license: cc-by-4.0
language:
- en
tags:
- ai
- llm
- large-language-models
- pricing
- api
- vram
- machine-learning
- benchmarks
pretty_name: Convly AI Models Database
size_categories:
- n<1K
configs:
- config_name: default
data_files: convly-ai-models.csv
Convly AI Models Database
A continuously updated, hand-verified dataset of 30+ AI language models — specs, licenses, API pricing (USD per 1M tokens), and local-hardware (VRAM) requirements.
Maintained by Convly.ai · Live interactive version: https://convly.ai/models/
Fields
name, slug, convly_url, developer, model_type, modality, parameters, context_window, max_output, license, open_weights, release_date, input_price (USD/1M tokens), output_price (USD/1M tokens), api_providers, vram_q4 (GB, 4-bit), min_gpu, official_url
Coverage
Frontier + open-weight models as of July 2026: Claude (Opus 4.8, Fable 5, Sonnet 4.6, Haiku 4.5), GPT-5.5, Gemini 3.1 Pro / 3.5 Flash, DeepSeek V4-Pro / V4-Flash / R1, Qwen3 family, Llama 3.x / 4, Gemma 3, Mistral, Phi-4, GLM 5.2, Kimi K2.7 Code, NVIDIA Nemotron 3, and more.
Why
Provider pricing pages are inconsistent and marketing-heavy. This dataset normalizes verified specs, like-for-like per-million-token pricing, and practical 4-bit VRAM requirements so you can tell instantly whether a model runs on your hardware and what it costs to call. It powers the LLM VRAM Calculator, the AI API Cost Calculator, and the AI Price-Performance Index.
Usage
from datasets import load_dataset
ds = load_dataset("sakd99/ai-models-database")
print(ds["train"][0])
Or load the raw files directly:
import pandas as pd
df = pd.read_csv("convly-ai-models.csv")
License
CC BY 4.0 — free to use, share and adapt with attribution to Convly.ai (a link to https://convly.ai/models/ is perfect).
Citation
Convly.ai (2026). Convly AI Models Database [Data set]. https://convly.ai/models/