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Free crowdsourced benchmark that pits top AI models head-to-head on design tasks and ranks them by public votes.
Open-source desktop app for running AI chat models locally or via APIs, as a private ChatGPT alternative.
Open-source library and desktop app for fast, memory-efficient local fine-tuning and inference of open LLMs.
AI research lab building multimodal 'omni' foundation models and infrastructure aimed at robotics and physical-world applications.
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Side-by-side model output comparison
- ✦Public voting on results
- ✦Leaderboards ranking AI models by 'taste'
- ✦Coverage of websites, games, 3D, UI, images, logos, SVG, video and slides
- ✦Run open-source LLMs locally
- ✦Connect to online models (OpenAI, Claude, Gemini)
- ✦Private, offline-capable AI chat
- ✦Open source and self-hostable
- ✦Model library via Hugging Face
- ✦Cross-platform desktop app
- ✦Optimized LoRA/FFT/PT training kernels for 500+ models
- ✦Local offline model runner for Mac and Windows
- ✦No-code dataset creation from PDFs, CSVs, and JSON
- ✦Unlimited tool-calling and web search inside model runs
- ✦Data Recipes workflow to turn documents into training datasets
- ✦Export to safetensors or GGUF for llama.cpp, vLLM, Ollama
- ✦Multi-GPU support on paid tiers
- ✦Omni multimodal model research and development
- ✦Real-time inference API (Infer) for enterprise use
- ✦Video tagging, search, and clipping infrastructure
- ✦Training data generation from egocentric and robotics footage
- ✦One unified, OpenAI-compatible API for 400+ models
- ✦Automatic provider failover for higher uptime
- ✦Edge routing for low latency
- ✦Custom data and provider policies
- ✦Pay-as-you-go credits usable across any model
- →Compare which AI model produces the best design output
- →Track AI design model rankings
- →Discover models for a specific creative task
- →Private local AI chat
- →Using multiple models in one app
- →Avoiding cloud data sharing
- →Experimenting with open models
- →ML engineers fine-tuning open models on a single GPU for free
- →Teams building custom datasets from unstructured documents
- →Developers wanting to run and compare LLMs fully offline
- →Enterprises needing faster, more accurate multi-node training
- →Powering robotics perception with multimodal AI
- →Running large-scale video search and analysis via API
- →Sourcing specialized training data for frontier AI models
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products