README.md

metadata

license: cc-by-4.0
task_categories:
  - tabular-classification
  - text-classification
language:
  - en
tags:
  - ai
  - benchmarks
  - llm
  - pricing
  - machine-learning
  - gpt
  - claude
  - gemini
  - deepseek
  - mcp
pretty_name: AI Model Benchmarks & Pricing 2026
size_categories:
  - n<1K

AI Model Benchmarks & Pricing Dataset 2026

A comprehensive survey of large language model performance and economics, maintained by BenchGecko.

What's Inside

File	Records	Description
data/models.csv	20	Top AI models with benchmark scores and API pricing
data/providers.csv	20	AI model providers with metadata
data/benchmarks.csv	40	Benchmark suites with methodology
data/mcp_servers.csv	20	Model Context Protocol servers

This is a sample from the full dataset. The complete dataset covers thousands of models, hundreds of providers, and over a hundred benchmarks, updated every two hours at benchgecko.ai.

Fields (models.csv)

Column	Type	Description
name	String	Model display name
provider	String	Organization that created the model
input_price	Float	USD per 1M input tokens
output_price	Float	USD per 1M output tokens
context_window	Integer	Maximum context length in tokens
average_score	Float	Weighted average across all benchmarks (0-100)
mmlu_score	Float	MMLU benchmark score
humaneval_score	Float	HumanEval coding score
gpqa_score	Float	GPQA Diamond science score
math_score	Float	MATH competition score
open_source	Boolean	Whether weights are publicly available
release_date	Date	Public release date

Quick Start

from datasets import load_dataset

dataset = load_dataset("BenchGeckoAI/ai-model-benchmarks-2026")
models = dataset["train"]

# Find the best open-source model
open_models = [m for m in models if m["open_source"]]
best = max(open_models, key=lambda m: m["average_score"])
print(f"Best open model: {best['name']} ({best['average_score']})")

Use Cases

• Model Selection: Compare benchmark scores across evaluation types before deploying
• Cost Analysis: Find the best price-to-performance ratio across providers
• Market Research: Track the AI model landscape and provider ecosystem
• Academic Research: Study capability trajectories and scaling laws

Full Dataset

This sample covers 20 models. The full live dataset is available through:

• Web: BenchGecko Model Rankings
• API: BenchGecko API Documentation
• Pricing: Cross-Provider Pricing Comparison
• Compare: Side-by-Side Model Comparison
• Economy: AI Economy Dashboard
• Compute: AI Compute Supply Chain
• Mindshare: Developer Mindshare Arena

Methodology

Benchmark scores sourced from original model technical reports and cross-verified using open-source evaluation frameworks (EleutherAI lm-evaluation-harness, BigCode HumanEval+). Pricing collected from official API documentation, updated within 48 hours of changes.

Citation

@dataset{benchgecko2026,
  author = {BenchGecko},
  title = {AI Model Benchmarks and Pricing Dataset 2026},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/BenchGeckoAI/ai-model-benchmarks-2026}
}

License

CC BY 4.0. Attribution: BenchGecko (https://benchgecko.ai)