Artificial Analysis Openness Index
Openness Index
Openness Index
Openness Index
Artificial Analysis Openness Index: puntuación
Componentes del índice
Artificial Analysis Openness Index: componentes
Transparencia frente a disponibilidad
Artificial Analysis Openness Index: disponibilidad frente a transparencia del modelo
Openness Index frente a fecha de lanzamiento
Artificial Analysis Openness Index: puntuación frente a fecha de lanzamiento
Apertura frente a inteligencia
Artificial Analysis Openness Index frente al Artificial Analysis Intelligence Index
Composición del Openness Index
Metodología de puntuación
Cada componente se puntúa en una escala cualitativa de 0 a 3 según el arquetipo de apertura que mejor encaje. Cada modelo se evalúa con base en toda la información pública oficial disponible.
Sintetizamos estos factores en una métrica unificada, el Artificial Analysis Openness Index, de la siguiente manera:
- Se promedian los elementos de datos de preentrenamiento y postentrenamiento (para un total de 6 puntos posibles en datos)
- Se suman las puntuaciones de todos los componentes (hasta un máximo de 18/18 puntos)
- Esta puntuación se normaliza a una escala de 0 a 100
Cuando un modelo deriva de un modelo base de terceros, puede estar limitado por la licencia o la divulgación del modelo original. Para versiones incrementales o actualizadas, solo consideramos la información divulgada explícitamente sobre la nueva versión (incluida la declaración de qué componentes se mantienen desde una versión anterior).
Clasificación del Openness Index
Creador | Modelo | Openness Index | Intelligence Index | Disponibilidad del modelo | Transparencia del modelo | Acceso a datos de preentrenamiento | Licencia de datos de preentrenamiento | Acceso a datos de postentrenamiento | Licencia de datos de postentrenamiento | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 |  Institute of Foundation Models | K2 Think V2 | 88,89 | 11,50 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 2 | Institute of Foundation Models | K2-V2 (High) | 88,89 | 9,87 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 3 | Institute of Foundation Models | K2-V2 (Medium) | 88,89 | 9,02 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 4 | Institute of Foundation Models | K2-V2 (Low) | 88,89 | 7,31 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 5 |  Allen Institute for AI | Olmo 3.1 32B Think | 88,89 | 7,12 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 6 | Allen Institute for AI | Olmo 3.1 32B Instruct | 88,89 | 6,50 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 7 | Allen Institute for AI | Olmo 3 32B Think | 88,89 | 6,47 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 8 | Allen Institute for AI | OLMo 2 32B | 88,89 | 5,97 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 9 | Allen Institute for AI | Olmo 3 7B Think | 88,89 | 5,62 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 10 | Allen Institute for AI | OLMo 2 7B | 88,89 | 5,58 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 11 | Allen Institute for AI | Molmo 7B-D | 88,89 | 5,56 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 12 | Allen Institute for AI | Olmo 3 7B Instruct | 88,89 | 5,24 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 13 |  Swiss AI Initiative | Apertus 70B Instruct | 88,89 | 5,11 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 14 | Swiss AI Initiative | Apertus 8B Instruct | 88,89 | 4,83 | 6,00 | 10,00 | 3,00 | 1,00 | 3,00 | 1,00 |
| 15 |  NVIDIA | Nemotron 3.5 Super VL | 83,33 | 26,49 | 6,00 | 9,00 | 2,00 | 1,00 | 2,00 | 1,00 |
Preguntas frecuentes
El Artificial Analysis Openness Index es una puntuación compuesta que mide qué tan accesibles y transparentemente documentados están los modelos de IA, incluidos sus pesos, licencias, datos y metodología de entrenamiento y código de entrenamiento.
El Artificial Analysis Openness Index puntúa los modelos en una escala cualitativa de 0 a 3 en disponibilidad —acceso a los pesos y licencia— y transparencia —acceso y licencia de los datos de entrenamiento, divulgación de la metodología y publicación del pipeline de entrenamiento—. Las puntuaciones de datos se promedian entre preentrenamiento y postentrenamiento; después, los componentes se suman hasta un máximo de 18 y se normalizan a una escala de 0 a 100.
K2 Think V2 tiene actualmente la puntuación más alta del Artificial Analysis Openness Index, con 89 entre los modelos con resultados publicados. Ver modelo
Los modelos abiertos y transparentes favorecen la reproducibilidad, la evaluación independiente, la adaptación a casos de uso específicos y el uso comercial sin dependencia de un proveedor. El Artificial Analysis Openness Index ofrece una puntuación única y comparable del grado de apertura, para que los usuarios puedan considerarlo junto con la inteligencia, la velocidad y el precio al elegir un modelo.
No. Pesos abiertos significa que los pesos del modelo están disponibles, pero no garantiza que también estén disponibles el código fuente, los datos y las recetas de entrenamiento o licencias permisivas. El Artificial Analysis Openness Index puntúa estos factores por separado para distinguir los modelos accesibles de aquellos que son ampliamente abiertos y transparentes.
Explorar evaluaciones
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.
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.
AA-3D: Interactive 3D Experience BenchmarkA benchmark developed by Artificial Analysis for how well AI models build interactive 3D experiences. Models work as agents in an offline sandbox via Stirrup to turn a written brief into a three.js scene, with Elo ratings derived from blind pairwise votes.
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 v1.1 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. v1.1, built in collaboration with Harvey, checks every deliverable against the task's source documents for hallucinations, so work that passes every rubric criterion but contains a material hallucination receives no credit. A panel of three judges grades each deliverable criterion-by-criterion.
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.
FrontierCode 1.1 Benchmark LeaderboardArtificial Analysis' implementation of Cognition's FrontierCode 1.1, a private agentic coding benchmark that tests models on completing coding tasks in a way that would actually get merged by maintainers of real-world repositories.
AA-PersonalityBench: AI Personality & Communication StyleMeasures how models communicate rather than whether they are correct: 30 judged personality traits rolled up into 8 indexes, plus 21 measured communication characteristics.
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.
HealthBench Benchmark LeaderboardOpenAI's HealthBench: 5,000 health conversations graded against physician-written rubrics, from emergency referrals to global health.
HealthBench Professional Benchmark LeaderboardOpenAI's HealthBench Professional: 525 clinician conversations covering consult, writing and research tasks, graded against physician-written rubrics.
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.