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.

August 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 costs

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.

September 22, 2026

OpenAI announced GPT-6 Sol at at a shocking price of $2 / $10 / $0.20 that halves the input and output price relative to the temporarily-discounted GPT-5.6 Sol! They simultaneously released GPT-6 Luna also at half the input and output price ($0.10 / $0.50 / $0.01) of the previous version.

On the same exact day, Anthropic announced Claude Opus 5.5, where it’s explicitly stated “Opus 5.5 requires less compute to serve than Opus 5, and its pricing reflects that”. Its price of $4 / $20 / $0.20 is much lower in total than Opus 5’s due to a 20% input/output cost cut and a larger cache read cut, resulting in a total cost reduction of 40-60% for a model that’s even better than Fable 5.1.

September 29, 2026

It turned out that GPT-6 Sol was a dud performancewise, so a week later OpenAI announced GPT-6.1 Sol, which surprisingly has comparable performance to Astra (can confirm after personal use), but OpenAI casually cut the cache price in half ($2 / $10 / $0.10) matching Anthropic’s 5%-of-input cache read cost, making this model family cumulatively 75% cheaper.

Overall, starting from the release of the GPT-5.6 model families in June to now, the cost of using these popular LLMs has cratered, which is not something I would have expected a year ago. These price cuts also apply to model usage from subscription agentic harnesses such as Codex and Claude Code. More importantly, these models do not appear to have cut any corners in terms of output quality to achieve their price cuts.

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?

The Behavioral Economics of LLMs and Competitor Pricing Pressure

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.

For open weight models, let’s take another look at DeepSeek Flash V4.1, which also recently released an updated 1017 variant.

The original DeepSeek V4 Flash 0420 was released in April 2026 at a price of $0.14 / $0.28 / $0.0028 and went megaviral as a result, especially as OpenAI didn’t have a small-class model for simple tasks at the time. That $0.0028 turned out to be the key, as a cache read proportion of 2% is substantially lower than the 5%-10% of the OpenAI and Anthropic models above, which makes the effective cost even lower than what is advertised. I suspect the 80% GPT-5.6 Luna price cut in July was meant to specifically compete with these smaller open-weight models. Now, the new DeepSeek Flash V4.1 1017 model performs significantly better than DeepSeek Flash V4 benchmark-wise and the cost from DeepSeek remains similar at $0.15 / $0.60 / $0.003.

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 at half the input/output price and the same cache read cost. Once the weights drop on November 4th, you can therefore expect most of them to support the new Kimi K3.1 Pro model at a roughly equivalent price.

One recurring trend among vocal AI detractors is that the price of tokens has been kept artificially low to hook people into the LLM ecosystem, but then the prices will increase to “the true cost of a token” and the AI boosters will be screwed once the free ride ends. The theory is that OpenAI and Anthropic are loss-leading, but unlike the ridesharing industry, OpenAI and Anthropic are not a duopoly who have the sole power to set prices in the ecosystem and the barrier to entry for the inference industry is not as high. As long as these many inference providers can continue to serve models at a similar price and a similar quality to what OpenAI and Anthropic can provide, and switching cost is negligible, then the prices may continue to decrease more.

IPO Pumping

The timing of these price cuts is notable because both OpenAI and Anthropic are rumored to be IPOing soon. So far, leaked financials from both OpenAI and Anthropic show unsurprisingly that they are losing billions due to R&D/scaling. 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.

In fairness, the profit margins for subscriptions such as Claude Code and Codex are uncertain given their immense value relative to the API usage, and if there are capital crunches I suspect subscription value will be cut before the base price of the models. For example, at the same time GPT-6.1 Sol was announced, OpenAI cut the usage allowance by 50% for the $200/month tier in Codex under the argument “to reflect our increasingly efficient models”.

Another less-cynical factor that could explain the price cuts is that LLM infrastructure has improved over the past few months. 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.