AI Trends
Performance, pricing, and the race between nations — tracked across 500+ models and 50+ benchmarks.
Benchmark Performance
How quickly frontier capability is improving, and how tightly packed the leaders have become.
Labs and Countries
Who is leading, who is shipping the most, and how the balance of power is shifting across labs and geographies.
Open Models
How open-weight models are growing, how close they are to proprietary systems, and where the open race is happening.
Model Capabilities
What kinds of models are being released, from multimodal systems to MoE-based open models.
Prices and Value
How fast intelligence is getting cheaper, which models deliver the most value, and where prices differ across the market.
Efficiency and Scale
How architecture, parameters, and training scale affect capability, and where the most efficient models sit on the frontier.
Speed and Context
What you trade off when deploying models: context length, throughput, and how much speed costs in capability.
Human Preference
Where benchmark scores line up with people, where they do not, and how human preferences differ across models and users.
The State of AI in
The pace of change in AI statistics is hard to overstate. Models that topped benchmarks six months ago are now middle of the pack. New AI growth trends are showing up in reasoning depth, multimodal understanding, and raw efficiency.
Much of this AI industry growth comes from labs competing on every front. OpenAI, Anthropic, Google, and Meta keep raising the bar, while Mistral, DeepSeek, and Alibaba release open-weight models that perform surprisingly well. We track these shifts across 500+ models and 50+ benchmarks in our LLM statistics.
Key AI Statistics
The forces shaping how AI models improve
US vs China AI Race
US labs like OpenAI, Anthropic, and Google still lead most benchmarks. But Chinese labs (DeepSeek, Alibaba, ByteDance) are closing in fast, especially on reasoning and coding tasks.
Open vs Closed Source
The gap is shrinking. Llama, Mistral, and Qwen now match or beat GPT-4 on several benchmarks. You can run capable models locally that would have required API access a year ago.
Falling Inference Costs
Prices keep dropping. GPT-4-level performance cost $30/M tokens in 2023. Today you can get it for under $1/M. Competition and better infrastructure are driving 10-100x reductions each year.
Parameter Efficiency
Smaller models are catching up. A 7B model today can hit scores that took 70B+ parameters last year. This means you can run strong models on a laptop or deploy them affordably.
Understanding AI Benchmark Statistics
AI benchmark statistics give you a way to compare models on specific tasks. GPQA tests graduate-level science reasoning. HumanEval measures code generation. MMLU covers broad knowledge. Each benchmark tells you something different about AI performance data.
When you look at LLM growth rate across these benchmarks, the improvement is clear. GPQA scores went from around 50% to 75%+ in just 18 months. That kind of language model growth will likely continue, though some benchmarks are starting to saturate.
Frequently Asked Questions
Common questions about AI statistics, growth trends, and industry data
What are the current AI growth trends?
How do US and China compare in AI development?
Are open-source AI models catching up to proprietary ones?
How fast are AI inference costs decreasing?
What AI statistics does LLM Stats track?
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