OpenAI and Anthropic cut their LLM prices by up to 80% within a few months. Why?
- ID
- 39436
- Status
- summarized
- Published
- 26 Oct 2026, 5:11 AM
- Fetched
- 26 Oct 2026, 12:19 PM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://minimaxir.com/2026/10/cheap-llms/
- Source URL
- https://hnrss.org/best
Summary
- Score
- 8.0
- Created
- 26 Oct 2026, 12:19 PM
- Tags
- Audience
- developersvibe_codersai_agent_userssaas_foundersai_ml_learners
What happened
Max Woolf's timeline tracks OpenAI and Anthropic LLM price cuts from June to September 2026: OpenAI cut GPT-5.6 Luna by 80% to $0.20/$1.20/$0.02 per input/output/cache-read MTok on July 30, and Anthropic made Sonnet 5's $2/$10/$0.20 launch pricing permanent on August 10. The post argues cache-read pricing is the bigger lever for agents: Fable 5.1 cut cache reads 75% to $0.25, and cache reads are 96-99% of token spend in agentic coding, implying roughly 70-75% lower cost per task. It also notes GPT-6 Sol launched at $2/$10/$0.20, continuing the price compression.
Why it matters
For anyone shipping LLM features or agents, the decision changes: measure cost-per-completed-task with real cache-hit rates, not headline output-token price. A 75% cache-read cut can dominate a model's economics because agent conversations re-read the cache 90-100% of the time and cache reads are 96-99% of token expenditure; Luna at $0.20/$1.20/$0.02 also resets the floor for high-volume cheap-model work.
Discussion angle
Given cache reads are 96-99% of agent token spend, what cache-hit rate and cost-per-task would make you switch a coding agent from Fable 5.1 or GPT-6 Astra to GPT-6 Sol or Luna, and how would you verify it?