Artificial Analysis Openness Index
Openness Index
Openness Index
Openness Index
Artificial Analysis Openness Index:得分
指数组成
Artificial Analysis Openness Index:组成部分
透明度与可用性
Artificial Analysis Openness Index:模型可用性与透明度
Openness Index 与发布日期
Artificial Analysis Openness Index:得分与发布日期
开放性与智能水平
Artificial Analysis Openness Index 与 Artificial Analysis Intelligence Index
Openness Index 的构成
评分方法
每个组成部分根据最符合的开放性类别,按0 至 3 分的定性标准评分。评估依据是所有公开的一手信息。
我们将这些因素综合为统一指标:Artificial Analysis Openness Index。计算方式如下:
- 对预训练和后训练的数据项目取平均值(数据部分共 6 分)
- 将所有组成部分的得分相加(最高 18/18 分)
- 将得分归一化到 0 至 100 分
如果模型基于第三方基础模型,其评分可能受到上游模型许可或信息披露范围的限制。对于增量版本或更新版本,我们只考虑明确针对新版本披露的信息(模型开发者可说明哪些部分与早期版本保持一致)。
Openness Index 排行榜
开发者 | 模型 | Openness Index | Intelligence Index | 模型可用性 | 模型透明度 | 预训练数据访问 | 预训练数据许可 | 后训练数据访问 | 后训练数据许可 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Olmo 3 7B Think | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 2 | Olmo 3.1 32B Think | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 3 | Apertus 70B Instruct | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 4 | OLMo 2 32B | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 5 | K2 Think V2 | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 6 | Apertus 8B Instruct | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 7 | Olmo 3 7B Instruct | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 8 | Olmo 3.1 32B Instruct | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 9 | K2-V2 (High) | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 10 | Molmo 7B-D | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 11 | Olmo 3 32B Think | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 12 | K2-V2 (Low) | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 13 | K2-V2 (Medium) | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 14 | OLMo 2 7B | 89 | - | 6.0 | 10.0 | 3.0 | 1.0 | 3.0 | 1.0 | |
| 15 | Nemotron 3.5 Super VL | 83 | 14 | 6.0 | 9.0 | 2.0 | 1.0 | 2.0 | 1.0 |
常见问题
Artificial Analysis Openness Index 是一个综合分数,用于衡量 AI 模型的开放可用程度和文档透明度,包括模型权重、许可、训练数据、训练方法和训练代码。
Artificial Analysis Openness Index 在模型可用性(权重访问和许可)以及模型透明度(训练数据访问和许可、方法披露、训练流水线发布)方面,以 0 到 3 的定性等级为模型评分。数据分数取预训练和后训练阶段的平均值,各组成部分最高合计 18 分,再归一化为 0 到 100 分。
在已发布 Openness Index 结果的模型中,Olmo 3 7B Think 目前以 89 获得最高分。 查看模型
开放透明的模型有助于复现、独立评估、针对具体场景进行定制,并避免供应商锁定地用于商业用途。Artificial Analysis Openness Index 以一个可比较的分数表示模型的开放程度,让用户在选择模型时可以同时权衡开放性、智能、速度和价格。
不等同。开放权重表示模型权重可用,但不保证源代码、训练数据、训练方法或宽松许可也可用。Artificial Analysis Openness Index 分别评估这些因素,以区分仅可访问的模型与广泛开放且透明的模型。
探索评测
A composite benchmark aggregating nine challenging evaluations to provide a holistic measure of AI capabilities across knowledge work, coding, science, and general knowledge.
A 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.
A 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.
Artificial Analysis' implementation of the APEX-Agents benchmark, testing AI agents on long-horizon, cross-application tasks in professional-services environments with realistic application tooling.
Artificial 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.
A benchmark measuring agentic task completion across simulated SaaS application environments, scoring the share of each task's objectives completed without guardrail violations.
Artificial 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.
Artificial 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.
Artificial 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.
A 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.
A 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' implementation of Cognition's FrontierCode 2.0, a private agentic coding benchmark of 132 tasks in real open-source repositories, graded on whether a maintainer would merge the agent's change.
A refined and updated 1,000-question set of Humanity's Last Exam, split evenly between reasoning and knowledge questions, with 272 questions that include an image.
Measures how models communicate rather than whether they are correct: 30 judged personality traits rolled up into 8 indexes, plus 21 measured communication characteristics.
A benchmark measuring factual recall and hallucination across various economically relevant domains.
The most challenging 198 questions from GPQA, where PhD experts achieve 65% accuracy but skilled non-experts only reach 34% despite web access.
Artificial 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.
An enhanced MMMU benchmark that eliminates shortcuts and guessing strategies to more rigorously test multimodal models across 30 academic disciplines.
A benchmark evaluating precise instruction-following generalization on 58 diverse, verifiable out-of-domain constraints that test models' ability to follow specific output requirements.
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.
Artificial Analysis' implementation of HeyGen's Code2Video Bench, evaluating how well models create short product and brand videos from 114 design briefs using HyperFrames HTML.
OpenAI's HealthBench: 5,000 health conversations graded against physician-written rubrics, from emergency referrals to global health.
CritPt v2 is the revised version of CritPt, a benchmark designed to test LLMs on research-level physics reasoning tasks, featuring 70 composite research challenges. Models solve problems with a code-execution tool in an offline Linux sandbox.
VIALS-AA is Artificial Analysis' implementation of Handshake AI's VIALS benchmark. It tests whether language models can interpret scientific images and answer questions about the evidence they contain.
Artificial Analysis' implementation of Google DeepMind's Multimodal Knowledge Agent (MKA) benchmark. It tests whether agents can read visually dense project files, such as architectural drawings, engineering schematics and clinical scans, and produce the deliverables a professional would.
GDPval-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.
A 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.
A 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.
Artificial 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.
A 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).
A scientist-curated coding benchmark featuring 288 test set subproblems from 80 laboratory problems across 16 scientific disciplines.
A frontier-level benchmark with 2,500 expert-vetted questions across mathematics, sciences, and humanities, designed to be the final closed-ended academic evaluation.
A benchmark designed to test LLMs on research-level physics reasoning tasks, featuring 71 composite research challenges.
An agentic benchmark evaluating AI capabilities in terminal environments through software engineering, system administration, and data processing tasks.
A dual-control conversational AI benchmark simulating technical support scenarios where both agent and user must coordinate actions to resolve telecom service issues.
An enhanced version of MMLU with 12,000 graduate-level questions across 14 subject areas, featuring ten answer options and deeper reasoning requirements.
A contamination-free coding benchmark that continuously harvests fresh competitive programming problems from LeetCode, AtCoder, and CodeForces, evaluating code generation, self-repair, and execution.
A 500-problem subset from the MATH dataset, featuring competition-level mathematics across six domains including algebra, geometry, and number theory.
All 30 problems from the 2025 American Invitational Mathematics Examination, testing olympiad-level mathematical reasoning with integer answers from 000-999.
A lightweight, multilingual version of MMLU, designed to evaluate knowledge and reasoning skills across a diverse range of languages and cultural contexts.