On 6 August OpenAI said it is removing limits on text chats and making GPT-5.6 Luna the default model for Free and Go accounts, replacing GPT-5.5 Instant. Free and Go users also get a Think button that routes harder questions through more reasoning. Plus and Pro accounts move to an updated GPT-5.6 Sol with a slider controlling how much thinking goes into an answer. Limits on files, images, voice and image generation stay in place. The announcement arrived shortly after ChatGPT passed a billion weekly users, with unlimited text chats for free accounts scheduled to land the following week.

The number worth holding onto is not the billion. It is the split.

Two tiers, two models, two answers about you

Until now, free accounts had a ceiling. Hit it and the conversation stopped, or dropped to a weaker fallback. That ceiling quietly rationed how many people used ChatGPT for the kind of open-ended question that mentions brands: which accounting tool suits a sole trader, who does commercial kitchen servicing near Leeds, what is the difference between two CRMs. Remove the ceiling and that behaviour has nowhere to stop.

So the model answering most brand questions is now GPT-5.6 Luna, and almost nobody in marketing has tested against it. OpenAI’s own evaluation puts factual errors 62% less common in Luna than in GPT-5.5 Instant, and 68% less common in Sol. Take those as directional rather than settled, since they are internal numbers with no published methodology. But the gap between the two figures is the point. Luna is better than what free users had. It is still not the model you are testing in.

A worked example. A regional B2B supplier checks how ChatGPT describes them, using the founder’s Plus account with the thinking slider left high. The answer is detailed, cites two trade publications, mentions a certification they hold. Reassuring. A buyer on a free account asks the same thing, gets a shorter reply from a different model, and the certification does not appear because the source it lives on was not retrieved. Nothing about the company changed between those two answers. Only the tier did.

What to actually do this week

Run the check twice, from two accounts, and write down which model produced each answer. That is the whole method. It takes about twenty minutes and it is the only way to see the gap.

Three questions are usually enough: your brand name alone, your main category with a location or qualifier attached, and a comparison question naming a competitor. Ask each in a fresh conversation. Log the date, the tier, the model, and whether any link was offered. Repeat monthly rather than daily, because the answers move around enough that a single day tells you very little.

If the free-tier answer is thinner, look at what the paid answer used that the free one did not. Usually it is a source that takes more retrieval effort to reach: a PDF, a page buried three clicks deep, a claim that only exists on a third-party directory. The fix is rarely clever. It is putting the fact somewhere shallower.

What can go wrong here

Three failure modes, and the first is the common one.

Treating a handful of answers as data. You are running a sample of two on one afternoon. Chatbot output varies between sessions for reasons that have nothing to do with your website. If you rewrite a page because of one bad answer, you will be rewriting it again next month. We covered the broader version of this problem in how AI describes your brand across many queries rather than one, and the same caution applies here.

Chasing model changes as a strategy. Default models will change again. Building a workflow around Luna specifically is building on something with a short shelf life. What lasts is the habit of checking both tiers, not the model name.

Assuming unlimited means used. Removing a cap raises the ceiling on usage. It does not prove your buyers were hitting that cap in the first place. If you sell into a market that researches through trade bodies, procurement portals or a rep they have known for years, none of this may show up in your pipeline for a long time.

When this is not worth your time

If your business runs on repeat customers and referrals, and search has never driven meaningful revenue, this is a twenty minute curiosity rather than a project. The teams that should take it seriously are the ones where a stranger comparing three options is a normal way to get discovered. For everyone else, knowing the gap exists is enough for now.

The uncomfortable part of this update is not the model change. It is that the AI answer most of your market sees has always been a different answer from the one you have been reading, and the tier that produces it just got a much bigger audience. Check both. Then decide whether the difference is worth fixing.

Editor’s note: This area changes quickly, so check the latest platform policy before making compliance decisions.

The post ChatGPT’s Free Tier Just Went Unlimited. You Have Been Checking the Wrong Model. appeared first on Mark8ng.com.

Atlas launched in October 2025. It is being retired about ten months later, having never shipped beyond macOS. OpenAI has said the browsing features move into products people already use, which in practice means the ChatGPT desktop app, a Chrome integration, ChatGPT Work and Codex. Conversation history lives in your ChatGPT account and is unaffected.

The bit worth paying attention to

Losing a browser is an inconvenience. The useful question is what it says about how quickly the ground moves under AI tooling, and how much of your working process you should let sit on top of it.

Consider a three-person agency that spent a fortnight last spring building a competitive research routine inside Atlas. Tabs organised by client, agent instructions saved per project, a browsing workflow the junior strategist was trained on. None of that structure survives the shutdown. The underlying skill does, and the ChatGPT account does, but the specific arrangement of it is gone, and someone has to rebuild it in a different tool during a normal working week.

That is not an argument against adopting new AI tools quickly. Moving early is often the whole advantage. It is an argument for being deliberate about what you build on top of them.

Three questions before the next tool goes into your workflow

Can you get your work out? Not “is there an export button” but “if this closed on Sunday, what would I lose and how long would recreating it take”. Atlas users are finding out that the answer for bookmarks and saved logins is manual and fiddly.

Is this the vendor core product or a side bet? A browser was never how OpenAI makes money. Assistants, ads and enterprise seats are. Side products from companies moving this fast get folded back in, and the reported security problems around prompt injection in agentic browsing did not help the case for keeping it alive.

How many people depend on it? A tool one person uses is a preference. A tool four people were trained on is a process, and processes need somewhere stable to live. Keep the process written down somewhere the tool cannot take with it.

What can go wrong if you overcorrect

The wrong lesson here is to wait for AI tools to prove themselves for a year before touching them. That caution has its own cost, and the businesses that got useful at prompting in 2023 did it by using things that later changed shape. The risk is not adopting early. The risk is quietly making a fragile tool load-bearing without noticing.

This also may not be worth your attention at all. If you tried Atlas twice and went back to Chrome, there is nothing to do here. The people who need to act are the ones who made it their default browser, and that is a small group, which is arguably why it is closing.

Editor’s note: This area changes quickly, so check the latest platform policy before making compliance decisions.

Before Sunday, open Atlas, export your bookmarks to HTML, and write down anywhere your saved logins existed only there. Then spend twenty minutes writing your research routine into a plain document rather than a tool. The next time a product gets folded into something else, and there will be a next time, that document is the thing that survives.

The post OpenAI Is Shutting Down Its Own Browser on Sunday. Check What You Built On It. appeared first on Mark8ng.com.

On 24 July, OpenAI added a Conversions objective to ChatGPT Ads with optimised cost-per-click bidding, so campaigns push delivery toward the clicks more likely to produce a conversion while still charging on a click basis. The same release brought geographic exclusions, average daily budgets calculated over a rolling seven day window, intraday budget pacing, AppsFlyer and Adjust integrations for app installs, automatic advanced matching using hashed customer data, and asynchronous bulk campaign creation through the Ads API.

Read as a feature list, it is housekeeping. Read as a business decision, it is the moment ChatGPT Ads becomes something a finance lead can put on the same spreadsheet row as Google and Meta. That matters more than any single feature in the release.

Why your first month will flatter the channel

Here is the trap. When a new channel gains conversion tracking, the first month almost always looks excellent, and the reason is rarely channel quality.

Three things inflate early numbers. The audience self-selects, because people clicking an ad inside a ChatGPT answer in the first weeks are curious, high intent, and unusually engaged, and that population does not scale. Automatic advanced matching improves attribution, which means conversions that were already happening now get credited to the channel, so it appears to create demand it merely observed. And small budgets in thin auctions face little competition, so early cost per acquisition sits well below where it lands at ten times the spend.

None of that means the channel is bad. It means the first result you get is not the result you should plan around.

A test design that survives contact with reality

Picture a home services company spending roughly $4,000 a month on Google Ads and producing about 60 leads from it. A sensible test looks like this.

Run four weeks, not two. Optimised bidding needs conversion events to learn from, and below roughly 30 conversions in the learning window the system is guessing confidently. Budget 10 to 15 percent of existing paid spend: enough to generate signal, small enough that a bad month is survivable.

Exclude your strongest existing geography using the new geo exclusions. This is the least obvious step and the most useful one. If you let a new channel run in the area where your brand is already known, you will measure brand recall rather than incremental demand.

Then write down, before you see any data, the number you would need in order to fund a second month. This sounds trivial. It is the step most teams skip, which is why so many channel tests end in an argument about whether the result was good instead of a decision.

Compare against that threshold, not against your Google account average. Your account average includes branded search, which converts at a rate no new channel will ever match.

What can go wrong

Attribution inside a conversational surface is not the same as attribution after a search click, even when the reported metric carries the same name. Someone may ask ChatGPT about a category, see your ad, ignore it, then search for your brand by name two days later. Advanced matching may connect those events. Your Google branded campaign may also claim them. Both platforms report a conversion and you have paid twice for one customer. The fix is not clever modelling, it is a holdout region and an honest look at blended cost per acquisition across everything.

There is a second risk that has nothing to do with bidding. Product feed ads are now showing updated cards with pricing and star ratings. If your feed pricing is stale or your review counts are thin, that unit will display the weakness more prominently than a text ad ever did.

When this is not worth your time

If you generate fewer than about 30 conversions a month in total, skip this entirely. Not because ChatGPT Ads is unsuitable, but because no bidding algorithm on any platform can learn from that volume. You would be paying for automation with nothing to automate. The same logic applied when OpenAI cut its API pricing: cheaper inputs only matter once you have volume running through them.

If your sales cycle runs longer than 60 days, a four week test tells you almost nothing about revenue. Either extend the window or judge the channel on lead quality reviewed by hand rather than on conversion counts.

The number nobody checks

The metric most likely to mislead you is not cost per acquisition. It is the conversion count sitting underneath it. Everyone checks whether CPA looks acceptable, and almost nobody checks whether the system had enough events to optimise on in the first place. A campaign reporting a $22 CPA on nine conversions has proven nothing. A campaign reporting $61 on 140 conversions has proven a great deal. Look at the denominator first, every time, on every platform.

If you want the platform’s own framing before planning a test, OpenAI set out its bidding and measurement approach in its post on new ways to buy ChatGPT ads, and the settings themselves are covered in detail in this breakdown of the July release. Start with a budget you would be relaxed about losing in full, and set your success threshold before the first impression serves.

Editor’s note: This area changes quickly, so check the latest platform policy before making compliance decisions.

Update, 6 August 2026: measurement arrived faster than expected

OpenAI has integrated AppsFlyer to bring in-app measurement to ChatGPT ads, with Grubhub and roughly forty other brands running the first tests. That closes part of the gap this post described, because advertisers can now connect a ChatGPT ad to what happened inside an app rather than guessing at the far end of the funnel.

The recommendation here does not change, but the timeline does. The original argument was to wait for measurement before moving real budget, and measurement is arriving in pieces. In-app conversions are covered. Web conversions, view-through effects and anything involving a long consideration window are not, and a closed-loop number supplied by the platform selling the media still deserves a sceptical read.

If you deferred a test on measurement grounds and you have an app, this is the point to revisit it with a small budget and your own analytics running alongside.

The post ChatGPT Ads Just Got Conversion Bidding. Here Is How to Test It Without Wasting Budget. appeared first on Mark8ng.com.

For most people using AI in marketing today, this changes nothing. If your team uses ChatGPT, Jasper, Copy.ai, or any consumer-facing AI writing tool, those products have their own pricing. Your subscription did not get cheaper overnight. API pricing is what developers and technical teams pay to build directly on the model, it does not flow through to consumer products automatically.

But for teams building custom AI workflows, or thinking about it, an 80% price drop on the budget tier is worth actually doing the math on.

What the numbers mean in practice

At the old Luna pricing ($1 input, $6 output per million tokens), running 10,000 short social media posts through the API, roughly 100 tokens in, 150 tokens out per post, would cost about $1 in input and $9 in output. Around $10 total.

At the new pricing, the same run costs roughly $0.20 in input and $1.80 in output. Under $2 total.

That is not a scenario where a big team saves money on one workflow. That is a scenario where a small business or solo founder can run an automated content pipeline, email personalisation system, or product description generator at a cost that is essentially negligible.

The workflows that were economically borderline before, where API cost was close to the value generated, are now clearly viable. The ones that were already profitable just got significantly more so.

What is still expensive

Sol, OpenAI’s best-performing model, did not move. It is still $5 per million input tokens and $30 per million output tokens. For tasks that genuinely require Sol’s reasoning capability, you are paying the same as before.

This matters because the price cuts apply to models that handle simpler, higher-volume tasks well, not the one you would use for complex strategic analysis or nuanced long-form writing. Test the task on Luna first, but do not assume the cheapest model will do every job. The cost difference between Luna and Sol is significant enough that getting the choice wrong at scale adds up fast.

The other thing worth noting: OpenAI’s engineering team cited a 20% reduction in serving costs thanks to infrastructure improvements, including using Sol to rewrite their own GPU kernels. The Luna cut goes far beyond a cost pass-through, it is a strategic price decision to keep developers on OpenAI’s platform as Chinese providers and Google compete aggressively on price.

Which marketing workflows benefit most

The tasks where Luna-level models typically perform well enough include: generating product descriptions at scale, first-draft social captions, bulk email subject line testing, FAQ generation from existing documentation, and translation of standard marketing copy.

The tasks where you generally still need a better model: anything requiring nuanced brand voice, complex reasoning about strategy, sensitive communications, and long-form content where quality needs to match strong human writing.

A useful approach for teams evaluating this: pick one high-volume task you currently do manually or with an expensive tool, run 50 examples through Luna at the new pricing, and score the output quality against your standard. The economics make testing easy now, you can run a meaningful sample for less than a dollar.

The broader picture

API pricing has been falling consistently across providers. A year ago, the compute cost for processing a million tokens was several times what it is today across all models. The ceiling on what is economically viable to automate keeps rising.

The question for marketing teams is not really about price anymore. It is about which tasks are actually worth automating, which require human judgment that AI reliably gets wrong, and how to build a workflow that handles both. The decision to build on the API instead of using off-the-shelf tools is still primarily about control and capability, price is now rarely the limiting factor.

If you want to understand which AI marketing workflows are worth building custom versus buying as a tool, that is the kind of practical systems question mark8ng.ai is built to help with.

The post OpenAI Cut GPT-5.6 Luna Pricing by 80%. Here Is What That Actually Changes. appeared first on Mark8ng.com.