Foundation Models & Agent Frameworks · Data Provider / Broker
Artificial Analysis
Independent AI model benchmarking and selection platform.
Available information varies by company and source.
Profile record updated:
Company facts
- Official name
- Artificial Analysis, Inc.
- Entity type
- COMPANY
- Headquarters
- United States
- Company size
- 10–49
- Market cluster / sector
- Foundation Models & Agent Frameworks
- Market role
- Data Provider / Broker
- Official website
- artificialanalysis.ai
What Artificial Analysis does
The company aggregates, standardises and produces benchmark data on AI models, then packages that data into software interfaces, APIs, leaderboards and enterprise decision-support tools. It creates value by reducing the time and uncertainty involved in AI model evaluation and procurement. The commercial engine combines public distribution for top-of-funnel adoption with paid access to richer datasets, advanced analytics and enterprise workflows.
Category differentiation
Artificial Analysis is not a foundation model developer or a general MLOps platform. It is an independent benchmarking and analytics layer used to compare and select AI models.
Strategic context
AI-supported assessment from the existing company research; distinguish interpretation from sourced facts.
Artificial Analysis, Inc. is a private US-based B2B SaaS provider focused on independent AI benchmarking and analysis. The company publishes model benchmarks, leaderboards, evaluation frameworks and recommendation tools that compare AI models on quality, speed, latency and cost. Its products help enterprises, developers, researchers and procurement teams assess competing AI models using standardised and vendor-neutral data. The company generates revenue through a freemium commercial model. Public leaderboards and a free API drive awareness and adoption, while premium subscriptions, commercial API access and enterprise-grade data access monetise deeper usage. Its direct customers are business and technical teams making model selection, procurement and validation decisions rather than end-consumers.
Company news briefing
Briefing updated:
Artificial Analysis continues to serve as a critical independent benchmarking authority, with its intelligence indexes and performance metrics widely cited in evaluations of major model releases. Recent industry evaluations increasingly incorporate its frameworks to assess frontier and open-weight models, including offerings from OpenAI, Anthropic, Google, and DeepSeek. These independent metrics provide objective performance and cost comparisons across the rapidly evolving artificial intelligence landscape.
Business model & monetisation
Artificial Analysis uses a freemium SaaS and data subscription model. Free access to public benchmarks, leaderboards and a free API builds market visibility and user adoption. Revenue comes from premium subscriptions, commercial API access, expanded benchmark datasets, advanced analytics, downloadable reporting and enterprise plans with deeper access and additional seats.
- Premium subscriptions
- Software Subscription
- Commercial API access
- Pay-per-Use
- Enterprise plans with deeper data access and seats
- Software Subscription
- Free API and public leaderboards
Products & capabilities
No products with linked sources are available in this view.
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Competitors & alternatives
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AI model evaluation platform powered by human feedback data.
Expert intelligence platform for AI evaluation and deployment.
AI software that automates product analytics and product insights.
AI-native SaaS for software sourcing and vendor evaluation.
Enterprise platform for AI data labelling, evaluation and expert services.
Side-by-side comparisons
Recent recorded signals
Dates refer to the source publication. Older entries are historical context, not evidence of a new event.
Mistral Large 4 Open Weights, Ranks Ahead of Meta
AI / LLM · Recorded impact score: 4/5
Mistral AI has released the open weights for Mistral Large 4, nicknamed 'Le Chonk', a trillion-parameter mixture-of-experts model with 52 billion active parameters. It processes text and images, supports over 160 languages, and is available on Hugging Face under a modified MIT license, permitting self-hosting and fine-tuning, but commercial resale requires a separate license. In Artificial Analysis' updated open-source ranking, it scored 26 points, placing eighth, ahead of Meta's Muse Spark Open but behind seven Chinese models like DeepSeek V4.1 Flash and GLM-5.3. The model excels in coding and cybersecurity, ranking second on the Coding Agent Index and third on the Cyber Index among open models. API pricing has been halved to €0.63 per million input tokens. Mistral positions it for programming agents and cybersecurity, with a specialized penetration-testing variant for partners like Orange Cyberdefense, highlighting Europe's AI competition with China and the US.
- Mistral AI released open weights for Mistral Large 4, a ~1 trillion parameter MoE model with 52B active parameters.
- The model is available on Hugging Face under a modified MIT license, with commercial resale requiring a separate license.
Artificial Analysis Intelligence Index v5 Penalizes Open-Weight Models
AI Models & Benchmarks · Recorded impact score: 3/5
Artificial Analysis released version 5 of its Intelligence Index, a major update that now relies on private test data for about 60% of scoring to prevent models being trained on known questions. The index, equally weighted across knowledge work, programming, science, and general knowledge, significantly downgraded open-weight models, widening the gap between the best proprietary and open-weight models from 3 to 12 points. Claude Opus 5.5 (Anthropic) retained the top spot with 58 points, with GPT-6 Astra close behind at 57. Open-weight models saw notable declines: Kimi K3.1 Pro (Moonshot AI) dropped from 55 to 46, DeepSeek V4.1 Flash from 45 to 32, and Xiaomi's MiMo-V2.6-Pro lost 16 points to 30. The update introduces new benchmarks (FrontierCode 2.0, Terminal-Bench-Science) and increased evaluation cost (e.g., $10 per task for Claude Opus 5.5). Rankings are highly dependent on test selection and weighting, and private tests reduce external verifiability.
- Artificial Analysis released Intelligence Index v5, with 60% of scoring based on private test sets.
- Gap between top proprietary and best open-weight model widened from 3 to 12 points.
OpenAI Launches GPT-6.1 Terra Model with Computer Use
AI Models · Recorded impact score: 4/5
OpenAI has introduced GPT-6.1 Terra, a cost-efficient AI model that enhances computer use, coding, and factual accuracy. Positioned as the balanced middle tier between high-end GPT-6.1 Sol/Astra and budget GPT-6 Luna, it offers significant performance gains at a lower price. On benchmarks like Agents' Last Exam, Terra improves by 15 percentage points over GPT-5.6 Terra and can match Astra's performance at one-tenth the cost. API pricing is $1 per million input tokens and $5 per million output tokens, 50-58% lower than GPT-5.6 Terra, making it ideal for high-volume automation. Terra solves 50.3% of tasks on Terminal-Bench 4.0, more than double its predecessor, and reduces hallucination rates from 88% to 38% on AA-Omniscience. Available immediately to ChatGPT Plus, Pro, Enterprise, and Edu subscribers in ChatGPT Work, Codex, and via the OpenAI API, it is rated 'Critical' under OpenAI's Preparedness Framework, prompting enhanced safety measures.
- OpenAI launched GPT-6.1 Terra, a cost-efficient model positioned between GPT-6.1 Sol/Astra and GPT-6 Luna, offering computer use, coding, and improved factuality.
- API pricing is $1 per million input tokens and $5 per million output tokens, 50-58% lower than GPT-5.6 Terra.
Gemini 4 Matches GPT-6 Astra, Trails Opus 5.5
AI Models · Recorded impact score: 4/5
Independent benchmark provider Artificial Analysis shows Google's Gemini 4 Argon scores 52.6-53 on its Intelligence Index, tying with OpenAI's GPT-6 Astra (52.7) but trailing Anthropic's Claude Opus 5.5 (57.6). Argon excels in agentic tasks and hallucination reduction (15% rate, lowest among top models) but lags in terminal coding and knowledge work. Google offers a 50% introductory discount for at least a month, pricing Argon at $2 per million input tokens and $10 per million output tokens—half the price of Opus 5.5 and a fifth of GPT-6 Astra. Per typical task, Argon costs $1.99, compared to $3.26 for GPT-6 Astra and $5.98 for Opus 5.5, though it is token-hungry. Initially, Argon is available only to select cybersecurity teams, with broader API access planned later.
- Gemini 4 Argon scores approximately 52.6-53 on the Artificial Analysis Intelligence Index, tying with GPT-6 Astra (52.7) but trailing Claude Opus 5.5 (57.6).
- Argon leads on AutomationBench (78%) and has a hallucination rate of 15%, the lowest among top models.
Korean AI Lab Upstage releases Solar Mini 4
Recorded impact score: 4/5
New articles added: Korean AI Lab Upstage releases Solar Mini 4, Gemini 4 Argon: Google is back as one of the top three labs in intelligence achieved, AA-AgentPerf-Local: Benchmarking local AI agents on laptops and workstations, GPT-6.1 Sol replaces GPT-6 Sol after just 7 days, with near-Astra intelligence, Announcing the Artificial Analysis Cyber Index Alliance, Claude Sonnet 5.5 reaches #2 on the Artificial Analysis Intelligence Index.
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Questions about Artificial Analysis
What is Artificial Analysis?
Artificial Analysis is a B2B AI benchmarking and analysis company that compares models using performance, cost, speed and evaluation data.
Who uses Artificial Analysis?
AI engineers, researchers, procurement teams and enterprise decision-makers use it to evaluate and select AI models.
How does Artificial Analysis make money?
It monetises through premium subscriptions, commercial API access and enterprise plans built on top of its benchmark data and analytics tools.
Sources & coverage
This profile uses public, official and technically observable information. Missing information does not prove that a product or relationship does not exist. The list below does not imply that every profile statement has been verified.
11 publicly documented primary sources and citations linked across the market graph.
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