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AI Stats helps you make informed decisions about which models to use by providing transparent pricing and performance metrics for every model and provider. This page explains how AI Stats calculates, standardises, and presents cost and speed data — allowing you to identify the best value models for your use case.

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Why pricing & performance matter

When choosing a model, you’re balancing three key factors:
  1. 💰 Price — how much each request costs.
  2. ⚙️ Performance — how fast and efficient the model is.
  3. 🧠 Quality — how well it performs on benchmarks.
AI Stats gives you clear, comparable metrics for the first two, and links them with benchmark data to help you find the sweet spot between cost, speed, and intelligence.

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Pricing structure

Pricing varies depending on the provider, model type, and usage mode.
AI Stats normalises all prices to USD per 1,000 tokens (for text models) or the equivalent unit for other modalities.
TypeUnitExample
Text ModelsPer 1,000 tokens0.005input/0.005 input / 0.015 output
Image ModelsPer image$0.02 per 1024×1024 image
Audio ModelsPer minute or MB$0.006 per minute of transcription
Video ModelsPer second or frame$0.01 per second of generated video
EmbeddingsPer 1,000 tokens$0.0001 per 1K tokens

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Input vs Output pricing

For most text-based models, you’ll see two separate costs:
  • Input price — charged per 1,000 tokens you send (the prompt).
  • Output price — charged per 1,000 tokens the model generates.
Example:
ModelInput (USD / 1K tokens)Output (USD / 1K tokens)
GPT-4o$0.005$0.015
Claude 3.5 Sonnet$0.003$0.015
Gemini 1.5 Pro$0.00125$0.005
Mistral Large$0.002$0.006
AI Stats displays both prices for full transparency and allows filtering or sorting by either.

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Cost per request (E2E)

AI Stats also computes an estimated cost per complete request, which factors in:
  • The average number of input tokens per request.
  • The average model output length.
  • The current pricing structure for that provider.
This gives a more realistic “per-call” cost when comparing across models and providers.

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Measuring performance

AI Stats tracks three main performance metrics for every model and provider:
MetricDescriptionExample
ThroughputThe average number of tokens processed per second (TPS).e.g. 400 tokens/sec
LatencyThe time between sending a request and receiving the first token (TTFT - time-to-first-token).e.g. 800ms
End-to-End LatencyThe total duration from request to the last token in the response.e.g. 3.2s
Together, these metrics reflect responsiveness and efficiency — critical for interactive or production workloads.

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How we measure it

AI Stats uses standardised benchmarking scripts to gather real-world performance data for each model and provider. Each test includes:
  • Repeated requests with consistent prompts (to reduce variance).
  • Averaged results across multiple time periods.
  • Measurements from different regions when applicable.
For transparency, raw performance data is often available through the AI Stats Gateway API or the open Performance Dataset.

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Interpreting performance

ScenarioWhat to look for
Interactive chatbotsPrioritise low latency (< 1s TTFT).
Large document summarisationFocus on high throughput (tokens/sec).
Batch processingBalance throughput and cost — consider cheaper models with slightly higher latency.
Real-time streamingRequires providers that support streaming APIs and sub-second TTFT.
Use AI Stats’ performance charts to visualise these trade-offs interactively.

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Example performance snapshot

{
	"model": "gpt-4o",
	"provider": "OpenAI",
	"input_price_usd_per_1k": 0.005,
	"output_price_usd_per_1k": 0.015,
	"throughput_tps": 420,
	"latency_first_token_ms": 700,
	"latency_total_ms": 3100,
	"uptime_percent": 99.97
}

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Cost-to-performance ratio

AI Stats calculates a cost-to-performance ratio (CPR) — a derived metric that helps identify the most efficient models. Formula: math CPR = (Cost per 1K tokens) / (Throughput in tokens/sec) Lower CPR values indicate better cost efficiency for the same throughput.

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Data refresh frequency

Pricing and performance data are refreshed regularly:
Data TypeUpdate Frequency
PricingEvery 24 hours
PerformanceEvery 6 hours
BenchmarksWeekly or on new releases
Each update is timestamped and versioned for transparency.

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Example use cases

GoalExample
Compare models by speed“Which model has the lowest latency?”
Find the best cost-per-token ratio“Which model offers the best throughput for under $0.01/1K tokens?”
Identify regional variations“Does latency differ between EU and US endpoints?”
Balance cost and performance“What’s the most efficient model for real-time summarisation?”

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Contributing or validating data

You can help by submitting verified pricing or performance updates through GitHub.
All submissions are reviewed before being included in the live dataset.

Contribute Pricing & Performance Data

Help maintain the accuracy of cost and performance data across providers.

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Next steps

Now that you understand how pricing and performance are measured, you can explore how to integrate models programmatically via the AI Stats Gateway.

Integrate with the Gateway

Learn how to build using the AI Stats Gateway API.
Last modified on February 11, 2026