Artificial Analysis
Independent benchmarking platform comparing AI models and inference providers across intelligence, speed, and cost.
Pick Artificial Analysis if you need to compare frontier models and inference providers on cost, speed, and quality before committing to one.
Skip it if you want a free programmatic feed of benchmark scores or in-depth qualitative model reviews.
Artificial Analysis is an independent evaluation platform that benchmarks frontier language models, coding agents, image generators, video models, and speech systems against each other. It runs proprietary benchmarks such as the Artificial Analysis Intelligence Index, GDPval-AA, Terminal-Bench, and AA-Briefcase, and tracks real-time latency, throughput, and pricing across 18+ API providers serving the same model. Coverage spans 500+ models from Anthropic, OpenAI, Google, Meta, Alibaba, DeepSeek and the open-weights ecosystem.
The target audience is engineering and procurement teams who need to pick a model and a hosting provider for a specific workload rather than relying on vendor marketing. Leaderboards are filterable by use case, and a recommendation tool maps requirements to a shortlist. Core leaderboards and provider comparisons are free to browse; expanded benchmark data, custom visualizations, and industry reports sit behind paid plans aimed at enterprise buyers.
The blind preference arenas for image, video, and speech add a human-judged signal that complements the quantitative benchmarks, and the per-provider speed and cost tables are particularly useful when the same open-weights model is served at very different price points. There is no public API for the benchmark data itself, which is a real limitation for anyone wanting to wire the numbers into their own dashboards.
The most useful neutral scoreboard in the LLM market right now. The provider-level latency and price tables alone justify bookmarking it before any serious model selection. The lack of an open API is the one thing keeping it from being indispensable infrastructure.
— The AI Tool Bible editorial team
Pros
- ✅ Independent, methodologically transparent benchmarks across 500+ models
- ✅ Real-time speed and price tracking per inference provider, not just per model
- ✅ Covers text, code, image, video, and speech under one roof
- ✅ Blind preference arenas add human-judged signal alongside quant scores
Cons
- ⚠️ No public API for programmatic access to benchmark data
- ⚠️ Premium pricing is not disclosed on the site
- ⚠️ Aggregate scores can mask task-specific performance differences
Use cases
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