Frugal AI Switchboard – UPDATED v2

🎛️ Frugal AI Switchboard

Most cost-effective: ${models.reduce((a, b) => a.costNum a.costNum < b.costNum ? a : b).cost})
Greenest: ${models.reduce((a, b) => a.emissionsNum a.emissionsNum < b.emissionsNum ? a : b).emissions})
Most accurate: ${models.reduce((a, b) => a.arizeNum > b.arizeNum ? a : b).name} (${models.reduce((a, b) => a.arizeNum > b.arizeNum ? a : b).arizeAccuracy})
Most sovereign: ${models.reduce((a, b) => a.sovereignty > b.sovereignty ? a : b).name} (${models.reduce((a, b) => a.sovereignty > b.sovereignty ? a : b).sovereignty}%)

Why smaller, local, open models beat hyperscale for most tasks

Select a category to view comparisons

Data sources: Energy & CO2: Ecologits; Accuracy: Arize Phoenix; Other data (pricing, parameters): Model cards

Frugal AI Switchboard was vibe coded by James Martin, BetterTech, on Mistral Vibe. This uses the Mistral Medium 3.5 model (128B), and Vibe consumed 282k tokens to create this module. Re. Ecologits, this equates to 148Wh of electricity; 10.6 grammes of CO2 (emissions); 239 ml of water, and 235 µgSbeq of metal/mineral impacts. Thanks to Tom Watson, RL Nabors and more for the inspiration. Keep your AI frugal; and ping me for any suggestions!

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