我每月花5000元养AI，Anthropic拿走了80%毛利
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TL;DR - A WeChat analysis pieces together third-party estimates (SemiAnalysis, The Information, Epoch AI, company filings) to show that LLM providers' inference gross margins have swung sharply positive, yet API list prices have stayed flat or risen — meaning falling compute costs are being retained to fund next-generation training rather than passed to users.
- Anthropic's API gross margin is estimated at >80% (blended ~65%), up from roughly -94% in 2024; the swing is attributed to capacity expansion (cited ~220k GPUs / 300+ MW added) plus inference optimizations (prompt caching, batching, speculative decoding, quantization, MoE routing). Training compute sits in R&D, not COGS, so it is excluded from these margins.
- Epoch AI data cited shows median inference price for equal capability falling ~50x per year, yet 2026 flagship pricing held flat or increased (Kimi K3 anchor raised 5-6x; Zhipu GLM Coding Plan Pro from ¥149 to ¥538; Anthropic Enterprise moved from a $200/seat cap to usage-based; DeepSeek adding peak/off-peak pricing).
- Margin profiles diverge by strategy: DeepSeek reportedly hits 70-80% margin at floor prices (engineering efficiency, ~$500M annualized revenue), Zhipu's API margin rose from <4% to 18.9% (¥724M 2025 revenue, 41% blended), MiniMax from 12.2% to 25.4% with ~70% overseas revenue, while OpenAI remains deeply negative (cited Q1 $9.3B loss, -122% non-GAAP margin) largely from subsidizing ~1B free users at ~$0.7/user/month.
- The author's framing: pricing has shifted from cost-plus to value-based ("what it would cost you to do the work"), and retained gross profit is the primary funding source for next-gen training — SemiAnalysis is cited projecting ~$160B reinvestable for Anthropic and ~$92B for OpenAI by 2027. Note these are third-party estimates, not audited figures.
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我每月花5000元养AI，Anthropic拿走了80%毛利
TL;DR - A WeChat analysis pieces together third-party estimates (SemiAnalysis, The Information, Epoch AI, company filings) to show that LLM providers' inference gross margins have swung sharply positive, yet API list prices have stayed flat or risen — meaning falling compute costs are being retained to fund next-generation training rather than passed to users.
- Anthropic's API gross margin is estimated at >80% (blended ~65%), up from roughly -94% in 2024; the swing is attributed to capacity expansion (cited ~220k GPUs / 300+ MW added) plus inference optimizations (prompt caching, batching, speculative decoding, quantization, MoE routing). Training compute sits in R&D, not COGS, so it is excluded from these margins.
- Epoch AI data cited shows median inference price for equal capability falling ~50x per year, yet 2026 flagship pricing held flat or increased (Kimi K3 anchor raised 5-6x; Zhipu GLM Coding Plan Pro from ¥149 to ¥538; Anthropic Enterprise moved from a $200/seat cap to usage-based; DeepSeek adding peak/off-peak pricing).
- Margin profiles diverge by strategy: DeepSeek reportedly hits 70-80% margin at floor prices (engineering efficiency, ~$500M annualized revenue), Zhipu's API margin rose from <4% to 18.9% (¥724M 2025 revenue, 41% blended), MiniMax from 12.2% to 25.4% with ~70% overseas revenue, while OpenAI remains deeply negative (cited Q1 $9.3B loss, -122% non-GAAP margin) largely from subsidizing ~1B free users at ~$0.7/user/month.
- The author's framing: pricing has shifted from cost-plus to value-based ("what it would cost you to do the work"), and retained gross profit is the primary funding source for next-gen training — SemiAnalysis is cited projecting ~$160B reinvestable for Anthropic and ~$92B for OpenAI by 2027. Note these are third-party estimates, not audited figures.