Artificial Analysis Openness Index
Openness Index
Openness Index
Openness Index
Artificial Analysis Openness Index：スコア
指数の構成要素
Artificial Analysis Openness Index：構成要素
透明性と利用可能性
Artificial Analysis Openness Index：モデルの利用可能性と透明性
Openness Indexとリリース日
Artificial Analysis Openness Index：スコアとリリース日
オープン性と知能
Artificial Analysis Openness IndexとIntelligence Index
Openness Indexの構成
採点方法
各構成要素は、最も適合するオープン性の類型に基づき、0～3の定性的尺度で採点します。評価には、公開されている一次情報をすべて使用します。
これらの要素を統合し、Artificial Analysis Openness Indexを次のように算出します。
- 事前学習と事後学習のデータ要素を平均します（データ全体で6点）
- すべての構成要素の点数を合計します（最大18/18点）
- この点数を0～100の尺度に正規化します
第三者の基盤モデルから派生したモデルは、元のモデルのライセンスや情報開示の制限を受ける場合があります。更新版では、新しい版について明示的に開示された情報のみを考慮します（以前の版と同じ構成要素を開発者が申告することもできます）。
Openness Indexランキング
開発元 | モデル | Openness Index | Intelligence Index | モデルの利用可能性 | モデルの透明性 | 事前学習データへのアクセス | 事前学習データのライセンス | 事後学習データへのアクセス | 事後学習データのライセンス | |
|---|---|---|---|---|---|---|---|---|---|---|
| 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 Ultra 550B A55B (Reasoning) | 83.33 | 22.93 | 6.00 | 9.00 | 2.00 | 1.00 | 2.00 | 1.00 |
よくある質問
Artificial Analysis Openness Index は、モデルの重み、ライセンス、学習データ、学習方法、学習コードを含め、AI モデルがどの程度オープンに利用でき、透明に文書化されているかを測る複合スコアです。
Artificial Analysis Openness Index は、モデルの利用可能性（重みへのアクセスとライセンス）と透明性（学習データへのアクセスとライセンス、方法論の開示、学習パイプラインの公開）をそれぞれ 0～3 の定性的な尺度で評価します。データのスコアは事前学習と事後学習の平均を取り、各要素の合計を最大 18 点とした後、0～100 の尺度に正規化します。
公開済みの Openness Index 結果があるモデルの中で、現在は K2 Think V2 が 89 で最高です。 モデルを見る
オープンで透明なモデルは、再現性、独立した評価、特定用途向けのカスタマイズ、ベンダーロックインのない商用利用を支えます。Artificial Analysis Openness Index はモデルのオープン性を単一の比較可能なスコアで示し、モデル選択時に知能、速度、価格と併せて検討できるようにします。
いいえ。オープンウェイトはモデルの重みが利用可能であることを意味しますが、ソースコード、学習データ、学習手順、寛容なライセンスまで利用できるとは限りません。Artificial Analysis Openness Index は各要素を別々に評価し、利用可能なモデルと、広くオープンで透明なモデルを区別します。
評価を探す
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.