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

Timeline of LLM price cuts

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.

The role of cache read costs

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%.

When 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 has similar pricing at $10 / $50 / $1 but without the lower cache read cost. Astra’s model performance is indeed comparable to Fable.

Market competition and open weights

From the timeline above, there is a clear tit-for-tat between Anthropic and OpenAI, and it is reasonable to assume that these particular price cuts from OpenAI and Anthropic are in reaction to each other. However, no one cuts prices by 80% unless there is some externality in play. So, what gives?

If you have been testing agents with new LLMs as they are released, it can be very difficult to differentiate them outside of subjective vibes. Since last November that launched the hypergrowth of agentic LLMs, models have become more of a commodity where it’s trivial to try a new model on your codebase with just a toggle in a harness like OpenCode and Zed Agent, or a different API endpoint string through OpenRouter, and as a result there is little moat. Additionally, there are many competitors—not just large companies like Google and Meta—who can provide models which are sufficient substitutes and therefore allow pricing pressure.

OpenAI and Anthropic will have to differentiate from these open weight models with higher-quality models—they are currently doing a good job with that particularly with Opus 5.5—but they need to keep doing that, which is why billions of dollars are being invested into R&D. Open-weight models are catching up fast: just a few days ago, Moonshot AI announced Kimi K3.1 Pro which has comparable performance to Opus 5.5 (Kimi at 55 vs. Opus 5.5 at 58) at half the input/output price and the same cache read cost ($2 / $10 / $0.20).

IPO pressures and future outlook

The timing of these price cuts is notable because both OpenAI and Anthropic are rumored to be IPOing soon—OpenAI in 2027, and Anthropic within a couple months. So far, leaked financials from both OpenAI and Anthropic show unsurprisingly that they are losing billions due to R&D/scaling, albeit in both cases these reports do not account for the hyperexpansion of agentic LLM use over the past few months. Investors will scrutinize the massive negative profits and demand that they have a plan to turn things back around.

So, why did they cut their primary revenue source by up to 80%? That’s the opposite of what you would want to do to look good on an S-1 prospectus! The profit margins on inference themselves likely tell a different story, with some reports saying the profit-per-token margins are as healthy as 80% with caveats. How much of a profit margin do they have after these major cuts?

Overall, the price cut is weird, but as a user I’m certainly not complaining! Open-weight models and secondary inference providers will continue to serve as an economic check on the prices of LLMs and other business shenanigans the large labs may concoct. Even if Anthropic’s and OpenAI’s IPOs are catastrophic and cause both companies to fold, it will not suddenly cause LLMs and agents as an industry to die.