让我们看看过去几个月OpenAI和Anthropic的主要LLM模式公告的快速时间表。 所有提供的定价编号均为输入MTok/输出MTok/缓存读作MTok.
2026年6月26日# (英语).
OpenAI 宣布 GPT-5.6 索尔和月球。 Sol是GPT 5.5的直接继任者，价格为5美元/30美元/0.50美元，而Luna是专门为更便宜的使用案例设计的小型型号，价格为1美元/6美元/0.10美元。 他们也是宣布的Terra模型
2026年6月30日# (中文(简体) ).
Anthropic 宣布 Claude Sonnet 5,Anthropic声称更接近他们的Opus模型，但反馈褒贬不一。 值得注意的是，发布的定价为2美元/10美元/0.20美元，这最初被认为是临时性的，但将会上升。
2026年7月24日# (中文(简体) ).
Anthropic 宣布 Claude Opus 5，鉴于Opus 4.5对代理编码上升的影响，它是一个期待已久的大版本更新，价格为5美元/25美元/0.50美元(类似于GPT-5.6 Sol). 虽然提供的一些基准测试结果令人惊讶地超过了大得多的Fable 5的结果，但真实世界的反馈却比较好坏参半。
2026年7月30日# (中文(简体) ).
OpenAI 宣布表示，他们将GPT-5.6 Luna的价格削减了80%(??)到0.20美元/1.20美元/0.02美元。
2026年8月10日# (中文(简体) ).
Anthropic 宣布临时Sonnet 5对发布的减价现在是永久的。
2026年8月21日#
OpenAI 宣布表示，他们在未来三个月中将GPT-5.6 Sol的价格削减~20%(4美元/20美元/0.40美元).
2026年9月1日# (中文(简体) ).
Anthropic 宣布 Fable 5.1，价格为10美元/50美元/0.25美元，除此之外，自6月起，在所有地区的基准测试性能都比Fable 5要好10美元/50美元/1美元，将缓存读取成本削减75%.
当使用 agents 编码时，缓存读取成本是全部代号支出的绝大多数(96% - 99%)，因为缓存是包含之前整个对话的每个回合。 此外，缓存往往受到大约90-100%时间的冲击(LLM推论提供商的基础设施造成的波动)。 总而言之，缓存读取成本减少75%与使用该模型的成本减少70-75%相关，这显然值得注意，即使缓存成本通常是一个脚注。
2026年9月3日# (中文(简体) ).
OpenAI 宣布 GPT-6 Astra，其大小与Fable 5.1类似，其定价类似，为10美元/50美元/1美元，但没有较低的缓存读取成本。 Astra的模型性能的确与Fable相当。
2026年9月22日# (中文(简体) ).
OpenAI Q Q Q 与临时贴现的GPT-5.6 Sol相比，以2美元/10美元/0.20美元的惊人价格解决，将投入和产出价格减半！它们同时是发布 GPT-6 Luna，也以上一个版本的输入和输出价格(0.10美元/0.50美元/0.01美元)的一半。
同一天，Anthropic 宣布 Claude Opus 5.5明确表明，" Opus 5.5要求服务比Opus 5少计算，其定价反映了这一点 "。 其总价为4美元/20美元/0.20美元，比Opus 5的价格低得多，因为投入/产出成本削减了20%，缓存读数减少更多，结果一个模型的成本削减了40-60 %，甚至比Fable 5.1更好(我有很多关于Opus 5.5的话要说，但这是另一个职位的)。
2026年9月29日# (英语).
结果发现GPT-6 Sol是哑弹性能，因此一周后OpenAI 宣布 GPT-6.1 Sol的性能与Astra(在个人使用后可以确认)相当，但OpenAI随意将缓存价格减半(2美元/10美元/0.10美元)，与Anthropic5%的输入缓存读取成本相匹配，使得这个模型家族累计降低75%的价格。
总的来说，从6月发布GPT-5.6模式家庭到现在，使用这些受欢迎的LLMs的成本已经暴涨，这不是一年前我所期望的。 这些价格削减也适用于来自订阅代理机的模型使用，如Codex和Claude Code. 更重要的是，这些模型在产出质量方面似乎没有削减任何角，以达到其削减的价格。
从上面的时间线来看，Anthropic和OpenAI之间有明显的"对奶"，可以合理地假设这些来自OpenAI和Anthropic的特定价格削减是相互反应的。 然而，除非存在某种外在作用，否则没有人削减的价格为80%. 那又怎样？
LLMs的行为经济学和竞争者定价压力#
如果你用新的LLMs作为发布测试了agents，那么除了主观的氛围之外，很难区分它们。 自去年11月发布代理 LLMs 的超增殖以来，模型就变成了一种商品，在你的编码库上尝试一种新模型是微不足道的，此外，有许多竞争者——不仅仅是Google和Meta等大公司——能够提供足够替代品的模型，从而允许价格压力。
对于开放重量模型，让我们再看看DeepSeek Flash V4.1，它也是最近更新的发布的1017变体。
最初的DeepSeek V4 Flash 0420是2026年4月的发布，价格为0.14 / 0.28 / 0.0028美元，并因此出现巨型病毒，尤其是OpenAI当时没有用于简单任务的小型型号。 这0.0028美元是关键，因为2%的缓存读数比例大大低于以上OpenAI和Anthropic模型的5%-10%，这使得有效成本甚至低于所公布的成本。 我怀疑7月的八成GPT-5.6卢纳减价计划是为了与这些更小的露天型号竞争现在，新的DeepSeek Flash V4.1 1017型机车的性能大大优于DeepSeek Flash V4 基准测试-wise(人工分析指数上45架对24架),DeepSeek的成本仍然与0.15美元/0.60美元/0.003美元相仿。 注意“来自DeepSeek”等字。
在OpenRouter上，任何推论提供商都可以列出他们主机的模型，当一个用户达到的OpenRouter API时，请求被引导到其中一个模型上——它默认被引导到任何可用的模型上以进行负载平衡，但开发商可以在API请求中指定"最低价"等偏好。 早在5月，我注意到有13个不同的供应商在OpenRouter上托管DeepSeek V4 Flash。 就更新后的1017个变体而言，目前有29个不同的供应商。
然而，审视有效的输入成本(其中考虑到缓存读取成本与缓存命中率之间的相互作用)，许多不同的提供者是接近成本的。
OpenAI和Anthropic必须区别于这些具有较高质量的开放式重量模型——它们目前在这方面做得非常好，特别是在Opus 5.5方面——但他们需要继续这样做，这就是为什么数十亿美元正在投入研发。 开放量级模型追赶速度很快：就在几天前，Moonshot AI 宣布 Kimi K3.1 Pro的性能与Opus 5.5(Kimi at 55 vs. Opus 5.5 at 58)相当，以输入/输出价格的一半和相同的缓存读数成本(2/10/0.20美元). 它与Kimi K3相同大小和架构，目前由超过20家不同的推论提供商主机：一旦重量在11月4日下降，值得注意的是，Moonshot AI本身正以低于原Kimi K3的价格(3美元/15美元/0.30美元)加入削减价格的乐趣和定价的Kimi K3.1 Pro.
发声AI贬低者反复出现的一个趋势是，令牌价格被人为地压低，将人们拖入LLM生态系统，但随后价格将上涨到“令牌的真正成本”，而当免费乘车结束后，AI助推器就会被破坏。 该理论认为OpenAI和Anthropic与2010年代Uber和Lyft对乘务共享行业所做的一样，都是亏损领先，但与当时不同的是，OpenAI和Anthropic并不是一个拥有在生态系统中设定价格的唯一权力的双垄断者，而推论行业进入的壁垒并不那么高。 只要这些众多推论提供商能够继续以与OpenAI和Anthropic相同的价格和类似质量为模型服务，并且切换成本可以忽略不计，那么价格可能会继续下降更多。 这两家公司都试图建造护城河来抵御这一趋势，例如专注于企业承诺和个人代理助理，如OpenAI Dots和Claude Dut.
象征物价突然上涨的想法更是一种幻想，没有行为经济学和LLM物价的实际趋势在过去几个月里为更好的模型大幅下降的支持，而不仅仅是来自OpenAI和Anthropic. 只要规模商能以每桶成本低于每桶收入的价格定价，那么这些推论提供者就会降低象征性成本，互相竞争，以至于OpenAI/Anthropic也不得不降低价格进行竞争。 OpenRouter上的29家独立供应商几乎没有为无利可图的模型服务的积极性(他们只是停止为模型服务，而是将计算能力用于有利可图的模型)，而“某物的真正成本”可能最终成为价格等于边际成本的地方。
在这点上，OpenAI和Anthropic的确都接近IPOing，融资进一步质疑价格削减。
IPO 泵 #
这些价格的时机削减是值得注意的，因为OpenAI和Anthropic都传闻很快——2027年的OpenAI和几个月之内的Anthropic. 到目前为止，来自OpenAI和Anthropic的泄露财务都表明，毫不奇怪，由于研发/规模化，它们正在损失数十亿美元，尽管在这两种情况下，这些报告都未说明过去几个月中LLM的代理用途过度扩大。 投资者将仔细检查巨大的负面利润，并要求他们有计划扭转局面。
那么，他们为什么将主要收入来源削减高达80%？这和你想在S-1计划书上表现的正好相反。 推论中的利润率本身可能讲述一个不同的故事，有些报道说，利润率高达80%，有警告。 在这些主要的削减之后，他们有多少利润率？
削减的价格确实影响了我如何花费在LLM agents上，但并没有影响我期望OpenAI/Anthropic希望他们的典型客户的行为方式。 GPT-5.6 Sol是顶级型号时，我曾经每月有100美元订阅Codex(5x Pro计划)，我设法完全用尽订阅中包含的所有用法，一旦GPT-6.1 Sol 发布和我注意到使用它的成本实际上削减了75%，你知道我做了什么吗？
我将最大使用配额削减到1/5 之后得到4x 更高效使用同一型号，所以它最终大致中立和明显的选择。
公平而言，Claude Code和Codex等订阅的利润率不确定，因为它们相对于API的使用值巨大，如果出现资本紧缩，我怀疑订阅值会在模型基价之前被削减。 例如，同时GPT-6.1 Sol是宣布，OpenAI在 " 反映我们日益高效的模型 " 的论点下，将Codex中200美元/月的用量允许削减50%，这在技术上是正确的，但写作很有趣。
我将非常好奇地看到Anthropic IPO Projectus和推论利润率，一旦它很快公开。
如果它们有意淘汰更昂贵的模型，在一个月前的Opus 5.5 发布之后
我完全期望有一条Fable 5.2/GPT-6.1 政变释放的阿斯特拉
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
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%.
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 (I have quite a lot of things to say about Opus 5.5, but that’s for another post).
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 (45 on the Artificial Analysis index vs. 24) and the cost from DeepSeek remains similar at $0.15 / $0.60 / $0.003. Note the operative words “from DeepSeek”.
On OpenRouter, any inference provider can list a model that they host, and when a user hits the OpenRouter API, the request is routed to one of those models—it’s routed to any available model by default for load balancing but the developer can specify preferences such as “lowest price” in the API request. Back in May, I noted that there were 13 distinct providers hosting DeepSeek V4 Flash on OpenRouter. For the updated 1017 variant, there are now 29 distinct providers.
However, looking at the effective input costs (which account for the interaction between cache read costs and cache hit rates), many distinct providers are close costwise.
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). It’s the same size and architecture as Kimi K3 which is currently being hosted by over 20 distinct inference providers: 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. Notably, Moonshot AI itself is joining the price cut fun and priced Kimi K3.1 Pro at less than the original Kimi K3 ($3 / $15 / $0.30).
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 just like what Uber and Lyft did to the ridesharing industry in the 2010s, but unlike then, 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. It’s no surprise both companies are trying to build moats to defend against this trend, such as a focus on enterprise commitments and personal agentic assistants like OpenAI Dots and Claude Duet.
The idea that token prices will suddenly rise is more a fantasy that is not supported by both behavioral economics and the actual trends of LLM prices going down significantly over the last few months for better models, and not just from OpenAI and Anthropic. As long as scalers can price at a cost-per-token lower than the revenue-per-token, then those inference providers will reduce token costs to compete with each other to a point that OpenAI/Anthropic will have to lower their prices to compete too. The 29 distinct providers on OpenRouter have little incentive to serve an unprofitable model (they would just stop serving it and use the compute capacity for a profitable model instead) and “the true cost of a token” may end up being where price equals marginal cost.
On that note, both OpenAI and Anthropic are indeed close to IPOing, which raises further questions about the price cuts.
IPO Pumping#
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?
Speaking personally, the price cuts have indeed affected how I spend on LLM agents, but not in the way I expect OpenAI/Anthropic want their typical customer to behave. I used to have a $100/month subscription to Codex (the 5x Pro plan) when GPT-5.6 Sol was the top model, and I managed to just fully exhaust all the usage included in the subscription despite getting annoyed with random usage resets. Once GPT-6.1 Sol released and I noticed that the cost of using it was effectively cut by 75%, you know what I did?
I cut the max usage quota to 1/5th after getting 4x more efficient usage of the same model, so it ends up roughly neutral and the obvious choice.
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”—which is technically correct but it’s funny to put in writing.
I will be very curious to see the Anthropic IPO prospectus and the inference profit margins once it becomes public soon.
I also question whether it’s good for OpenAI/Anthropic if they intentionally obsolete their more expensive models. After Opus 5.5 released a month ago, there hasn’t been any discussion about either Fable 5.1 or GPT-6 Astra because Opus 5.5 is just that good.
I fully expect a Fable 5.2/GPT-6.1 Astra to release in a coup
Auto-fetched and translated by AI Atlas. All rights belong to the original author.