Model distillation means training a smaller model (student) on the output of a stronger one (teacher). In 2026, more and more Chinese enterprises feed high-quality GPT / Claude / Gemini output into distillation corpora to train their own models — sidestepping part of the data barrier while pushing inference cost down.
The cost structure: three price tiers
For the same 100 million tokens of distillation corpus, cost varies wildly by sourcing channel:
- Direct original API keys: list price, no discount, and high-frequency batch calls easily trip risk controls;
- Enterprise Enhanced: channel package pricing — roughly 35% off list for GPT/Gemini and 24% off for Claude, stable and stockpilable;
- Shared accounts / key pools: looks cheaper on paper, but multiplier games, opaque metering and ban risk are uncontrollable — not fit for enterprise distillation.
What the gap is worth
At 1 billion tokens a month, Enterprise Enhanced saves a seven-figure sum a year against list-price buying; shared accounts look cheap until a single ban interrupts the pipeline, and that loss dwarfs the discount. Enterprise distillation needs guaranteed subscription, stability and auditable billing — not the lowest sticker price.
That is why LOCOM positions US-model APIs as a dedicated channel for enterprise distillation: Shenzhen source, Philippine card-payment stock, 30-day stockpiling — turning an uncontrollable grey channel into contract-backed corporate supply.
LOCOM frontier-model API, built for enterprise distillation: Enterprise Enhanced GPT/Gemini at 35% off list, Claude at 24% off. Subscription-protected, stockable for 30 days, sourced direct from Shenzhen with corporate invoicing.
Need a distillation-specific quote? Write to 23854381@qq.com or batony@szlocom.com.