Making 'good enough' AI a business: cheap models, cost tiers and the budget gap
This analysis (originally by 最话FunTalk, republished by 36Kr and TMTPost) examines how AI vendors are turning "good enough" intelligence into a business. It notes that on October 6 OpenAI published math research produced by its internal frontier model, saying each result took compute equivalent to roughly three hours of ChatGPT Pro thinking, and that on October 7 Anthropic released the small model Haiku 5.5 for high-volume tasks such as summarization, classification and database queries, while OpenAI pushed GPT-6 to more ChatGPT users and gave free users the lighter Luna. Citing published API prices, it says GPT-6 Astra costs $50 per million output tokens versus $0.5 for Luna, while Haiku 5.5 is priced at $0.1 per million input tokens and $0.5 per million output tokens for prompts under 100,000 tokens, with Anthropic claiming roughly 75% average savings versus its predecessor on real tasks and halving Sonnet 5.5 cache-read prices. In China, Alibaba's Qwen Office, Tencent's Marvis and WorkBuddy, and ByteDance's Doubao Work integration with Feishu are all embedding AI into office workflows; the article argues the test is winning incremental budget, not topping leaderboards.
It points to a second competitive axis beyond frontier capability: whoever delivers adequate intelligence into everyday workflows at a low enough unit cost is more likely to turn heavy AI capex into durable revenue.