LLM API Pricing Fell Up to 80%: Why, and How to Pay Less

If you build on ChatGPT or Claude, or use apps that do, the meter just slowed down. LLM API pricing, the per-token rates developers pay to send text to these models, fell hard between June and late September 2026. OpenAI's smallest model got 80% cheaper in one cut, and both OpenAI and Anthropic shipped new flagship models that cost less than the ones they replaced.

The catch is in the fine print. That headline 80% applied to one small model, not the whole lineup. The reasons behind the cuts also say a lot about who holds the power in this market now.

By the end you will know what changed, what a dollar buys today, the four forces pushing prices down, and a short checklist for paying less without settling for a worse model.

What actually got cheaper since June?

First, how to read LLM API pricing. Providers quote prices in dollars per million tokens, in three parts.

Input is what you send (your question, a document, some code). Output is what the model writes back. Cache read is a discounted rate for input the provider has already processed recently, such as a long set of instructions you resend with every request. A token is a small chunk of text, often a piece of a word.

Here is the sequence, drawn from a detailed price timeline published on minimaxir.com and Reuters' report on OpenAI's July cut:

• June 26: OpenAI launched GPT-5.6 Sol at $5 input, $30 output and $0.50 cache read, the same as GPT-5.5. Its small sibling, GPT-5.6 Luna, came in at $1, $6 and $0.10.
• June 30: Anthropic launched Claude Sonnet 5 at $2, $10 and $0.20, first described as temporary launch pricing. On August 10 it made that price permanent.
• July 30: OpenAI cut Luna by 80%, to $0.20, $1.20 and $0.02. The mid-tier Terra model dropped less: input from $2.50 to $2, output from $15 to $12. The flagship Sol did not move.
• September 1: Anthropic released Fable 5.1 at $10, $50 and $0.25, and cut its cache read price by 75%.
• September 22: OpenAI released GPT-6 Sol at $2, $10 and $0.20, plus GPT-6 Luna at $0.10, $0.50 and $0.01. The same day Anthropic released Claude Opus 5.5 at $4, $20 and $0.20, down from Opus 5's $5, $25 and $0.50.
• September 29: OpenAI followed with GPT-6.1 Sol at $2, $10 and $0.10, halving the cache read price again.

So the flagship story is real too, just slower. OpenAI's top model went from $30 per million output tokens in June to $10 by the end of September. The timeline's author calls the Sol family 75% cheaper cumulatively, and puts Opus 5.5's total cost reduction against Opus 5 at 40% to 60%.

LLM API price comparison: what does $1 buy now?

Price lists are hard to feel. A simpler view is to divide a dollar by the rate. The table works as a quick LLM API cost comparison, collecting the list prices from the timeline above, all per million tokens.

Now the division. In June, $1 of output from OpenAI's small model bought you about 167,000 tokens. At GPT-6 Luna's $0.50, the same dollar buys 2 million. That is twelve times more writing for the same money in under three months.

At the top end, $1 of Opus 5.5 output buys 50,000 tokens. Same dollar on GPT-6.1 Sol buys 100,000. Neither is wrong to pay for. They are different tools, and the gap between "small" and "flagship" is now far wider than the gap between the two big labs.

Why is LLM pricing dropping so fast?

Four forces show up in the sources. None of them is charity.

1. Models got cheaper to run. OpenAI told Reuters the lower prices were partly enabled by efficiency gains from GPT-5.6, including improved code and performance optimization during internal development. Anthropic was blunter about Opus 5.5, saying it "requires less compute to serve than Opus 5, and its pricing reflects that." When serving a request costs less in chips and electricity, a lab can cut the price and keep its margin. (For one concrete example of how that kind of saving happens, see how DeepSeek cut AI server costs by 80%.)

2. Open-weight rivals set a low ceiling. Reuters reports that open-source Chinese models such as Z.ai's GLM-5.2 nearly match US performance at lower cost. DeepSeek V4 Flash, released in April, lists at $0.14 input and $0.28 output. The timeline's author also claims Kimi K3.1 Pro charges about half of Opus 5.5's input and output price at similar benchmark scores. Every one of those is a price a buyer can point to in a negotiation.

3. Companies started reading their AI bills. The same Reuters report says businesses facing high AI costs are scrutinizing what they spend. The first wave of AI projects was often funded as experiments. Once an app is running in production and sending millions of requests, the finance team notices, and a cheaper model becomes a line item, not a nice-to-have.

4. Switching is easy. DeepSeek V4.1 Flash is hosted by 29 providers on OpenRouter, up from 13 for V4 Flash in May, according to the timeline. When the same model is sold by dozens of hosts, and most apps reach LLM APIs through a simple call, moving to a cheaper option can take an afternoon.

What about the rumour that OpenAI is cutting prices to look good ahead of an IPO? The author mentions the IPO talk but argues price cuts would look counterproductive for IPO financials. No company has given that as a reason.

Is Claude cheaper than GPT?

At list price, mostly no, but it depends on which tier you compare.

At the flagship level, GPT-6.1 Sol costs $2, $10 and $0.10. Claude Opus 5.5 costs $4, $20 and $0.20. On all three numbers, Opus is exactly double. Anthropic's pricier Fable 5.1, at $10 input and $50 output, sits in a bracket of its own.

One tier down, the picture evens out. Claude Sonnet 5 at $2, $10 and $0.20 matches GPT-6 Sol's launch price on every line. Against GPT-6.1 Sol it matches on input and output, but pays double for cached reads.

At the cheap end, the timeline does not report a new small Claude price, so the race for the cheapest LLM API is between OpenAI's Luna and open-weight models. GPT-6 Luna, at $0.10 input, now undercuts DeepSeek V4 Flash's $0.14 on input, while DeepSeek stays cheaper on output ($0.28 against $0.50) and on cache reads.

Two cautions. List price is not cost per task: a model that solves your problem in one try can beat a cheaper one that needs three attempts. And benchmark claims like "similar performance" are averages, so test on your own work before you switch.

What is a cache read price and why does it matter?

Many AI apps send the same long block of text over and over. A customer support bot resends its rulebook with every message. A coding assistant resends large chunks of your project. A cache lets the provider remember that repeated part, and charge you a much lower rate to read it again.

This is where some of the quietest cuts in LLM API pricing landed. Anthropic cut Fable 5.1's cache read price by 75%. Opus 5.5's cache read fell from $0.50 to $0.20. GPT-6.1 Sol's dropped to $0.10, which is one twentieth of its $2 input price.

For a chatbot that is mostly repeated instructions, the cache read price can matter more than the headline input rate. Two models with the same input price can produce very different bills once caching is counted. Opus 5.5 shows it: its cache read price fell 60% while input and output fell 20%, so a cache-heavy workload saves more.

What this means for builders and everyday users

If you pay for an API, the practical moves are simple:

• Start with the cheapest tier that could work. Try a small model like Luna or an open-weight option first. Move up only when it fails on real examples from your app.
• Design for the cache. Put the parts of your prompt that never change (instructions, reference material) at the start, and the parts that change at the end, so repeated text can be served at cache read rates.
• Watch output, not just input. Output costs five times input on GPT-6.1 Sol and Opus 5.5. Asking for shorter answers is often the cheapest optimization you have.
• Re-check LLM API pricing every quarter. These two companies announced new prices more than six times between June and September. Sonnet 5's "temporary" launch price became permanent. Last spring's comparison spreadsheet is already wrong.
• Keep switching cheap. Route model calls through one place in your code, so moving to the cheapest LLM API provider is a config change instead of a rewrite.

If you just use AI apps, the effect is indirect but real. The companies behind your writing tools, coding helpers and chat apps now pay far less per answer. Whether that shows up as lower subscription prices, bigger usage limits or fatter margins is each company's choice.

What to watch next in LLM API pricing

Three things will tell you whether this keeps going. First, whether Anthropic answers OpenAI's Luna cuts at the small-model tier. Second, whether flagship prices keep falling now that GPT-6.1 Sol and Opus 5.5 have set a new floor. Third, how many hosts pick up the next open-weight release, since that number is a good proxy for how hard the price pressure will be.

Efficiency research keeps pushing in the same direction, including ideas like 1-bit models that swap hard math for simple addition. Nobody can promise the cuts continue, so build as if LLM API pricing will keep moving.