 2026-10-27; carried from 2026-10-27; carried from 2026-10-27; carried from 2026-10-27; carried from 2026-10-27; carried from 2026-10-27; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-10-28; carried from 2026-11-04; 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