Discover which large language models are gaining traction, explore historical token usage, compare pricing, and estimate inference costs—all in one place.
The artificial intelligence landscape is evolving at an extraordinary pace. New large language models (LLMs) are released regularly, pricing changes, context windows expand, and developers have more choices than ever before.
But with so many models available, one important question remains:
How do you know which AI models are actually being used?
That’s why we created LLM Radar, a data-driven platform designed to make exploring and understanding the LLM ecosystem easier.
Whether you’re an AI developer, researcher, startup founder, or simply curious about the latest AI models, LLM Radar helps you look beyond benchmarks and marketing claims to explore real-world usage trends.
What Is LLM Radar?
LLM Radar is an AI model analytics platform that combines historical token usage statistics with model specifications and pricing information from OpenRouter.
Instead of jumping between different websites to research models, LLM Radar brings useful information together in an intuitive dashboard.
With LLM Radar, you can:
- Explore historical token usage: See how model activity changes over time.
- Identify trending models: Discover which models are experiencing increased usage.
- Compare model popularity: Understand how models rank based on recorded token consumption.
- Examine model specifications: Review context windows, output limits, and supported modalities.
- Analyze pricing: Compare published input and output token rates.
- Estimate inference costs: Calculate approximate API expenses using the companion LLM Token Calculator.
The goal is simple: make LLM usage data more accessible, understandable, and actionable.
Go Beyond AI Benchmarks
Benchmarks are useful for evaluating model capabilities, but they don’t tell the whole story.
A model might perform exceptionally well on coding, reasoning, or mathematics benchmarks while seeing relatively little real-world usage.
Conversely, a smaller, more affordable model might attract substantial usage because of its speed, accessibility, or cost efficiency.
LLM Radar provides another perspective by examining token consumption across models represented in its historical dataset.
Track Token Usage Over Time
One of LLM Radar’s core features is its historical token usage dashboard.
Search for a supported model and explore statistics such as:
- Total recorded tokens
- Average daily token usage
- Peak usage days
- Recent usage trends
- Relative usage rankings
Interactive charts make it easier to identify changes in model activity over time.
For example, you might discover that a newly released model is gaining adoption rapidly or that an established model is maintaining consistent demand despite growing competition.
These observations can provide useful context when evaluating the changing AI ecosystem.
It’s important to note that token usage reflects activity within the dataset’s coverage, not necessarily total worldwide model adoption.
Discover Which AI Models Are Trending
With new models appearing frequently, keeping track of the ecosystem can become overwhelming.
LLM Radar helps users explore which models are generating substantial token activity and how that activity changes over time.
Rather than relying exclusively on social media attention or benchmark leaderboards, users can examine recorded usage patterns.
This makes it possible to investigate questions such as:
Which models are gaining momentum?
Are smaller models becoming more widely used?
How does a new release compare with established alternatives?
Which models consistently rank among the most-used models?
By combining historical trends with model information, LLM Radar makes these questions easier to explore.
Introducing the LLM Token Calculator
Alongside LLM Radar, we’ve also introduced a companion tool: LLM Token Calculator.
Understanding token usage is important, but understanding how tokens translate into API costs is equally valuable.
Different AI models use different tokenization methods, and providers often charge separate rates for input and output tokens.
The LLM Token Calculator is designed to simplify this process.
Count Tokens with Modern Tokenization Libraries
The calculator supports token counting through two JavaScript libraries:
GPT Tokenizer
The gpt-tokenizer library provides tokenization for supported GPT-style encodings, helping developers understand how text is divided into tokens.
Transformers.js
Transformers.js enables browser-based tokenization using compatible Hugging Face tokenizer repositories.
This is particularly useful when working with models outside the GPT family, such as Llama and other open-weight architectures.
Where a model-specific tokenizer is available, the calculator can provide a more representative count than using a generic tokenizer.
Token estimates may still differ from billed usage because API providers can include chat templates, special tokens, tool calls, and other processing overhead.
Estimate API Costs Before Sending Requests
The LLM Token Calculator also retrieves model pricing information from OpenRouter.
Users can estimate the cost of processing text using a selected model.
For example, suppose a model charges:
| Pricing component | Example rate |
|---|---|
| Input tokens | $2 per million |
| Output tokens | $8 per million |
| Input usage | 10,000 tokens |
| Expected output | 2,000 tokens |
The estimated cost would be:
- Input: $0.020
- Output: $0.016
- Total: $0.036
These are illustrative rates, not the current pricing of a particular model.
For developers building applications that process large volumes of text, this kind of calculation can help with budgeting and model selection.
Powered by OpenRouter Data
LLM Radar uses data from OpenRouter, a platform that provides access to a broad range of AI models through a unified API.
OpenRouter publishes model metadata and datasets covering daily token usage for leading models.
LLM Radar uses these resources to present historical usage alongside information such as:
- Model identifiers and providers
- Input and output token pricing
- Context window sizes
- Supported input and output modalities
- Available output token limits
To help keep historical statistics current, LLM Radar also includes a PHP-based data updater designed to retrieve newly published daily usage records and merge them into its CSV dataset.
The dashboard currently focuses on models represented in that dataset, while the companion calculator can explore models available through OpenRouter’s catalog.
Built for Developers, Researchers, and AI Enthusiasts
LLM Radar is designed for anyone who wants a clearer view of the rapidly changing LLM landscape.
For developers, it offers model specifications, usage context, and token cost estimates that can inform technical decisions.
For AI researchers, historical token trends provide another signal for studying model activity and ecosystem changes.
For startups and businesses, pricing comparisons and usage analytics can support early-stage model evaluation and API budgeting.
For AI enthusiasts, the dashboard offers a straightforward way to explore which models are attracting attention through measurable usage.
A Simple, Accessible Approach to AI Analytics
We believe useful AI analytics should be easy to access without complicated setup or expensive enterprise dashboards.
That’s why LLM Radar emphasizes a straightforward experience.
Search for a model, explore its usage history, inspect its specifications, and compare pricing.
When you need to estimate how much a prompt might cost, switch to the companion LLM Token Calculator.
Both tools are designed to make AI model exploration more practical.
What’s Next for LLM Radar?
LLM Radar is just getting started.
As the AI ecosystem continues to evolve, there are several exciting directions for future development:
- Model comparison dashboards: Compare multiple models side by side.
- Usage trend alerts: Identify meaningful changes in token activity.
- Pricing history: Track how model pricing evolves over time.
- Advanced analytics: Explore growth rates, market share within the dataset, and longer-term trends.
- Expanded tokenizer support: Improve automatic tokenizer selection across model families.
- Model discovery tools: Make it easier to identify promising models based on cost, capabilities, and usage.
Our long-term vision is to make LLM Radar a useful destination for exploring the data behind the AI model ecosystem.
Start Exploring LLM Radar
The world of large language models is moving quickly, and choosing the right model requires more than looking at benchmark scores alone.
Usage patterns, token efficiency, pricing, and technical capabilities all matter.
LLM Radar brings these signals together to help you explore the AI landscape with greater clarity.
Whether you’re comparing models for your next application, investigating industry trends, or estimating the cost of an AI-powered project, LLM Radar and the LLM Token Calculator are designed to make the process easier.
Explore the models. Follow the trends. Understand the tokens.
Welcome to LLM Radar.
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