OpenAI and Anthropic cut their LLM prices by up to 80% within a few months. Why?

Let’s look at a quick timeline of the major LLM model announcements from both OpenAI and Anthropic over the past few months. All pricing numbers provided are input MTok / output MTok / cache read MTok.June 26, 2026#OpenAI announced GPT-5.6 Sol and Luna. Sol is the direct successor to GPT 5.5 at the same price of $5 / $30 / $0.50, while Luna is a small model specifically designed for cheaper use cases at $1 / $6 / $0.10. They also announced a Terra model that’s in between those two models, but few cared about it.June 30, 2026#Anthropic announced Claude Sonnet 5, which Anthropic claimed to be closer to their Opus models, but feedback was mixed. Notably, it had launch pricing of $2 / $10 / $0.20 which was initially supposed to be temporary but would go up.July 24, 2026#Anthropic announced Claude Opus 5, which was a long-awaited major-version update given the impact Opus 4.5 had on the rise of agentic coding, at a price of $5 / $25 / $0.50 (similar to GPT-5.6 Sol). Although some of the provided benchmark results surprisingly exceeded those of the much larger Fable 5, real-world feedback was more mixed.July 30, 2026#OpenAI announces that they cut the price of GPT-5.6 Luna by 80% (???) to $0.20 / $1.20 / $0.02.Model quality vs. Cost-per-task for GPT-5.6 Luna after the massive price reduction. Top-and-left is better, and if OpenAI didn’t do the 80% price cut, it would have lost to DeepSeek V4 Pro. via OpenAIAugust 10, 2026#Anthropic announces the temporary Sonnet 5 price cut for the launch is now permanent.August 21, 2026#OpenAI announces that they are cutting the price for GPT-5.6 Sol by ~20% ($4 / $20 / $0.40) for the next three months.September 1, 2026#Anthropic announced Fable 5.1 at a price of $10 / $50 / $0.25, which, in addition to having strictly better benchmark performance in all areas than Fable 5 from June at $10 / $50 / $1, cut the cache read cost by 75%.Cache read costsWhen using agents for coding, cache read costs are the vast majority (96% - 99%) of the total token expenditure as the cache is what contains the entire previous conversation each turn. Additionally, the cache tends to be hit around 90 - 100% of the time (varies by the infrastructure of the LLM inference provider). Taken together, a 75% reduction in the cache read cost correlates to a 70-75% reduction in the cost of using the model—obviously notable, even if cache costs are typically a footnote!September 3, 2026#OpenAI announced GPT-6 Astra, which is similar in size to Fable 5.1 and …