Artificial Analysis
Artificial Analysis is an independent evaluation platform for
Artificial Analysis
Core parameters and statistics
| Parameters | Official verifiable information |
|---|---|
| Product Positioning | The leading independent AI benchmarking company |
| Evaluation spindle | Intelligence, Speed, Cost per Task, Provider Performance |
| Platform coverage | Language, Coding Agents, Image, Video, Speech, Music |
| Data scale | 500+ models, 100+ inference providers, 0B+ evaluation tokens (the page displays continuous growth indicators) |
| Key self-research indicators | Artificial Analysis Intelligence Index, AA-Briefcase, AA-Omniscience, GDPval-AA v2 |
| Commercialization | Pro $417/month per seat; Enterprise custom |
| API capabilities | Provide API and Databook Download |
| Public Commitments | Independence Policy, providers cannot pay for results or methodology changes |
A brief comment: The core value of Artificial Analysis is not "who ranks first", but to bring the cost, speed, quality and supplier differences, which are the most difficult to combine in AI selection, into the same framework.
Publicity verification: The official positions itself as an independent benchmarking company. This selling point is more tenable than ordinary ranking sites, because it not only lists scores, but also discloses methodology, provider performance, pricing cache rules, time per task and benchmark breakdown together.
User and market recognition
Artificial Analysis is not a tool for ordinary consumers. It is aimed at decision makers who need to do model procurement, inference selection, benchmark comparison and management reporting. The Pricing page publicly lists a number of logos publicly referenced by, including Amazon, Google, IBM, Meta, Microsoft, Bloomberg, CNBC, Financial Times, BCG, McKinsey, Stanford HAI, and others. This cannot simply be understood to mean that all are customers, but at least it shows that its charts and conclusions have been widely cited at the industry and media levels.
Another strong signal is the pace of platform updates. The FAQ states that leading models and providers usually complete the benchmark within 24 hours of release, which makes it more like a real-time intelligence system rather than a research report updated every six months.
Cost advantage
| Package | Official public price | Core content |
|---|---|---|
| Free / Public site | $0 | View public lists, methodology, some charts and changelog |
| Pro | $417/month/seat | Full access to data, reports, API, custom charts, email support |
| Enterprise | Custom | Higher API rate limits, custom benchmarking, workshops, AI advisory, personalized support |
Free truth: Public sites are enough for you to see trends, but the real purchase value is Pro and Enterprise, because export, table building API, industry reports and support are all in the paid tier.
Quantification of cost reduction and efficiency improvement: For teams doing model selection, the most time-consuming thing is not to run a benchmark, but to summarize "quality, speed, price provider differences, and caching rules" into decision-making conclusions. Artificial Analysis compresses this type of research from manual collection of many days or even weeks to hour-level screening and export within the same platform. This is a process deduction based on the platform form, not an official commitment.
Hidden benefits/costs: It can reduce the rework costs caused by wrong procurement and wrong model routes, but it will also make the team more dependent on third-party evaluation standards. If your real workload is very different from its benchmark structure, it will still be a mistake to draw conclusions directly based on the list.
Main functions
- Intelligence / Speed / Cost triple view: Put model intelligence, output speed and cost per task in one coordinate system, suitable for first-pass model selection.
- Provider Performance: The same model looks at output speed, price and cache pricing according to the provider dimension, which is suitable for inference supplier selection.
- Coding Agent Index: conduct end-to-end software engineering benchmark on agents such as Claude Code, Codex, Cursor Desktop, Gemini Desktop, etc.
- AA-Briefcase / GDPval-AA / AA-Omniscience: A benchmark that covers long-cycle knowledge work, economic value tasks, illusions and knowledge reliability, etc. that are closer to actual business.
- Image / Video / Speech leaderboards: Expand the platform to multi-modality, not just focus on language models.
Expert View: The hidden linkage of Artificial Analysis is that "the chart is not the chart itself". When cost, speed cache pricing, provider P50 sustained performance and benchmark methodology are unified in one system, you can save a lot of Excel spreadsheet work.
Model and version evolution
The evolution of Artificial Analysis is not the push-button release of traditional software functions, but the continuous expansion of "new index + new benchmark + new provider data layer".
| Milestones | Dates | Key changes |
|---|---|---|
| 2026-07 platform snapshot | 2026-07-01 | Continuously update models and provider benchmarks, and keep the language and multimodal lists up to date |
| Intelligence Index v4.1 | 2026-06-15 | The index is more biased toward agentic workloads and strengthens per-task metrics |
| AA-Briefcase | 2026-06-18 | New growth cycle knowledge work benchmark |
| Coding Agent Benchmarks | 2026-05-11 | Online coding agent comprehensive evaluation |
| ITBench-AA | 2026-05-27 | Introducing SRE / Kubernetes incident root-cause scenario benchmark |
This shows that it has expanded from "making language model rankings" to "making AI industry intelligence infrastructure." The direction of adding new benchmarks is also very clear, getting closer and closer to the real workflow, rather than just scoring common test scores.
Technical advantages
Main type judgment: The main delivery form of Artificial Analysis is productivity/business-side application, and the core output is benchmark intelligence and decision support, not the underlying model or database itself.
Complete evaluation dimensions: Many lists can only tell you who is better, but they cannot tell you why it is expensive, why it is fast, and why it is so different. Artificial Analysis explicitly separates these variables.
Higher transparency of methodology: The site directly opens the methodology, cache pricing breakdown, P50 sustained performance caliber, and various benchmark components, which makes the results more reviewable, rather than just looking at the conclusion.
Get closer to purchasing decisions: Pro is not just about looking at pictures, but Data Playground, Table Builder, Data Export, API and industry reports. These are all for decision-making and reporting, not for onlookers.
Human-machine collaboration boundary: The platform can automatically complete a large number of benchmark aggregation, price comparison and chart generation, but the actual supplier signing, workload reproduction, compliance review and final procurement decision must still be reviewed by the internal team according to their own scenarios. Lists cannot replace PoC.
How to use
| Entrance | Applicable objects | Description |
|---|---|---|
| Public web page | Developers, researchers, investors, product managers | Browse Intelligence, Speed, Cost, Provider, Agent, Multimodal data |
| Pro | Decision-making teams who need exports and APIs | For local analysis, internal reporting and system access |
| Enterprise | Large organizations, research departments, infrastructure teams | For custom benchmarks, workshops, AI advisory |
| API | Internal tools, intelligence panels, data platforms | Obtain programmable data sources |
The typical usage sequence is to first narrow down the model scope on the public page, then look at provider performance and cost per task, and finally use Pro to export data or API to connect to the internal dashboard. For procurement or platform teams, this is much more efficient than manually searching for docs and pricing pages.
Product Pricing
| Package | Price | Adaptation Scenario |
|---|---|---|
| Public access | $0 | View the list, view the methodology, and track the changelog |
| Pro | $417/month/seat | Single-seat user requires full access, API, exports and reporting |
| Enterprise | Custom | Large team, redistribution, custom benchmark, consulting services |
Another key point is that the official clearly states no free trial. This means that it does not rely on low-threshold trials to gain volume, but sells itself as a professional intelligence product.
Application scenarios
- Model and provider selection: Compare the price, speed and sustained performance of the same model on different providers, suitable for inference stack procurement.
- Management and Investment Research: Develop a decision-making briefing on AI market trends, leading models, cost reduction rates and supplier competitive landscape.
- Agent and multi-modal route judgment: Use the Coding Agent Index, AA-Briefcase, and Speech/Image/Video lists to see real workflow capabilities, instead of just looking at plain text test scores.
Dimensionality reduction attack scenario: It is most suitable for teams that want to answer "which model should be used, which provider should be used, and where are the cost and risks" in a short time, rather than ordinary users who simply watch the rankings.
Applicable people
- Suitable for AI platform team and architecture leaders: especially those who need to clearly explain model selection.
- Suitable for research, investment, consulting and industry analysis positions: Because it does not provide a single point of view, but a continuously updated data baseline.
- Suitable for organizations with large budgets but high cost of decision-making errors: High unit price subscriptions are reasonable for this type of users.
Not suitable for boundaries: If you are just an individual developer and occasionally choose a model to try, the public page is usually enough. The Pro price is obviously too high for light users.
Summary and Outlook
The scarcity of Artificial Analysis is not that "it also has a ranking list", but that it combines the list, price provider, agent benchmark and methodology into a unified decision-making system. For those who are really making AI selections, this is much more valuable than reading another "Top Ten Model Recommendations".
Its procurement/adoption risks mainly lie in three points. First, the price is high and not cost-effective for light users. Second, any third-party benchmark has workload deviations and cannot directly replace your own PoC. Third, the more you rely on external intelligence platforms, the more attention you need to pay attention to whether internal business constraints are fully mapped. Treating it as a decision-making accelerator rather than a decision-making substitute is the correct way to open it.
Version Info
- Artificial Analysis platform snapshot 2026-07 ：The current public platform covers Intelligence, Speed, Cost per Task, API Provider Performance, Coding Agent, Image/Video/Speech leaderboards, and will continue to update the latest model and provider benchmark results on 2026-07-01.
- Artificial Analysis Intelligence Index v4.1 ：Intelligence Index v4.1 further shifts the focus of evaluation to agentic workloads, and updates GDPval-AA v2, τ³-Banking, Terminal-Bench v2.1 and per-task metrics.
- AA-Briefcase launch ：Launched AA-Briefcase for long-term knowledge work, using rubric, analytical quality and presentation quality to evaluate agentic knowledge work.
- ITBench-AA launch ：Launched ITBench-AA for SRE scenarios and used Kubernetes incident response tasks to test agentic enterprise IT capabilities.
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