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Artificial Analysis' run of Cognition's FrontierCode 1.1, separate from Cognition's own board.","unit":"percent","higherIsBetter":true,"category":"coding","defaultWeight":0,"eligibleForDefault":false,"categories":["coding","agentic"]},{"id":"aa-frontiercode-2-0","name":"FrontierCode 2.0-AA","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/frontiercode-2-0","description":"Would a maintainer merge it? An agent makes changes in real open-source repositories (132 private tasks, three attempts each) and is graded on correctness, design and tests; failing any blocking check scores 0. Artificial Analysis' run of Cognition's FrontierCode 2.0, separate from Cognition's own board.","unit":"percent","higherIsBetter":true,"category":"coding","defaultWeight":15,"eligibleForDefault":true,"versionPolicy":{"kind":"fresh","supersedes":"aa-frontiercode"},"categories":["coding","agentic"]},{"id":"aa-gdp-pdf","name":"GDP.pdf","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/gdp-pdf","description":"Professional questions about the PDFs real work runs on: manuals, wiring diagrams, filings and forms. Share of tasks where the answer meets every criterion an expert wrote.","unit":"percent","higherIsBetter":true,"category":"professional","defaultWeight":5,"eligibleForDefault":true,"categories":["professional","long-context","vision"]},{"id":"aa-gdpval-aa","name":"GDPval-AA v2.1","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/gdpval-aa","description":"Real knowledge-work assignments from many occupations, each producing a finished deliverable. 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The refined version of Humanity's Last Exam, from CAIS and Scale AI.","unit":"percent","higherIsBetter":true,"category":"knowledge","defaultWeight":10,"eligibleForDefault":true,"versionPolicy":{"kind":"fresh","supersedes":"aa-humanitys-last-exam"},"categories":["knowledge","reasoning","science"]},{"id":"aa-humanitys-last-exam","name":"Humanity's Last Exam","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/humanitys-last-exam","description":"Expert-written questions at the frontier of many academic fields, built to be too hard for today's models. Share correct.","unit":"percent","higherIsBetter":true,"category":"knowledge","defaultWeight":0,"eligibleForDefault":false,"categories":["knowledge","reasoning","science"]},{"id":"aa-ifbench","name":"IFBench","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/ifbench","description":"Does the model do exactly what it is told? Each prompt carries precise constraints that can be checked automatically. 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Scored from -100 to 100: right answers add, wrong answers subtract and declining to answer costs nothing, so guessing is punished. v1.1 drops saturated and public questions; scores run about 2 points below v1.","normalization":{"kind":"linear","min":-100,"max":100},"unit":"score","higherIsBetter":true,"category":"knowledge","defaultWeight":0,"eligibleForDefault":false,"versionPolicy":{"kind":"fresh","supersedes":"aa-omniscience"},"categories":["knowledge"]},{"id":"aa-omniscience-v1-1-accuracy","name":"AA-Omniscience v1.1 Accuracy","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/omniscience","description":"The share of AA-Omniscience v1.1 questions answered correctly, with no penalty for wrong answers.","unit":"percent","higherIsBetter":true,"category":"knowledge","defaultWeight":10,"eligibleForDefault":true,"versionPolicy":{"kind":"fresh","supersedes":"aa-omniscience-accuracy"},"variantOf":"aa-omniscience-v1-1","variantLabel":"Accuracy","categories":["knowledge"]},{"id":"aa-omniscience-v1-1-business","name":"AA-Omniscience v1.1 Business","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/omniscience","description":"Only the Business questions of AA-Omniscience v1.1: hard factual questions scored from -100 to 100, where right answers add, made-up answers subtract and declining to answer costs nothing.","normalization":{"kind":"linear","min":-100,"max":100},"unit":"score","higherIsBetter":true,"category":"knowledge","defaultWeight":0,"eligibleForDefault":false,"variantOf":"aa-omniscience-v1-1","variantLabel":"Business","categories":["knowledge","professional"]},{"id":"aa-omniscience-v1-1-health","name":"AA-Omniscience v1.1 Health","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/omniscience","description":"Only the Health questions of AA-Omniscience v1.1: hard factual questions scored from -100 to 100, where right answers add, made-up answers subtract and declining to answer costs nothing.","normalization":{"kind":"linear","min":-100,"max":100},"unit":"score","higherIsBetter":true,"category":"knowledge","defaultWeight":0,"eligibleForDefault":false,"variantOf":"aa-omniscience-v1-1","variantLabel":"Health","categories":["knowledge","professional"]},{"id":"aa-omniscience-v1-1-humanities","name":"AA-Omniscience v1.1 Humanities & Social Sciences","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/omniscience","description":"Only the Humanities & Social Sciences questions of AA-Omniscience v1.1: hard factual questions scored from -100 to 100, where right answers add, made-up answers subtract and declining to answer costs nothing.","normalization":{"kind":"linear","min":-100,"max":100},"unit":"score","higherIsBetter":true,"category":"knowledge","defaultWeight":0,"eligibleForDefault":false,"variantOf":"aa-omniscience-v1-1","variantLabel":"Humanities & Social Sciences","categories":["knowledge"]},{"id":"aa-omniscience-v1-1-law","name":"AA-Omniscience v1.1 Law","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/omniscience","description":"Only the Law questions of AA-Omniscience v1.1: hard factual questions scored from -100 to 100, where right answers add, made-up answers subtract and declining to answer costs nothing.","normalization":{"kind":"linear","min":-100,"max":100},"unit":"score","higherIsBetter":true,"category":"knowledge","defaultWeight":0,"eligibleForDefault":false,"variantOf":"aa-omniscience-v1-1","variantLabel":"Law","categories":["knowledge","professional"]},{"id":"aa-omniscience-v1-1-non-hallucination","name":"AA-Omniscience v1.1 Non-hallucination","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/omniscience","description":"Of the AA-Omniscience v1.1 questions the model did not get right, the share where it declined or was partly right instead of giving a wrong answer. Higher means fewer made-up answers. Not the combined Omniscience Index.","unit":"percent","higherIsBetter":true,"category":"knowledge","defaultWeight":5,"eligibleForDefault":true,"versionPolicy":{"kind":"fresh","supersedes":"aa-omniscience-non-hallucination"},"variantOf":"aa-omniscience-v1-1","variantLabel":"Non-hallucination","categories":["knowledge"]},{"id":"aa-omniscience-v1-1-science","name":"AA-Omniscience v1.1 Science, Engineering & Math","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/omniscience","description":"Only the Science, Engineering & Math questions of AA-Omniscience v1.1: hard factual questions scored from -100 to 100, where right answers add, made-up answers subtract and declining to answer costs nothing.","normalization":{"kind":"linear","min":-100,"max":100},"unit":"score","higherIsBetter":true,"category":"knowledge","defaultWeight":0,"eligibleForDefault":false,"variantOf":"aa-omniscience-v1-1","variantLabel":"Science, Engineering & Math","categories":["knowledge","science"]},{"id":"aa-omniscience-v1-1-software-engineering","name":"AA-Omniscience v1.1 Software Engineering","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/omniscience","description":"Only the Software Engineering questions of AA-Omniscience v1.1: hard factual questions scored from -100 to 100, where right answers add, made-up answers subtract and declining to answer costs nothing.","normalization":{"kind":"linear","min":-100,"max":100},"unit":"score","higherIsBetter":true,"category":"knowledge","defaultWeight":0,"eligibleForDefault":false,"variantOf":"aa-omniscience-v1-1","variantLabel":"Software Engineering","categories":["knowledge","coding"]},{"id":"aa-openness-index","name":"AA Openness Index","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/artificial-analysis-openness-index","description":"How open a model is, from 0 to 100: whether its weights are available and under what license, and how transparent the lab is about training data and methods. Not a capability measure.","normalization":{"kind":"linear","min":0,"max":100},"unit":"score","higherIsBetter":true,"category":"other","defaultWeight":0,"eligibleForDefault":false,"categories":["other"]},{"id":"aa-output-speed","name":"Output Speed (tokens/s)","shortName":"Output Speed","sourceId":"aa","url":"https://artificialanalysis.ai/models#speed","description":"Output tokens per second over a request's full duration, so time to first token and reasoning tokens count. AA averages its workloads (1k, 10k and 100k prompts, and multi-turn agent sessions) over the past 5 days, and a request that fails on the endpoint's side counts as 0. Read from the lab's own API where AA has one, otherwise the median across providers.","unit":"count","higherIsBetter":true,"category":"speed","defaultWeight":0,"categories":["speed"]},{"id":"aa-personality-conviction","name":"AA-PersonalityBench Conviction","shortName":"Conviction","sourceId":"aa-personality","url":"https://artificialanalysis.ai/evaluations/aa-personalitybench","description":"A personality trait, not a skill: direct, confident, opinionated, and willing to push back. A percentile from 1 to 100 among the models Artificial Analysis tested, from AI-judged side-by-side comparisons. 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Built for trends over time; the Intelligence Index measures today's frontier.","unit":"score","higherIsBetter":true,"category":"composite","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"eligibleForDefault":false,"categories":["composite"]},{"id":"aa-robotics","name":"AA-Robotics","shortName":"AA-Robotics","sourceId":"aa-robotics","url":"https://artificialanalysis.ai/articles/aa-robotics","description":"A model, working through its lab's coding agent, directly controls a 6-axis robot arm through everyday tabletop tasks such as pressing a button, pouring a drink and writing \"hello world\". Mean score per task from 0 to 100, with partial credit for progress.","normalization":{"kind":"linear","min":0,"max":100},"unit":"score","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["agentic"]},{"id":"aa-scicode","name":"SciCode","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/scicode","description":"Writing the code behind real scientific research from a paper's description. Share of sub-problems solved. Under review at Artificial Analysis: a preprint (SciCode-Verified) reports dataset defects that reject correct solutions, and AA dropped SciCode from its Intelligence Index in v5.0.","unit":"percent","higherIsBetter":true,"category":"science","defaultWeight":0,"eligibleForDefault":false,"categories":["science","coding"]},{"id":"aa-tau2-bench","name":"τ²-Bench Telecom","sourceId":"aa","url":"https://artificialanalysis.ai/evaluations/tau2-bench","description":"Customer support for a telecom: the model talks with a simulated customer and uses tools, while the customer also has to act on their own device. 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Measures what people prefer, not what is correct.","lastUpdated":"2026-11-09","unit":"elo","higherIsBetter":true,"category":"vision","eligibleForDefault":false,"defaultWeight":0,"variantOf":"arena-vision","variantLabel":"OCR","categories":["vision","preference"]},{"id":"arena-webdev","name":"Arena WebDev","sourceId":"arena","url":"https://arena.ai/leaderboard/code/webdev","description":"Which web app people prefer: two anonymous models each build a working front end from the same prompt and users vote. A rating from those votes.","lastUpdated":"2026-11-11","unit":"elo","higherIsBetter":true,"category":"coding","eligibleForDefault":true,"defaultWeight":5,"categories":["coding","creative","preference"]},{"id":"arena-webdev-html","name":"Arena WebDev · HTML","sourceId":"arena","url":"https://arena.ai/leaderboard/code/webdev","description":"The \"HTML\" slice of Arena WebDev: which of two anonymous models' working front ends people prefer, counting only those prompts.","lastUpdated":"2026-11-11","unit":"elo","higherIsBetter":true,"category":"coding","eligibleForDefault":false,"defaultWeight":0,"variantOf":"arena-webdev","variantLabel":"HTML","categories":["coding","creative","preference"]},{"id":"arena-webdev-react","name":"Arena WebDev · React","sourceId":"arena","url":"https://arena.ai/leaderboard/code/webdev","description":"The \"React\" slice of Arena WebDev: which of two anonymous models' working front ends people prefer, counting only those prompts.","lastUpdated":"2026-11-11","unit":"elo","higherIsBetter":true,"category":"coding","eligibleForDefault":false,"defaultWeight":0,"variantOf":"arena-webdev","variantLabel":"React","categories":["coding","creative","preference"]},{"id":"automationbench","name":"AutomationBench (Zapier)","shortName":"AutomationBench","sourceId":"zapier","url":"https://zapier.com/benchmarks","unit":"percent","higherIsBetter":true,"category":"agentic","eligibleForDefault":true,"defaultWeight":0,"description":"End-to-end business workflows across 47 real tools in six business functions, built by Zapier. A task passes only if the final state of every app is exactly right. Private held-out set.","categories":["agentic","professional"]},{"unit":"percent","higherIsBetter":true,"category":"vision","normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":true,"defaultWeight":0,"url":"https://unipat.ai/benchmarks/BabyVision","sourceId":"unipat-babyvision","id":"babyvision","name":"BabyVision","description":"Visual tasks a young child handles easily: tracking lines, completing patterns, simple picture puzzles (388 items, 22 task types). Share correct without tools; humans score 94.1%. 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An older benchmark that current models have nearly maxed out; random guessing scores 25%. Via Epoch AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science","knowledge"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-arena-hard-auto","name":"Arena-Hard-Auto v0.1","shortName":"Arena-Hard-Auto v0.1","sourceId":"boggs","url":"https://github.com/lmarena/arena-hard-auto","description":"Hard real user prompts collected from Arena and graded automatically: a judge model compares each answer with a GPT-4 baseline. An older board. 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Share solved, as measured by Artificial Analysis. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science","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":"A short, open-ended request of the kind a finance professional gets, inside a folder full of documents: work out what is needed and deliver finished files (80 tasks, about 16 hours of human work each). A task passes only if every item on an expert checklist is met. From Surge AI. 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The best score reached within the 12 hours, averaged over tasks. From ByteDance Seed. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","categories":["agentic","science","coding"],"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":"The agent works as support and operations staff at a simulated online PC-hardware store, using 23 tools to check warranties, fix delayed shipments and follow company policy. A task counts only if every expert criterion is met. From Surge AI. 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The share of tasks where the final submission beats the human reference on every measure and hidden workload.","unit":"percent","higherIsBetter":true,"category":"science","versionPolicy":{"kind":"fresh"},"normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":false,"defaultWeight":0,"lastUpdated":"2026-11-06","categories":["science","coding","agentic"]},{"id":"bg-frontierml","name":"FrontierML","shortName":"FrontierML","sourceId":"boggs","url":"https://www.frontierml.org/","description":"21 long machine-learning projects, such as turning a language model into a text-diffusion model, done with a wallet of $10, $50 or $200 to spend on compute and data. Scored on how the finished model does on held-out tests, averaged across wallet sizes. From Induction Labs. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science","coding","agentic"],"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":"Models play full games against each other: chess, heads-up poker and the social-deduction game Werewolf. One pooled rating from the results. From Kaggle and Google DeepMind. Collected by Jake Boggs' benchmark dashboard.","unit":"elo","higherIsBetter":true,"category":"games","categories":["games"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-gamedevbench","name":"GameDevBench","shortName":"GameDevBench","sourceId":"boggs","url":"https://waynechi.com/gamedevbench/","description":"333 game-development tasks in the Godot engine, covering 2D and 3D graphics, interface and gameplay; the agent edits code, shaders, sprites and scenes. Share solved on the first try, using each model's best setup. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","categories":["coding","agentic","vision"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-gba-eval","name":"GBA Eval","shortName":"GBA Eval","sourceId":"boggs","url":"https://gbaeval.com/","description":"Write a working Game Boy Advance emulator from scratch, with no internet and no existing emulator code. Scored by running real games and hardware tests on it and comparing the output with a reference emulator. From Mechanize. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","categories":["coding","agentic"],"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":"Questions about a realistic Excel workbook, such as a company financial model, lab results or supply-chain data: the agent traces the evidence through the sheets and answers. The average share of rubric criteria met. From Surge AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"professional","categories":["professional","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":"SemiAnalysis's long frontier-work tasks across fields such as chip design, finance, law and materials science. This reading uses partial credit with fields weighted equally and leaves out biology, cybersecurity and alignment, where safety classifiers lower scores. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"professional","categories":["professional","science","agentic"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-gsm8k","name":"GSM8K","shortName":"GSM8K","sourceId":"boggs","url":"https://epoch.ai/benchmarks/gsm8k","description":"Grade-school math word problems. An older benchmark that current models have nearly maxed out. Share with exactly the right answer. Via Epoch AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"math","categories":["math"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-halluhard","name":"HalluHard","shortName":"HalluHard","sourceId":"boggs","url":"https://halluhard.com/","description":"Open questions on legal cases, research, medical guidelines and coding, where the model must back its claims with sources. The share of checked claims whose source does not exist or does not support them, on the first turn without web search. Lower is better. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":false,"category":"knowledge","categories":["knowledge"],"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":"The agent gets a long company handbook and does the routine office work it governs, inside a simulated company with email, chat, calendar and ticketing, while users push for things the rules forbid. A trial passes only if every automatic check holds. From Surge AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","categories":["agentic","professional","behavior"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-hellaswag","name":"HellaSwag","shortName":"HellaSwag","sourceId":"boggs","url":"https://epoch.ai/benchmarks/hella-swag","description":"Pick the most sensible ending to a short everyday scene, from four choices. An older common-sense test; random guessing scores 25%. Via Epoch AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","categories":["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":"Stanford's HELM Lite: a broad, older suite of question answering, reasoning, math, law, medicine and translation tests. The value is how often a model beats the others across the core tests. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"knowledge","categories":["knowledge","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":"150 everyday agent tasks in a workspace with real files: job search, email triage, research, diagrams, code review and more, run in Nous Research's Hermes Agent. Mean score per task, with partial credit. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","categories":["agentic"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-humaneval","name":"HumanEval","shortName":"HumanEval","sourceId":"boggs","url":"https://llm-stats.com/benchmarks/humaneval","description":"Short Python functions to write from a description, checked by tests. An older benchmark that current models have nearly maxed out. Share solved on the first try. Via LLM Stats. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","categories":["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":"IT help-desk tickets in a simulated company with dozens of mocked systems: find the root cause, follow the runbook, fix it and leave everything consistent (89 tasks). Share solved in one run, verified by automatic checks. From Atomicwork. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","categories":["agentic","professional"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-job-bench","name":"JobBench","shortName":"JobBench","sourceId":"boggs","url":"https://job-bench.github.io/","description":"A dossier of messy real-work files for one of 35 occupations, and the professional deliverables to produce from it, such as a memo, a workbook and a source log. A weighted score from fact-based rubric criteria, judged by an AI model. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"professional","categories":["professional","agentic"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-matharena","name":"MathArena","shortName":"MathArena","sourceId":"boggs","url":"https://matharena.ai/models","description":"MathArena's overall estimate of how a model would do across everything it runs, from recent competition problems to research-level ones. Expected share solved. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"math","categories":["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":"Math questions that depend on a picture: charts, geometry figures, tables and diagrams. An older benchmark; share correct, where random guessing scores 17.9%. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"vision","categories":["vision","math"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-maze-bench","name":"MazeBench","shortName":"MazeBench","sourceId":"boggs","url":"https://mazebench.com/leaderboard/ai","description":"The agent explores a world of connected rooms and solves block-pushing puzzles to collect hidden gems, seeing the world as text and using tools. The share of gems collected. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"games","categories":["games","agentic","reasoning"],"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":"Long, multi-app assignments for three jobs, corporate lawyer, investment-banking analyst and management consultant, written by people from top firms. The share of tasks where one attempt meets the whole rubric, at each model's best. From Mercor. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"professional","categories":["professional","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":"The agent works alone for days to improve the training recipe of a small GPT so it reaches a set quality in fewer steps. The share of the gap closed between the baseline and the human record; 100% ties the record. From Prime Intellect. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science","coding","agentic"],"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":"Models play multi-player poker at tables against other models. A rating from who ends each table with more chips, with human players left out. From Olam Labs. Collected by Jake Boggs' benchmark dashboard.","unit":"elo","higherIsBetter":true,"category":"games","categories":["games"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-openbookqa","name":"OpenBookQA","shortName":"OpenBookQA","sourceId":"boggs","url":"https://epoch.ai/benchmarks/open-book-qa","description":"Elementary science questions with four choices that need a known fact plus a step of reasoning. An older benchmark; random guessing scores 25%. Via Epoch AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science","knowledge"],"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":"The puzzle game Opus Magnum played without visuals: the model builds a machine by coordinates in code, then optimizes it. Each solution is compared with the human world record; 100% would match the record on every puzzle. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"games","categories":["games","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":"Simple look-and-answer questions about an image that need no reasoning: counting, reading text, comparing, spotting what is missing (3,000 questions). Share correct. From Moonshot AI's Kimi team. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"vision","categories":["vision"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-ple-bench","name":"PLE-Bench","shortName":"PLE-Bench","sourceId":"boggs","url":"https://ple-bench.com/leaderboard","description":"Real engineering projects, structural, civil, electrical or mechanical, with their drawings, spreadsheets and specs: the agent has up to 6 hours to produce the calculations and analysis. Mean rubric score on the 56 hidden tasks, each model in its best harness. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science","professional","agentic"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-posttrainbench-v1","name":"PostTrainBench v1","shortName":"PostTrainBench v1","sourceId":"boggs","url":"https://posttrainbench.com/?version=v1","description":"The agent acts as an AI engineer: given a base model and a compute budget, it runs a whole fine-tuning pipeline, scored on how much the tuned model improves. The archived v1 board's weighted overall score. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science","coding","agentic"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-ppbench","name":"PPBench","shortName":"PPBench","sourceId":"boggs","url":"https://ppbench.com/","description":"300 pencil-and-paper logic puzzles of 20 types, the Sudoku family. A rule checker verifies every solution; the value is the model's best success rate. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","categories":["reasoning","games"],"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":"The agent plans, builds and runs a small factory in a text simulation: stations, workers, materials and deadlines, across 20 scenarios. Scored on the money left at the end against the starting budget. From Haladir. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"agentic","categories":["agentic","games"],"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":"Real paid freelance jobs, mostly from Upwork: game development, product design, architecture, data analysis, animation. The share of projects where people judge the AI's deliverable at least as good as the freelancer's. From the Center for AI Safety and Scale AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"professional","categories":["professional","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":"Research-level math problems written by professional mathematicians, each with one closed-form answer; the model may use code and search. Share answered correctly. Fully private; from Surge AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"math","categories":["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":"Engineering design briefs solved with real CAD and simulation tools: design a crankshaft, route a memory interface on a circuit board, simulate heat around a turbine vane. A run passes only if the design meets every specification, checked by physics solvers; first-try pass rate. From Xplore. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science","coding","agentic"],"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":"Does the agent ask when it should? Coding and database tasks with key information removed or contradictory, and a tool for asking a person. The share of tasks solved in at least one of three runs. From Scale AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","categories":["coding","agentic","behavior"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-scbench","name":"SlopCodeBench","shortName":"SlopCodeBench","sourceId":"boggs","url":"https://www.scbench.ai/leaderboard","description":"The agent implements a written spec, then keeps extending its own code as the spec changes, checkpoint after checkpoint. The share of checkpoints whose own tests pass. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","categories":["coding","agentic"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-sciuniverse","name":"SciUniverse","shortName":"SciUniverse","sourceId":"boggs","url":"https://c5r.net/sciuniverse","description":"Real chemistry, biology and materials lab work at a physical facility: the model controls instruments, instructs human operators and reads real instrument data. Share of tasks passed on the first try, across 17 equally weighted task families. From C5R. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science","agentic"],"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":"100 real features, fixes and migrations from production open-source repositories. A task counts only if the change passes the tests and also clears code-quality bars: no bloat, and it follows the project's own practices. First-try rate. From Snorkel AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"coding","categories":["coding","agentic"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-sudo-l7","name":"sudo L7","sourceId":"surge-sudo-l7","url":"https://surgehq.ai/benchmarks/sudo-l7","description":"60 staff-level software engineering tasks, graded by expert rubrics on correctness, architecture, production risk, verification and communication. From Surge AI.","unit":"percent","higherIsBetter":true,"category":"coding","normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":true,"defaultWeight":0,"categories":["coding","agentic"]},{"id":"bg-tasteval","name":"TasteVal","shortName":"TasteVal","sourceId":"boggs","url":"https://pzeroresearch.com/work/tasteval/","description":"Research judgment: on 8 new AI-research problems the model decides which experiments to run and what to conclude, while a fixed agent writes the code. Scored by how much less compute it needs than human experts to reach the same result, scaled to the current best model. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"science","categories":["science"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-triviaqa","name":"TriviaQA","shortName":"TriviaQA","sourceId":"boggs","url":"https://epoch.ai/benchmarks/trivia-qa","description":"Trivia questions written by quiz enthusiasts. An older benchmark; share with exactly the right answer. Via Epoch AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"knowledge","categories":["knowledge"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-winogrande","name":"WinoGrande","shortName":"WinoGrande","sourceId":"boggs","url":"https://epoch.ai/benchmarks/wino-grande","description":"Fill-in-the-blank sentences where common sense decides which of two words fits. An older benchmark; random guessing scores 50%. Via Epoch AI. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"reasoning","categories":["reasoning"],"versionPolicy":{"kind":"fresh"},"defaultWeight":0},{"id":"bg-zerobench","name":"ZeroBench","shortName":"ZeroBench","sourceId":"boggs","url":"https://zerobench.github.io/","description":"100 visual problems built to be impossible for the multimodal models of their day. Share solved in at least one of five tries. Collected by Jake Boggs' benchmark dashboard.","unit":"percent","higherIsBetter":true,"category":"vision","categories":["vision","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":"225 hard coding exercises from Exercism in six programming languages; the model must edit the code correctly without help. Share completed. From Aider.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["coding"]},{"id":"bh-apprenticebench-api","name":"ApprenticeBench API (NeoCognition)","shortName":"ApprenticeBench API","sourceId":"benchmarkheaven","url":"https://apprenticebench.com/","description":"100 accounts-payable tasks in a row in the Odoo business system, with less mentoring as it goes, done through the system's API. Cumulative success rate. From NeoCognition.","unit":"percent","higherIsBetter":true,"category":"professional","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["professional","agentic"]},{"id":"bh-apprenticebench-cua","name":"ApprenticeBench CUA (NeoCognition)","shortName":"ApprenticeBench CUA","sourceId":"benchmarkheaven","url":"https://apprenticebench.com/","description":"100 accounts-payable tasks in a row in the Odoo business system, with less mentoring as it goes, done by operating its screens like a person. Cumulative success rate. From NeoCognition.","unit":"percent","higherIsBetter":true,"category":"professional","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["professional","agentic","vision"]},{"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-picked web tasks: page-interaction challenges plus tasks from WebBench, Mind2Web 2, GAIA and BrowseComp. Share completed. From Browser Use.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["agentic"]},{"id":"bh-buzzbench","name":"BuzzBench","shortName":"BuzzBench","sourceId":"benchmarkheaven","url":"https://eqbench.com/buzzbench.html","description":"Explaining jokes: the model explains the humor in introductions from the TV show Never Mind the Buzzcocks and predicts how the audience took them. Scored by an AI judge against human-written answers. From EQ-Bench.","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["creative","behavior"]},{"id":"bh-charxiv-reasoning-val-v1.0","name":"CharXiv Reasoning","shortName":"CharXiv Reasoning","sourceId":"benchmarkheaven","url":"https://charxiv.github.io/#leaderboard","description":"Questions about real charts from scientific papers that need combining values, trends and labels across the figure. Share of 1,000 questions correct. From Princeton.","unit":"percent","higherIsBetter":true,"category":"vision","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["vision","science"]},{"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":"Find and reproduce the right one of eight near-identical earlier replies hidden in a long conversation. Average score across conversation lengths from 8,000 to 128,000 tokens. From Context Arena.","unit":"percent","higherIsBetter":true,"category":"long-context","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["long-context"]},{"id":"bh-cursorbench-4.0","name":"CursorBench 4.0 (Cursor)","shortName":"CursorBench 4.0","sourceId":"benchmarkheaven","url":"https://cursor.com/cursorbench","description":"Ambiguous, multi-file coding tasks taken from real Cursor sessions: editing, refactoring, investigating and following intent. Cursor's own score; it does not publish how tasks are graded.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["coding","agentic"]},{"id":"bh-eq-bench-4","name":"EQ-Bench 4","shortName":"EQ-Bench 4","sourceId":"benchmarkheaven","url":"https://eqbench.com/","description":"Emotional and social intelligence in multi-turn role-play chats with simulated people. A rating from blind head-to-head comparisons by a panel of three AI judges. From EQ-Bench.","unit":"elo","higherIsBetter":true,"category":"behavior","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["behavior"]},{"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":"Short creative writing from prompts, judged by an AI model, with controls for vocabulary level and overused AI phrases. A rating from head-to-head comparisons. From EQ-Bench.","unit":"elo","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["creative"]},{"id":"bh-eqbench-judgemark-4","name":"Judgemark v4","shortName":"Judgemark v4","sourceId":"benchmarkheaven","url":"https://eqbench.com/judgemark-v4.html","description":"How good is the model as a judge? It scores fixed creative-writing samples and is rated on how well its scores tell good writing from bad. From EQ-Bench.","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["creative","reasoning"]},{"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":"The model plans and writes a novella over eight 1,000-word chapters. An AI judge scores it 0 to 100 on 14 qualities, with a penalty if the writing gets worse as it goes. From EQ-Bench.","unit":"percent","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["creative","long-context"]},{"id":"bh-eqbench-slop-score","name":"Slop Score","shortName":"Slop Score","sourceId":"benchmarkheaven","url":"https://eqbench.com/slop-score.html","description":"How much the model's writing reads like stock AI prose, from its overused words, \"not X but Y\" contrasts and repeated phrases. Lower is better. From EQ-Bench.","unit":"score","higherIsBetter":false,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["creative","behavior"]},{"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":"Make real code faster: the agent gets a codebase and a performance test and must match an expert developer's optimization (102 tasks). The share where one attempt reaches at least 95% of the human speedup and stays correct. From UC Berkeley.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["coding","agentic"]},{"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 material: policies, transcripts, recordings and a client API. Scored by how well the agent it built then serves simulated customers on 53 held-out tasks. From Sierra.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["agentic","coding"]},{"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":"Role-play in Japanese: 10-turn character dialogues scored by four AI judges on eight qualities. Average score.","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["creative","multilingual"]},{"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":"Role-play in Japanese, scored on staying in role, conversation quality, a stable persona and recovering from hard turns. Average of five qualities. A fork of Japanese-RP-Bench.","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["creative","multilingual"]},{"id":"bh-livebench-2026-06-25","name":"LiveBench","shortName":"LiveBench","sourceId":"benchmarkheaven","url":"https://livebench.ai/","description":"A regularly refreshed mix of objectively graded tasks in reasoning, coding, math, data analysis and language, renewed so answers cannot be memorized. The average across categories.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["reasoning","coding","math"]},{"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":"46 long jobs in a terminal with up to 90 minutes each: building systems, migrating frameworks, playing games move by move. Average score with partial credit for real progress.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["agentic","coding"]},{"id":"bh-longbench-2","name":"LongBench v2","shortName":"LongBench v2","sourceId":"benchmarkheaven","url":"https://longbench2.github.io/","description":"503 hard multiple-choice questions over very long material, from 8,000 to 2 million words: documents, code, dialogue and data. Share correct. From Tsinghua (THUDM).","unit":"percent","higherIsBetter":true,"category":"long-context","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["long-context","reasoning"]},{"id":"bh-matharena-aime-2026","name":"AIME 2026 (MathArena)","shortName":"AIME 2026","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"The 30 problems of the 2026 AIME, the US high-school exam that qualifies students for the Math Olympiad; every answer is an integer. Share correct. Run by MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"id":"bh-matharena-apex-2025","name":"Apex 2025 (MathArena)","shortName":"Apex 2025","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"12 competition problems with final answers that the best models of mid-2025 all failed. Share correct. Biased against the four models used to pick them (Grok 4, GPT-5, Gemini 2.5 Pro, GLM 4.5). From MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"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 that at least one of two reference models got wrong. Share correct. From MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"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 questions with a checkable final answer, drawn from arXiv papers of June 2026, too recent to be in training data. Share correct. From MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"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 questions with a checkable final answer, drawn from arXiv papers of August 2026, too recent to be in training data. Share correct. From MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"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 math statements taken from June 2026 arXiv papers. The model scores by saying the statement is false instead of \"proving\" it. From MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math","behavior"]},{"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 math statements taken from August 2026 arXiv papers. The model scores by saying the statement is false instead of \"proving\" it. From MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math","behavior"]},{"id":"bh-matharena-hmmt-2025-11","name":"HMMT November 2025 (MathArena)","shortName":"HMMT November 2025","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"The final-answer problems of the November 2025 Harvard-MIT Mathematics Tournament, one of the largest US high-school math competitions. Share correct. Run by MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"id":"bh-matharena-hmmt-2026-02","name":"HMMT February 2026 (MathArena)","shortName":"HMMT February 2026","sourceId":"benchmarkheaven","url":"https://matharena.ai/","description":"The final-answer problems of the February 2026 Harvard-MIT Mathematics Tournament, one of the largest US high-school math competitions. Share correct. Run by MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"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. Each proof is graded 0 to 7 by AI judges against a rubric built from the official solution; the value is the average score. Run by MathArena.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"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":"Short stories written to the same detailed creative briefs, compared in pairs by AI judges. A relative score, where zero is near the middle of the field. From Lech Mazur.","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["creative"]},{"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":"A creativity test: produce 25 words as different as possible from each other and from 50 seed words, under starting-letter rules. Scored 0 to 10 by AI judges on how far apart the words are. From Lech Mazur.","unit":"score","higherIsBetter":true,"category":"creative","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"normalization":{"kind":"linear","min":0,"max":10},"categories":["creative","reasoning"]},{"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":"A social survival game: eight models talk, form alliances and vote each other out. A skill rating from final placings across many games. From Lech Mazur.","unit":"score","higherIsBetter":true,"category":"games","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["games","behavior"]},{"id":"bh-mcp-atlas","name":"MCP Atlas (Scale AI)","shortName":"MCP Atlas","sourceId":"benchmarkheaven","url":"https://labs.scale.com/leaderboard/mcp_atlas","description":"Multi-step work across real tool servers (MCP): find the right tool in a crowded menu, call it correctly, recover from errors and give an accurate final answer. Share of 1,000 tasks passed. From Scale AI.","unit":"percent","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["agentic"]},{"id":"bh-mcpmark-verified","name":"MCPMark Verified","shortName":"MCPMark Verified","sourceId":"benchmarkheaven","url":"https://mcpmark.ai/leaderboard/verified","description":"Realistic multi-step tasks through tool servers for Notion, GitHub, files, a database and a browser, each checked by a script. 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Share correct. Run by Epoch AI.","unit":"percent","higherIsBetter":true,"category":"science","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["science","knowledge"]},{"id":"epoch-math-level-5","name":"MATH Level 5 (Epoch AI)","shortName":"MATH Level 5","sourceId":"epoch-tasks","url":"https://epoch.ai/benchmarks/math-level-5","description":"The hardest tier of the MATH competition problem set. Share correct. Run by Epoch AI.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"id":"epoch-mirrorcode","name":"MirrorCode (Epoch AI, with METR)","shortName":"MirrorCode","sourceId":"epoch-tasks","url":"https://epoch.ai/benchmarks/mirrorcode","description":"Rebuild a large existing program from its behavior alone, without seeing its code. Share of tests passed. Built by Epoch AI with METR.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["coding","agentic"]},{"id":"epoch-mystery-game-puzzles","name":"Mystery Game Puzzles (Epoch AI)","shortName":"Mystery Game Puzzles","sourceId":"epoch-tasks","url":"https://epoch.ai/benchmarks/mystery-game-puzzles","description":"Deduction puzzles: work out the hidden rules of a mystery game from what can be observed. Share solved. From Epoch AI.","unit":"percent","higherIsBetter":true,"category":"reasoning","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["reasoning","games"]},{"id":"epoch-oeis-open","name":"OEIS Open (Epoch AI)","shortName":"OEIS Open","sourceId":"epoch-tasks","url":"https://epoch.ai/benchmarks/oeis-open","description":"Open integer sequences from the OEIS: find the rule behind a sequence and extend it. Share solved. From Epoch AI.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"id":"epoch-oeis-open-lite","name":"OEIS Open Lite (Epoch AI)","shortName":"OEIS Open Lite","sourceId":"epoch-tasks","url":"https://epoch.ai/benchmarks/oeis-open","description":"The smaller Lite split of OEIS Open: find the rule behind open integer sequences and extend them. Share solved. From Epoch AI.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"id":"epoch-otis-mock-aime","name":"OTIS Mock AIME 2024-2025","shortName":"OTIS Mock AIME 2024-2025","sourceId":"epoch-tasks","url":"https://epoch.ai/benchmarks/otis-mock-aime","description":"Practice AIME exams written for the OTIS olympiad program (2024 and 2025): competition problems with integer answers. Share correct over repeated samples. Run by Epoch AI.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"id":"epoch-simpleqa-verified","name":"SimpleQA Verified (Epoch AI run)","shortName":"SimpleQA Verified","sourceId":"epoch-tasks","url":"https://epoch.ai/benchmarks/simpleqa-verified","description":"1,000 short factual questions with human-verified answers, testing whether a model knows a fact or makes one up. Share correct. Google DeepMind's cleaned-up version of OpenAI's SimpleQA, run by Epoch AI.","unit":"percent","higherIsBetter":true,"category":"knowledge","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["knowledge"]},{"id":"epoch-spireclimb","name":"SpireClimb (Epoch AI)","shortName":"SpireClimb","sourceId":"epoch-tasks","url":"https://epoch.ai/benchmarks/spireclimb","description":"An agent plays the deck-building game Slay the Spire, choosing cards and routes. Progress up the spire. From Epoch AI.","unit":"percent","higherIsBetter":true,"category":"games","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["games","agentic"]},{"id":"epoch-swe-bench-verified","name":"SWE-bench Verified (Epoch AI)","shortName":"SWE-bench Verified","sourceId":"epoch-tasks","url":"https://epoch.ai/benchmarks/swe-bench-verified","description":"Fix real GitHub issues in open-source repositories, on the human-checked SWE-bench tasks. Share resolved, using Epoch AI's own agent scaffold.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["coding","agentic"]},{"id":"exploitbench-v0.1","name":"ExploitBench v0.1","sourceId":"exploitbench","url":"https://exploitbench.ai/","description":"An agent exploits real memory-corruption bugs in the V8 JavaScript engine. The mean share reached of a 16-step capability ladder, from triggering the bug up to running code.","unit":"percent","higherIsBetter":true,"category":"safety","eligibleForDefault":true,"defaultWeight":5,"categories":["safety","coding","agentic"]},{"id":"frontiercode-1.1-extended","name":"FrontierCode 1.1 Extended","shortName":"FrontierCode 1.1 Ext","sourceId":"frontiercode","url":"https://cognition.com/frontiercode","description":"Would a maintainer merge it? Coding agents make changes on private tasks written by the maintainers of real repositories, graded by tests, rubrics and verifiers. From Cognition. The 1.1 Extended set.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"eligibleForDefault":false,"defaultWeight":0,"variantOf":"frontiercode-1.1-main","variantLabel":"Extended","categories":["coding","agentic"]},{"id":"frontiercode-1.1-main","name":"FrontierCode 1.1 Main","shortName":"FrontierCode 1.1","sourceId":"frontiercode","url":"https://cognition.com/frontiercode","description":"Would a maintainer merge it? Coding agents make changes on private tasks written by the maintainers of real repositories, graded by tests, rubrics and verifiers. From Cognition. The 1.1 Main set.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"eligibleForDefault":false,"defaultWeight":0,"categories":["coding","agentic"]},{"id":"frontiercode-2.0-main","name":"FrontierCode 2.0","shortName":"FrontierCode","sourceId":"frontiercode","url":"https://cognition.com/frontiercode","description":"Would a maintainer merge it? 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From Epoch AI; separate from the FrontierMath tiers.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"id":"frontiermath-tier-4-v2","name":"FrontierMath Tier 4 (v2)","shortName":"FM Tier 4","sourceId":"frontiermath","url":"https://epoch.ai/benchmarks/frontiermath-tier-4-v2","description":"Research-level math problems written by professional mathematicians, the hardest tier of FrontierMath; models may use Python. Share correct on the private set. From Epoch AI.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["math"]},{"id":"frontiermath-tiers-1-3-v2","name":"FrontierMath Tiers 1–3 (v2)","shortName":"FM Tiers 1–3","sourceId":"frontiermath","url":"https://epoch.ai/benchmarks/frontiermath-tiers-1-3-v2","description":"Original, expert-written math problems below FrontierMath's research tier; models may use Python. Share correct on the private set. From Epoch AI.","unit":"percent","higherIsBetter":true,"category":"math","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"variantOf":"frontiermath-tier-4-v2","variantLabel":"Tiers 1 to 3","categories":["math"]},{"id":"frontierphysics","name":"FrontierPhysics","sourceId":"frontierphysics","url":"https://www.benchflow.ai/frontierphysics","lastUpdated":"2026-11-04","description":"End-to-end physics research by an agent: 56 tasks in theory, computation and experiment across nine fields, written by 42 physicists. A pass needs every automated test and every must-have rubric item; the pass rate is averaged over 3 trials.","normalization":{"kind":"linear","min":0,"max":100},"unit":"percent","higherIsBetter":true,"category":"science","eligibleForDefault":false,"defaultWeight":0,"categories":["science","agentic"]},{"id":"frontierswe-v1","name":"FrontierSWE v1","shortName":"FrontierSWE v1","sourceId":"frontierswe","url":"https://www.frontierswe.com/v1","description":"17 very long software engineering tasks, each model in its own command-line agent. The value is how often a model beats a random opponent on a random task, averaged over 5 runs. The original board, kept as published.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh"},"eligibleForDefault":false,"defaultWeight":0,"categories":["coding","agentic"]},{"id":"frontierswe-v2","name":"FrontierSWE v2","shortName":"FrontierSWE","sourceId":"frontierswe","url":"https://www.frontierswe.com/","description":"34 very long software engineering tasks graded 0 to 100 on implementation, performance and research, with up to 20 hours per task. Average of 5 runs at maximum reasoning effort. From Cognition.","unit":"percent","higherIsBetter":true,"category":"coding","versionPolicy":{"kind":"fresh","supersedes":"frontierswe-v1"},"eligibleForDefault":true,"defaultWeight":25,"advanced":[{"id":"implementation","name":"Implementation"},{"id":"performance","name":"Performance"},{"id":"research","name":"Research"}],"categories":["coding","agentic"]},{"id":"gamefeel","name":"GameFeel Benchmark","shortName":"GameFeel","sourceId":"gamefeel","url":"https://kodasdream.github.io/gamefeel-benchmark/","description":"Each model adds items, mechanics and balance changes of its own choosing to 12 unpublished game projects. The overall score, out of 100, averages seven areas: creating, bug fixing, game feel, ideas, visuals, audio and reverse evaluation. Game feel itself is rated by people.","unit":"score","higherIsBetter":true,"category":"creative","normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":false,"defaultWeight":0,"lastUpdated":"2026-11-13","categories":["creative","coding","games"]},{"id":"gdpx","name":"GDPx","shortName":"GDPx","sourceId":"gdpx","url":"https://gdpx.semianalysis.com","lastUpdated":"2026-11-09","description":"206 long frontier-work tasks across 11 fields, among them chip design, cybersecurity, biology, finance, law, quant trading and materials science, with up to 8 hours per task. Average of 3 attempts, every field weighted equally. From SemiAnalysis; most tasks are private, and it says safety classifiers lower some models' scores.","normalization":{"kind":"linear","min":0,"max":100},"unit":"percent","higherIsBetter":true,"category":"professional","eligibleForDefault":true,"defaultWeight":0,"categories":["professional","science","agentic"]},{"id":"hearingbench","name":"Hearing Bench","sourceId":"hearingbench","url":"https://hearing-bench.adamholter.chatgpt.site/","lastUpdated":"2026-11-12","description":"Models see only a spectrogram image of a sound, never the audio, and must name it: everyday sounds, instrument notes, voices, spoken words and sentences. Share of 100 clips named correctly, graded strictly.","normalization":{"kind":"linear","min":0,"max":100},"unit":"percent","higherIsBetter":true,"category":"vision","eligibleForDefault":false,"defaultWeight":0,"categories":["vision"]},{"id":"hle-text-only","name":"Humanity's Last Exam (text only)","shortName":"HLE text-only","sourceId":"scale-hle-text","url":"https://scale.com/leaderboard/humanitys_last_exam_text_only","unit":"percent","higherIsBetter":true,"category":"knowledge","normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":true,"defaultWeight":0,"description":"Humanity's Last Exam without its image questions: expert-written questions at the frontier of many fields. Share correct, with a 95% interval. Scale's run; models tested after the exam went public carry a contamination warning.","categories":["knowledge","reasoning","science"]},{"id":"honeybench-v0.2","name":"HoneyBench v0.2","shortName":"HoneyBench","sourceId":"honeybench","url":"https://goodhartlabs.com/honeybench","description":"Does the model cheat when it can? Tasks with clear rules and a flawed scorer, such as dosing simulated patients, grid control and ML repair. The share of scored runs whose final submission exploits the flaw. Lower is better.","unit":"percent","higherIsBetter":false,"category":"safety","normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":false,"defaultWeight":0,"categories":["safety"]},{"id":"humanitys-sixth-sense","name":"Humanity's Sixth Sense","shortName":"HSS","sourceId":"scale-hss","url":"https://labs.scale.com/leaderboard/hss","unit":"percent","higherIsBetter":true,"category":"vision","eligibleForDefault":true,"defaultWeight":0,"description":"522 image and silent-video tasks on intuitive judgment: timing, space, social cues and abstract patterns. Share passed on the first try, averaged over three attempts; every rubric item must pass. From Scale AI and Elorian.","categories":["vision","reasoning"]},{"id":"julius-deepfinance","name":"DeepFinance","sourceId":"julius-deepfinance","url":"https://www.juliusresearch.ai/deep-finance","lastUpdated":"2026-11-05","description":"21 private-equity and investment-banking assignments that take a professional about 27 hours each. The mean share of 84 rubric criteria met per task, over 10 attempts. From Julius Research.","normalization":{"kind":"linear","min":0,"max":100},"unit":"percent","higherIsBetter":true,"category":"professional","eligibleForDefault":false,"defaultWeight":0,"categories":["professional","agentic"]},{"id":"kernelbench-com-cuda","name":"KernelBench CUDA (kernelbench.com)","shortName":"kernelbench.com CUDA","sourceId":"kernelbench-com","url":"https://kernelbench.com/#cuda","lastUpdated":"2026-11-11","description":"Coding agents write four hard GPU kernels in CUDA and are scored on how fast they run. Each problem counts as a share of the best model's result, averaged over all four; failed, flagged or unaudited runs count 0, and every run is audited for cheating. Not Stanford's KernelBench.","unit":"percent","higherIsBetter":true,"category":"coding","eligibleForDefault":false,"defaultWeight":0,"categories":["coding","agentic"]},{"id":"kernelbench-com-hard","name":"KernelBench Hard (kernelbench.com)","shortName":"kernelbench.com Hard","sourceId":"kernelbench-com","url":"https://kernelbench.com/#hard","lastUpdated":"2026-11-11","description":"Coding agents write six GPU kernels in CUDA or Triton and are scored on how fast they run. Each problem counts as a share of the best model's result, averaged over all six; failed, flagged or unaudited runs count 0, and every run is audited for cheating. Not Stanford's KernelBench.","unit":"percent","higherIsBetter":true,"category":"coding","eligibleForDefault":false,"defaultWeight":0,"categories":["coding","agentic"]},{"id":"llm-chess","name":"LLM Chess","sourceId":"llm-chess","url":"https://maxim-saplin.github.io/llm_chess/","lastUpdated":"2026-11-10","description":"Models play chess through a text tool interface against a random-move player and the Komodo Dragon engine at set skill levels. An Elo estimated from those games.","unit":"elo","higherIsBetter":true,"category":"games","eligibleForDefault":false,"defaultWeight":0,"categories":["games"]},{"id":"llm2014-logic","name":"LLM2014 Logic","shortName":"LLM2014 logic","sourceId":"llm2014","url":"https://llm2014.github.io/llm_benchmark/","lastUpdated":"2026-11","description":"About 28 private logic and reasoning questions that rotate monthly: rule induction, log analysis, puzzles. The working must be correct, so lucky guesses earn nothing. Median score out of 100 over three runs.","normalization":{"kind":"linear","min":0,"max":100},"unit":"score","higherIsBetter":true,"category":"reasoning","eligibleForDefault":false,"defaultWeight":0,"categories":["reasoning"]},{"id":"matharena-apex","name":"MathArena Apex","shortName":"Apex","sourceId":"matharena-apex","url":"https://matharena.ai/?comp=apex--apex_2025","description":"12 competition problems with final answers that the best models of mid-2025 all failed. Share correct over repeated attempts. Biased against the models used to pick the problems (Grok 4, GPT-5, Gemini 2.5 Pro, GLM 4.5).","unit":"percent","higherIsBetter":true,"category":"math","normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":true,"defaultWeight":0,"categories":["math"]},{"id":"matharena-apex-shortlist","name":"MathArena Apex Shortlist","shortName":"Apex Shortlist","sourceId":"matharena-apex","url":"https://matharena.ai/?comp=apex--shortlist_2025","description":"The hardest final-answer problems from 2025 math competitions, the ones models still struggle with. Share correct over repeated attempts. From MathArena.","unit":"percent","higherIsBetter":true,"category":"math","normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":true,"defaultWeight":0,"variantOf":"matharena-apex","variantLabel":"Shortlist","categories":["math"]},{"id":"metr-time-horizon","name":"METR Time Horizon (50%)","shortName":"METR 50% horizon","sourceId":"metr","url":"https://metr.org/time-horizons/","description":"How long a task can the model finish on its own? The length of software and reasoning task, in minutes of skilled human work, that it completes half the time. Longer is better. From METR.","unit":"score","higherIsBetter":true,"category":"agentic","versionPolicy":{"kind":"fresh"},"defaultWeight":0,"categories":["agentic","coding"]},{"id":"minebench","name":"MineBench","sourceId":"minebench","url":"https://minebench.ai/leaderboard","description":"Models turn a text prompt into a Minecraft-style build, written out as raw block coordinates, and people vote between pairs of rendered builds. A conservative rating from those votes.","unit":"elo","higherIsBetter":true,"category":"creative","eligibleForDefault":false,"defaultWeight":0,"categories":["creative"]},{"id":"openrouter-latency","name":"Provider latency","shortName":"Provider latency","sourceId":"openrouter","url":"https://openrouter.ai","description":"How long a request waits before the answer starts, in seconds: the median across the providers serving the model on OpenRouter over the past 3 days. Lower is better.","unit":"seconds","higherIsBetter":false,"category":"speed","eligibleForDefault":false,"defaultWeight":0,"categories":["speed"]},{"id":"openrouter-providers","name":"Provider count","shortName":"Provider count","sourceId":"openrouter","url":"https://openrouter.ai","description":"How many providers serve the model on OpenRouter. More providers means more fallbacks when one is down.","unit":"count","higherIsBetter":true,"category":"reliability","eligibleForDefault":false,"defaultWeight":0,"proxy":true,"categories":["reliability"]},{"id":"openrouter-tool-errors","name":"Tool-call error rate","shortName":"Tool-call error rate","sourceId":"openrouter","url":"https://openrouter.ai","description":"How often a tool call comes back malformed, averaged across OpenRouter providers by traffic over the past 3 days. Lower is better.","unit":"percent","higherIsBetter":false,"category":"reliability","eligibleForDefault":false,"defaultWeight":0,"categories":["reliability"]},{"id":"openrouter-tps","name":"Provider throughput","shortName":"Provider throughput","sourceId":"openrouter","url":"https://openrouter.ai","description":"How fast the answer streams, in tokens per second: the median across the providers serving the model on OpenRouter over the past 3 days.","unit":"count","higherIsBetter":true,"category":"speed","eligibleForDefault":false,"defaultWeight":0,"categories":["speed"]},{"id":"openrouter-uptime","name":"Uptime (7d)","shortName":"Uptime (7d)","sourceId":"openrouter","url":"https://openrouter.ai","description":"The share of the past week that OpenRouter's providers could serve the model, averaged across providers.","unit":"percent","higherIsBetter":true,"category":"reliability","eligibleForDefault":false,"defaultWeight":0,"categories":["reliability"]},{"id":"osworld-2","name":"OSWorld 2.0","shortName":"OSWorld 2.0","sourceId":"osworld","url":"https://osworld-v2.xlang.ai/","unit":"percent","higherIsBetter":true,"category":"agentic","eligibleForDefault":true,"defaultWeight":0,"description":"Computer use: an agent completes long, realistic tasks across desktop apps in a virtual machine, working from the screen. Share of tasks fully completed, on the newest task set.","categories":["agentic","vision"]},{"id":"osworld-verified","name":"OSWorld-Verified","shortName":"OSWorld-Verified","sourceId":"osworld","url":"https://os-world.github.io/","unit":"percent","higherIsBetter":true,"category":"agentic","eligibleForDefault":true,"defaultWeight":0,"description":"Computer use: 361 checked tasks on an Ubuntu desktop in apps such as Chrome, LibreOffice, GIMP and VS Code. Share completed, best across step limits.","categories":["agentic","vision"]},{"id":"owe-bench","name":"OpenWorldEngineer (OWE-Bench)","shortName":"OWE-Bench","sourceId":"mercor-apex","url":"https://openworldengineer.mercor.com","description":"26 long engineering projects built by domain experts, from GPU kernels to aircraft design, across science, software, ML and engineering. An agent must push each project past the best public methods; an attempt scores 0% at the untouched baseline and 100% at the task's ceiling, on held-out cases. Mean of 8 attempts per task, with a 95% interval. From Mercor.","unit":"percent","higherIsBetter":true,"category":"agentic","categories":["agentic","coding","science"],"normalization":{"kind":"linear","min":0,"max":100},"eligibleForDefault":false,"defaultWeight":0},{"id":"posttrainbench-v1.1","name":"PostTrainBench v1.1","sourceId":"posttrainbench","url":"https://posttrainbench.com/?version=v1.1","description":"The agent acts as an AI engineer: given a base model and a compute budget, it runs a whole fine-tuning pipeline. Scored on how much the tuned model improves on target benchmarks, averaged over seeds.","unit":"percent","higherIsBetter":true,"category":"science","eligibleForDefault":true,"defaultWeight":5,"categories":["science","coding","agentic"]},{"id":"ramp-accounting-bench","name":"Ramp Accounting Bench","sourceId":"ramp-accounting-bench","url":"https://labs.ramp.com/ramp-accounting-bench","lastUpdated":"2026-11-09T06:36:11.117114Z","description":"137 long accounting tasks across realistic financial systems, all models in one shared agent harness at high reasoning effort. The mean share of each task's criteria met over three attempts, graded by an AI judge. From Ramp.","normalization":{"kind":"linear","min":0,"max":100},"unit":"percent","higherIsBetter":true,"category":"professional","eligibleForDefault":false,"defaultWeight":0,"categories":["professional","agentic"]},{"id":"roboflow-visual-reasoning","name":"Roboflow Visual Reasoning","shortName":"Visual Reasoning","sourceId":"roboflow","url":"https://playground.roboflow.com/evals/visual-reasoning","unit":"percent","higherIsBetter":true,"category":"vision","eligibleForDefault":true,"defaultWeight":0,"description":"Questions about images that take counting, comparing, spatial sense and several steps of inference; the answer is never written in the image. Share correct, judged by an AI model.","categories":["vision","reasoning"]},{"id":"simplebench","name":"SimpleBench","sourceId":"simplebench","url":"https://simple-bench.com/","description":"Over 200 multiple-choice trick questions on everyday spatial, time and social sense, where ordinary people beat frontier models. Share correct, averaged over 5 runs.","unit":"percent","higherIsBetter":true,"category":"reasoning","eligibleForDefault":true,"defaultWeight":5,"categories":["reasoning"]},{"id":"simpleqa-verified","name":"SimpleQA Verified","shortName":"SimpleQA","sourceId":"simpleqa","url":"https://www.kaggle.com/benchmarks/deepmind/simpleqa-verified","description":"1,000 short factual questions with human-verified answers, testing whether a model knows a fact or makes one up. Share correct. Google DeepMind's cleaned-up version of OpenAI's SimpleQA.","unit":"percent","higherIsBetter":true,"category":"knowledge","eligibleForDefault":true,"defaultWeight":5,"categories":["knowledge"]},{"id":"subscription-token-price","name":"Subscription token price (USD per 1M tokens)","shortName":"Subscription price","sourceId":"realapipricing","url":"https://github.com/FeiZhuLulu/real-api-pricing","lastUpdated":"2026-11-10","description":"Monthly fee ÷ the tokens a subscriber can use in a month on the lab's own plan (median plan), from Real API Pricing's measured allowances and plans sold as a dollar budget at API rates (Mistral Vibe). A lower bound: it assumes the whole allowance is used. Model-level, no reasoning effort; it powers the subscription cost view.","unit":"usd","higherIsBetter":false,"category":"cost","proxy":true,"eligibleForDefault":false,"defaultWeight":0,"categories":["cost"]},{"id":"swe-atlas-qna","name":"SWE Atlas-QnA","shortName":"SWE Atlas QnA","sourceId":"scale","url":"https://labs.scale.com/leaderboard/sweatlas-qna","unit":"percent","higherIsBetter":true,"category":"coding","eligibleForDefault":true,"defaultWeight":0,"description":"An agent explores a real code repository with shell tools and answers deep questions about how it works. The share of questions where every rubric item is met. Best harness per model and effort.","categories":["coding","agentic"]},{"id":"t3-cost-per-turn","name":"T3 Code cost per turn","shortName":"T3 $/turn","unit":"usd","category":"cost","description":"What a turn of real coding work costs at API prices: the average T3 Code turn's fresh, cached, cache-write and output tokens over the last 30 days, at the model's current list price. Not what anyone was billed. Lower is cheaper.","sourceId":"slopalytics","url":"https://slopalytics.com","lastUpdated":"2026-11-12","higherIsBetter":false,"eligibleForDefault":false,"defaultWeight":0,"categories":["cost"]},{"id":"t3-minutes-per-turn","name":"T3 Code minutes per turn","shortName":"T3 min/turn","unit":"score","category":"speed","description":"How long a turn takes in real coding work: average time from sending a turn in T3 Code to its completion, tool and wait time included, over the last 30 days. Lower is faster. From T3 Code's telemetry via Slopalytics.","sourceId":"slopalytics","url":"https://slopalytics.com","lastUpdated":"2026-11-12","higherIsBetter":false,"eligibleForDefault":false,"defaultWeight":0,"categories":["speed"]},{"id":"t3-output-tokens-per-turn","name":"T3 Code output tokens per turn","shortName":"T3 tokens/turn","unit":"count","category":"cost","description":"How much a model writes per turn in real coding work: output tokens, thinking included, per T3 Code turn over the last 30 days. Lower is terser. From T3 Code's telemetry via Slopalytics.","sourceId":"slopalytics","url":"https://slopalytics.com","lastUpdated":"2026-11-12","higherIsBetter":false,"eligibleForDefault":false,"defaultWeight":0,"categories":["cost"]},{"id":"t3-usage-share","name":"T3 Code usage share","shortName":"T3 Code usage","sourceId":"slopalytics","url":"https://slopalytics.com","lastUpdated":"2026-11-12","description":"Share of all turns sent in T3 Code (2026-10-13 to 2026-11-11), from T3's own telemetry via Slopalytics. Revealed preference among T3 Code users, not a quality measure: price, plans and harness defaults drive it too. 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(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 (thinking=True)","TheDrummer/Skyfall-36B-v2","allura-org/Q3-30B-A3B-Designant","schonsense/Diagesis","WokeAI/Tankie-DPE-12b-SFT","bgg1996/Melinoe-30B-A3B-Thinking","ReadyArt/Dark-Oddity-12B-v2.0","ReadyArt/Dark-Oddity-12B-v2.1-GGUF","zerofata/MS3.2-PaintedFantasy-v3-24B","OddTheGreat/Rotor_24B_V.1","OddTheGreat/Circuitry_24B_V.3","Naphula/Goetia-24B-v1.1 (chatml)","Naphula/Goetia-24B-v1","Naphula/Dark-World-24B-v1","Naphula/Evilmind-24B-v1","Delta-Vector/Francois-PE-V2-Huali-12B","vanta-research/scout-4b","ReadyArt/Dark-Osmosis-24B-v1.0","LGAI-EXAONE/EXAONE-4.0-32B","SicariusSicariiStuff/Sweet_Dreams_12B","OddTheGreat/Mars_27B_V.1","DarwinAnim8or/Trouper-12B","RekaAI/reka-flash-3","DarwinAnim8or/Prima-24B","p-e-w/phi-4-heretic","TheDrummer/Precog-24B-v1","google/gemma-3-270m-it","p-e-w/gemma-3-270m-it-heretic","yamatazen/LoyalMaid-12B","ReadyArt/Forgotten-Safeword-12B-v4.0","Marcjoni/QuasiStarSynth-12B","Marcjoni/KiloNovaSynth-12B","Marcjoni/SuperNovaSynth-12B","Vortex5/Dreamstar-12B","Marcjoni/HyperNovaSynth-12B","icefog72/IceAbsintheLoucheRP-7b","Vortex5/Chaos-Unknown-12b","Vortex5/Abyssal-Seraph-12B","Vortex5/Scarlet-Eclipse-12B","Vortex5/Harmonic-Lumina-12B","TheDrummer/Snowpiercer-15B-v4 (chatml)","TheDrummer/Snowpiercer-15B-v4 (alpaca)","TheDrummer/Snowpiercer-15B-v4 (chatml w/ <think> prefill)","TheDrummer/Snowpiercer-15B-v4 (alpaca w/ <think> prefill)","TheDrummer/Rivermind-24B-v1","FlareRebellion/BereavedCompound-v1.0-24b","ohyeah1/Violet-Lyra-Gutenberg-v2b","Epiculous/Violet_Twilight-v0.2b","Vortex5/Sunlit-Shadow-12B","Vortex5/Moonlit-Shadow-12B","Vortex5/Crimson-Twilight-12B","DreadPoor/Smoothie-12B-Model_Stock","DreadPoor/Strawberry_Smoothie-12B-Model_Stock","allenai/Olmo-3-32B-Think","Vortex5/Scarlet-Seraph-12B","Vortex5/Forsaken-Void-12B","Vortex5/Harmony-Bird-12B","icefog72/IceAbsintheLoucheV2RP-7b","icefog72/IceAbsintheNeatRP-7b","miromind-ai/MiroThinker-v1.0-30B","huihui-ai/Huihui-MiroThinker-v1.0-30B-abliterated","Naphula/Golem-70B-v1a","Naphula/Golem-70B-v1b","Ionze/Lilitu-L3.3-70b-0.1","moonshotai/Kimi-Linear-48B-A3B-Instruct","PrimeIntellect/INTELLECT-3 (/nothink)","PrimeIntellect/INTELLECT-3","mistralai/Ministral-3-14B-Instruct-2512","mistralai/Ministral-3-14B-Instruct-2512 ([THINK] prefill)","mistralai/Ministral-3-14B-Instruct-2512 ([THINK] + Ministral 3 sys prompt)","mistralai/Ministral-3-8B-Instruct-2512","mistralai/Ministral-3-8B-Instruct-2512 ([THINK] prefill)","mistralai/Ministral-3-8B-Instruct-2512 ([THINK] + Ministral 3 sys prompt)","Vortex5/Stellar-Umbra-12B","vprilepskii/Seed-OSS-36B-Instruct-biprojected-norm-preserving-abliterated (- budget 0)","vprilepskii/Seed-OSS-36B-Instruct-biprojected-norm-preserving-abliterated","NousResearch/Hermes-4.3-36B","NousResearch/Hermes-4.3-36B (Hermes-4 reasoning)","vprilepskii/Harbinger-24B-biprojected-norm-preserving-abliterated","nvidia/Nemotron-Orchestrator-8B (thinking=False)","nvidia/Nemotron-Orchestrator-8B (thinking=True)","amazon/nova-2-lite-v1 (reasoning=disabled)","amazon/nova-2-lite-v1 (reasoning=enabled)","EssentialAI/rnj-1-instruct","grimjim/Nemo-Instruct-2407-MPOA-v3-12B","grimjim/Nemo-Instruct-2407-baked-v1-12B","grimjim/Nemo-Instruct-2407-MPOA-v2-12B","ReadyArt/Dark-Nexus-24B-v2.0","TheDrummer/Behemoth-X-123B-v2.1","TheDrummer/Precog-123B-v1","mistralai/Ministral-3-14B-Reasoning-2512","mistralai/Ministral-3-14B-Reasoning-2512 ([THINK] prefill)","mistralai/Ministral-3-14B-Reasoning-2512 ([THINK] + Ministral 3 sys prompt)","mistralai/Ministral-3-8B-Reasoning-2512","mistralai/Ministral-3-8B-Reasoning-2512 ([THINK] + Ministral 3 sys prompt)","mistralai/Ministral-3-8B-Reasoning-2512 ([THINK] prefill)","temaq-org/Tema_Q-R4.2","Vortex5/Prototype-X-12b","temaq-org/Tema_Q-R3.1","huihui-ai/Huihui-Orchestrator-8B-abliterated (thinking=False)","huihui-ai/Huihui-Orchestrator-8B-abliterated (thinking=True)","cgato/Nemo-12b-Humanize-SFT-v0.2.5-KTO","Vortex5/Red-Synthesis-12B","Vortex5/Shining-Prism-12B","Vortex5/Poetic-Rune-12B","TheDrummer/Cydonia-24B-v4.3","TheDrummer/Cydonia-24B-v4.3 ([THINK] prefill)","nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16","nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (<think> prefill)","redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-v3","ReadyArt/Dark-Desires-12B-v1.0-GGUF","Vortex5/NoctyxCosma-12B","Vortex5/Moonlit-Umbra-12B","TheDrummer/Magidonia-24B-v4.3","TheDrummer/Magidonia-24B-v4.3 (<thinking> prefill)","redrix/GodSlayer-12B-ABYSS","Delta-Vector/Rei-V3-KTO-12B","Tongyi-Zhiwen/QwenLong-L1.5-30B-A3B","Casual-Autopsy/Maginum-Cydoms-24B","Vortex5/Starlit-Shadow-12B","Vortex5/Starry-Shadow-12B","Vortex5/Eclipsed-Prism-12B","bgg1996/Melinoe-30B-A3B-Thinking-Diluted","bgg1996/Melinoe-Next-80B-A3B-Thinking-v0","bgg1996/Melinoe-Next-80B-A3B-Thinking-Diluted-v0","SicariusSicariiStuff/Impish_Bloodmoon_12B","SicariusSicariiStuff/Angelic_Eclipse_12B","coder3101/Magidonia-24B-v4.3-heretic-v2","coder3101/Magidonia-24B-v4.3-heretic-v2 (<thinking> prefill)","coder3101/Magidonia-24B-v4.3-heretic","coder3101/Magidonia-24B-v4.3-heretic (<thinking> prefill)","coder3101/Cydonia-24B-v4.3-heretic","coder3101/Cydonia-24B-v4.3-heretic ([THINK] prefill)","coder3101/Cydonia-24B-v4.3-heretic-v2","coder3101/Cydonia-24B-v4.3-heretic-v2 ([THINK] prefill)","LatitudeGames/Hearthfire-24B","Retreatcost/KansenSakura-Conflagration-RP-12b","SicariusSicariiStuff/Phi-Line_14B","SicariusSicariiStuff/Phi-lthy4","Lambent/Mira-v1.21-27B-rlvr","GalrionSoftworks/MN-LooseCannon-12B-v1","LyraNovaHeart/Stellar-Odyssey-12b-v0.0","QuixiAI/Ina-v11.1","YanLabs/Seed-OSS-36B-Instruct-MPOA-v1 (- budget 0)","YanLabs/Seed-OSS-36B-Instruct-MPOA-v1","YanLabs/Seed-OSS-36B-Instruct-MPOA (- budget 0)","YanLabs/Seed-OSS-36B-Instruct-MPOA","temaq-org/Tema_Q-R15.6","temaq-org/Tema_Q-R12.2","Azure99/Blossom-V6.3-36B","LiquidAI/LFM2-8B-A1B","arnomatic/Qwen3Guard-Gen-8B-DeathGuard-heretic (thinking=True)","arnomatic/Qwen3Guard-Gen-8B-DeathGuard-heretic (thinking=False)","MuXodious/LFM2-8B-A1B-absolute-heresy","HyperXR/SapientiaOcculta-12B","Vortex5/Mystic-Matron-12B","Vortex5/Lunar-Twilight-12B","Vortex5/MS3.2-24B-Penumbra-Aether (chatml)","Nanbeige/Nanbeige4-3B-Thinking-2511","Nanbeige/Nanbeige4-3B-Thinking-2510","Goekdeniz-Guelmez/Nanbeige4-3B-Thinking-2511-gabliterated","BeaverAI/Anubis-Mini-8B-v1h-GGUF","nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16","nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16 (no-think)","nvidia/Nemotron-Cascade-14B-Thinking","yasserrmd/Neuro-Orchestrator-8B","nvidia/NVIDIA-Nemotron-Nano-9B-v2","nvidia/NVIDIA-Nemotron-Nano-9B-v2 (no-think)","TheDrummer/Anubis-70B-v1.2","TheDrummer/Skyfall-31B-v4.1","TheDrummer/Valkyrie-49B-v2.1","TheDrummer/Valkyrie-49B-v2.1 (<think> prefill)","CrucibleLab/L3.3-70B-Loki-V2.0","CrucibleLab/L3.3-Dark-Ages-70b-v0.1","hrktos-37/Hermes-4-70B-heretic","hrktos-37/Hermes-4-70B-heretic (Hermes-4 reasoning)","Shifusen/L3.3-70B-PippaMaid-1.0","sophosympatheia/Midnight-Miqu-70B-v1.5 (vicuna)","TheDrummer/Rocinante-X-12B-v1","TheDrummer/Rocinante-X-12B-v1 (<thinking> 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