Medical Long Context Reasoning (MLCR-AA)
MLCR-AA Score
MLCR-AA Accuracy (Judged Responses)
MLCR-AA Completeness (Judged Responses)
MLCR-AA Conciseness
Ergebnis
MLCR-AA: Ergebnis
MLCR-AA: Ergebnis vs. Kosten pro Aufgabe
Medical Long Context Reasoning (MLCR-AA): Genauigkeit vs. Vollständigkeit bewerteter Antworten
Token-Nutzung
MLCR-AA: Ausgabe-Token pro Aufgabe
Kosten
MLCR-AA: Kosten pro Aufgabe
Geschwindigkeit
MLCR-AA: Zeit pro Aufgabe
Ergebnis vs. Veröffentlichungsdatum
MLCR-AA: Ergebnis vs. Veröffentlichungsdatum
Beispielaufgaben
Häufig gestellte Fragen
MLCR-AA ist die Bewertung von MLCR durch Artificial Analysis. Der offene Benchmark von Wisedocs prüft, ob KI-Modelle lange, fragmentierte Krankenakten schlüssig auswerten und mehrere Dokumente wie Fachleute bei Versicherungs- und Gesundheitsfällen zusammenführen können. Die synthetischen medizinischen Akten umfassen ungefähr 25.000 bis 64.000 Tokens. Die Fragen sind in sechs Schwierigkeitsstufen eingeteilt — vom Auffinden einer einzelnen Tatsache bis zur klinischen Synthese auf Expertenniveau und zu zusammengesetztem, mehrteiligem Schlussfolgern.
Jede Modellantwort muss zunächst eine Prägnanzprüfung bestehen: Eine Antwort, die mehr als fünfmal so lang wie die Referenzantwort ist, erhält null Punkte. Anschließend bewertet ein Panel aus drei LLMs die prägnanten Antworten; Genauigkeit und Vollständigkeit werden per Mehrheitsentscheid bestimmt. Die allgemeine Erfolgsquote rechnet eine Antwort nur an, wenn sie prägnant sowie vollständig und genau ist. Auf dieser Seite ist die allgemeine Erfolgsquote für die Experten- und zusammengesetzten mehrteiligen (schwierigen) Fragensätze die Hauptpunktzahl.
Claude Sonnet 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) erzielt mit einer allgemeinen Erfolgsquote von 75 % die höchste MLCR-AA-Punktzahl unter den Modellen mit veröffentlichten Ergebnissen. Modell ansehen
Wer eine Akte mit Hunderten von Terminen prüft, sucht nicht nur nach einer einzelnen Tatsache, sondern rekonstruiert Chronologie, Kausalität, Behandlungsmuster und die Relevanz für den Versicherungsfall über fragmentierte Dokumente hinweg. MLCR-AA misst genau diese Fähigkeit. Sie ist für Organisationen wichtig, die Standard-KI für Krankenakten, Versicherungsfälle und andere Arbeitsabläufe mit langen medizinischen Dokumenten bewerten.
Nein. MLCR-AA wird als eigenständige Bewertung ausgewiesen und fließt nicht in den Artificial Analysis Intelligence Index ein. Artificial Analysis bewertet den Benchmark unabhängig.
Evaluationen entdecken
Artificial Analysis Intelligence Index v4.3.2A composite benchmark aggregating ten challenging evaluations to provide a holistic measure of AI capabilities across mathematics, science, coding, and reasoning.
Artificial Analysis Openness IndexA composite measure providing an industry standard to communicate model openness for users and developers.
AA-Briefcase v1.1: Agentic Knowledge Work BenchmarkA private evaluation developed by Artificial Analysis for frontier agentic capability in long-horizon knowledge work, testing agents on realistic business workflows that require deliverables such as spreadsheets, presentations, and memos.
GDPval-AA v2.1 LeaderboardGDPval-AA v2.1 is Artificial Analysis' evaluation framework for OpenAI's GDPval dataset. It tests AI models on real-world tasks across 44 occupations and 9 major industries. Models are given shell access and web browsing capabilities in an agentic loop via Stirrup to solve tasks, with Elo ratings derived from blind pairwise comparisons.
APEX-Agents-AA Benchmark LeaderboardArtificial Analysis' implementation of the APEX-Agents benchmark, testing AI agents on long-horizon, cross-application tasks in professional-services environments with realistic application tooling.
AA-AnalystAgent Benchmark LeaderboardArtificial Analysis' data analysis benchmark, testing AI agents on their ability to work with spreadsheets and documents to answer quantitative questions a Business Analyst or Data Analyst would face day-to-day.
AutomationBench-AA: Agentic SaaS Workflow BenchmarkA benchmark measuring agentic task completion across simulated SaaS application environments, scoring the share of each task's objectives completed without guardrail violations.
Harvey LAB-AA Benchmark LeaderboardArtificial Analysis' implementation of Harvey's Legal Agent Benchmark (LAB), testing AI agents on real-world legal work from Harvey's dataset of 120 private tasks spanning 24 legal practice areas. The agent reads case documents in a sandbox and produces legal deliverables (e.g., memos, disclosure schedules, deposition summaries), graded criterion-by-criterion by a single LLM rubric judge.
GDP.pdf Benchmark LeaderboardArtificial Analysis' implementation of Surge AI's GDP.pdf benchmark, testing whether language models can reason over long, real-world professional documents and satisfy detailed task-specific criteria.
EnterpriseOps-Gym-AA Benchmark LeaderboardArtificial Analysis' independent implementation of ServiceNow's EnterpriseOps-Gym, an agentic benchmark testing whether LLM agents can complete stateful, multi-step enterprise workflows across eight business domains via live tool use, graded on the final state of the underlying databases.
Terminal-Bench 4.0 Benchmark LeaderboardA harder 66-task benchmark of complex terminal work across software, machine learning, science, operations, security, hardware, and media, with recalibrated compute and time allowances and improved instructions, environments, and verifiers.
Terminal-Bench-Science 0.1 Benchmark LeaderboardA 70-task benchmark of research workflows authored and reviewed by domain experts across the life, physical, earth, mathematical, and engineering sciences, each completed in a terminal and checked by its own set of tests.
Artificial Analysis Long Context Reasoning Benchmark LeaderboardA challenging benchmark measuring language models' ability to extract, reason about, and synthesize information from long-form documents ranging from 10k to 100k tokens (measured using the cl100k_base tokenizer).
AA-Omniscience: Knowledge and Hallucination BenchmarkA benchmark measuring factual recall and hallucination across various economically relevant domains.
SciCode Benchmark LeaderboardA scientist-curated coding benchmark featuring 288 test set subproblems from 80 laboratory problems across 16 scientific disciplines.
Humanity's Last Exam Benchmark LeaderboardA frontier-level benchmark with 2,500 expert-vetted questions across mathematics, sciences, and humanities, designed to be the final closed-ended academic evaluation.
CritPt Benchmark LeaderboardA benchmark designed to test LLMs on research-level physics reasoning tasks, featuring 71 composite research challenges.
The most challenging 198 questions from GPQA, where PhD experts achieve 65% accuracy but skilled non-experts only reach 34% despite web access.
ITBench-AA Benchmark LeaderboardArtificial Analysis' implementation of IBM's ITBench benchmark, testing AI agents on Kubernetes incident root-cause analysis from offline incident snapshots. The agent inspects alerts, events, traces, and topology and identifies the contributing-factor entities (deployments, pods, namespaces, network policies, etc.) responsible for the failure.
MMMU-Pro Benchmark LeaderboardAn enhanced MMMU benchmark that eliminates shortcuts and guessing strategies to more rigorously test multimodal models across 30 academic disciplines.
IFBench Benchmark LeaderboardA benchmark evaluating precise instruction-following generalization on 58 diverse, verifiable out-of-domain constraints that test models' ability to follow specific output requirements.
Medical Long Context Reasoning (MLCR-AA)An open benchmark from Wisedocs measuring how well models reason over long, fragmented medical records, performing the multi-document synthesis claims professionals rely on when reviewing insurance and healthcare cases.
𝜏³-Banking Benchmark LeaderboardA fintech customer-support benchmark from the 𝜏-Knowledge framework that tests whether agents can navigate a large unstructured knowledge base and execute multi-step tool calls to resolve realistic banking workflows.
Terminal-Bench 2.1 Benchmark LeaderboardA verified refresh of Terminal-Bench 2.0 — 89 curated tasks across software engineering, system administration, data processing, model training, and security, with environment and instruction fixes so scores reflect agent capability rather than environment gaps.
Terminal-Bench Hard Benchmark LeaderboardAn agentic benchmark evaluating AI capabilities in terminal environments through software engineering, system administration, and data processing tasks.
𝜏²-Bench Telecom Benchmark LeaderboardA dual-control conversational AI benchmark simulating technical support scenarios where both agent and user must coordinate actions to resolve telecom service issues.
MMLU-Pro Benchmark LeaderboardAn enhanced version of MMLU with 12,000 graduate-level questions across 14 subject areas, featuring ten answer options and deeper reasoning requirements.
LiveCodeBench Benchmark LeaderboardA contamination-free coding benchmark that continuously harvests fresh competitive programming problems from LeetCode, AtCoder, and CodeForces, evaluating code generation, self-repair, and execution.
MATH-500 Benchmark LeaderboardA 500-problem subset from the MATH dataset, featuring competition-level mathematics across six domains including algebra, geometry, and number theory.
AIME 2025 Benchmark LeaderboardAll 30 problems from the 2025 American Invitational Mathematics Examination, testing olympiad-level mathematical reasoning with integer answers from 000-999.
Global-MMLU-Lite Benchmark LeaderboardA lightweight, multilingual version of MMLU, designed to evaluate knowledge and reasoning skills across a diverse range of languages and cultural contexts.