AI Models
Frontier and local models in plain language: what they cost, what runs on your hardware.
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What's the difference between a frontier model and a local model?
A frontier model (Claude, GPT, Gemini) runs in someone else's data center. You send text in, pay per token, and get the most capable systems available. A local model (Qwen, Llama, DeepSeek) is a file you download and run yourself: free per use, fully private, but capped by your hardware. Most people end up using both.
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How much do the Claude, GPT, and Gemini APIs actually cost?
Frontier APIs bill per million tokens, split into input (what you send) and output (what the model writes). In mid-2026, Gemini 2.5 Flash is the cheapest capable option at $0.30/$2.50, while GPT-5.5-pro is the priciest at $30/$180. Output always costs several times more than input, and caching plus batch mode can cut the bill by anywhere from 50 to 90%.
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What are the best local AI models to run in 2026?
For most people running models locally in 2026, Qwen3 is the safest starting point. It spans tiny to flagship sizes and uses a permissive Apache 2.0 license. QwQ-32B leads on reasoning and math, Llama 4 Scout offers a giant 10M-token context (though using it all takes many GPUs, not one), and Mistral Small is the efficient multilingual pick. The right one depends on your hardware tier.
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Claude vs GPT vs Gemini: which frontier model should I use?
In 2026, Claude (Anthropic) leads on coding and long-horizon agents, GPT (OpenAI) on reasoning breadth and ecosystem, and Gemini (Google) on raw context size and low cost. Their flagships are close enough in quality that price, context window, and the specific job usually decide it more than any benchmark does.