How GPT-4o selects brands differently than GPT-3.5

11 min readJuly 11, 2026By Spawned Team

GPT-4o uses multimodal reasoning and fresher training data to pick brands. Here's exactly what changed and how to rank in both models.

Two laptops on a wooden desk comparing AI model outputs side by side

TL;DR: GPT-4o picks brands using a larger context window (up to 128k tokens), training data through early 2024, multimodal reasoning, and stronger instruction-following than GPT-3.5. Structured, authoritative, long-form content is far more likely to surface in GPT-4o answers. GPT-3.5 leans harder on raw frequency of co-occurrence from pre-2021 text. The gap is wide enough to change your content strategy.

What actually changed between GPT-3.5 and GPT-4o?

GPT-3.5 and GPT-4o are not two versions of the same engine. They're built differently, and those differences decide which brands get recommended.

GPT-3.5 Turbo runs a context window of around 16k tokens in its common form. GPT-4o runs up to 128k tokens [1]. That single difference changes what the model can weigh before it answers. GPT-4o can read an entire whitepaper, a full product comparison page, or a long review thread before it picks a brand. GPT-3.5 can't hold that much at once.

The training cutoffs are years apart. GPT-4o's data runs into early 2024. The common gpt-3.5-turbo checkpoint stops at September 2021 [1]. Brands that earned authority between 2022 and 2024, through press, launches, or piled-up reviews, barely exist in GPT-3.5's world.

Third is multimodality. GPT-4o processes images, audio, and text in one pass [1]. For brand selection, that matters most where products differ visually. A query like "which running shoe has the best cushioning" can factor in product images and spec tables instead of only text frequency.

GPT-3.5 is a text-only, frequency-weighted model at its core. It surfaces the brands it saw most often in training, with little sense of whether those mentions were positive, authoritative, or relevant. GPT-4o applies stronger instruction-following and reasoning to filter for relevance and source quality [2].

How does GPT-4o decide which brands to recommend?

GPT-4o weighs source quality, contextual fit, and recency far more than GPT-3.5 ever did. That's the core shift.

Research on how factual recall works in large models found that recall degrades for entities that appear infrequently in training, and that source diversity and recency improve entity-level accuracy in GPT-4 class models [7]. GPT-4o reflects that pattern in how it picks brands.

Four signals matter more to GPT-4o than to GPT-3.5:

Entity consistency. When a brand name, its category, and specific attributes (price range, use case, company size) appear together across many independent sources, GPT-4o treats that as a stronger signal than any single page. GPT-3.5 mostly counted mentions.
Source authority. GPT-4o is more likely to pull recommendations from content that was widely linked, cited, or published in credible places (major publications, government procurement lists, analyst reports). GPT-3.5 was less picky.
Instruction alignment. GPT-4o was trained with substantially more reinforcement learning from human feedback (RLHF) than GPT-3.5 [9]. Ask for "the best CRM for a 10-person startup" and GPT-4o is more likely to name a brand that actually fits that constraint, not the most-mentioned CRM overall.
Recency. Because its training data runs later and it can browse in ChatGPT's live mode, recent press and recent structured content can move GPT-4o answers. GPT-3.5 has no path to post-2021 data without explicit tool use.

Here's the practical consequence. A brand that dominated forums and blog posts before 2022, then went quiet, holds its position in GPT-3.5 answers much better than in GPT-4o answers. The newer model has other options and it uses them.

Does GPT-4o use the internet in real time to find brands?

Sometimes. It depends on how you reach it.

In the default ChatGPT interface, GPT-4o can call a browsing tool that pulls live web content [1]. When that fires, it can surface brands from pages indexed in the past days or weeks. GPT-3.5 has no equivalent in its standard deployment.

Browsing doesn't fire on every query. OpenAI's system triggers search based on signals like recency markers in the question ("right now," "currently," "2024") and whether the training data is likely stale for the topic. For an evergreen query like "best project management software," browsing may or may not activate. For "what project management tools launched this year," it almost always does.

That creates two tracks inside GPT-4o. Track one draws from parametric memory (the training weights). Track two draws from live retrieval. Brands need to work on both. Track one is what's baked into the model. Track two is whether your content shows up in search results clearly enough for GPT-4o to parse and cite it accurately. Retrieval-augmented systems weigh document quality and relevance when they generate answers, which is the framework the browsing tool follows [8].

GPT-3.5 through the API has no browsing at all. It runs entirely on parametric memory. Simpler to reason about, and impossible to influence with new content short of a retrain.

For AI search visibility strategy, the split is stark. Content published after 2022 reaches GPT-3.5 users only if OpenAI retrains on it, which happens rarely. The same content can reach GPT-4o users through browsing much faster.

GPT-4o vs GPT-3.5: key capability differences affecting brand selection

| | | |---|---| | Context window (GPT-3.5, tokens k) | 16 | | Context window (GPT-4o, tokens k) | 128 | | Training data cutoff (GPT-3.5, year) | 2,021 | | Training data cutoff (GPT-4o, year) | 2,024 |

Source: OpenAI GPT-4 Technical Report (arXiv:2303.08774) and OpenAI documentation, 2023-2024

Why does GPT-4o cite some brands more confidently than GPT-3.5?

Calibration is the quiet difference nobody talks about. GPT-4o hedges more accurately than GPT-3.5, and that changes which brands surface.

GPT-3.5 tends to name brands with flat certainty even when its training data was sparse or contradictory. It lists five CRM tools with identical enthusiasm whether or not the evidence behind each was equal. Research on model calibration documented this overconfidence in earlier, smaller models. The study "Language Models (Mostly) Know What They Know" found that larger models are better at judging when their own answers are likely correct [4].

GPT-4o frames it differently. It's more likely to say "Salesforce is the most commonly cited option for enterprise teams, while HubSpot comes up more often for SMBs" than to present everything as equal. That framing pushes forward brands with strong, specific category associations over brands with only general name recognition.

Good news for focused brands. If you've built a clear reputation in a narrow category (inventory management for food distributors, not "supply chain software"), GPT-4o is more likely to name you for the right query even when your total mention volume trails a generalist competitor.

It's also why volume marketing loses steam here. Blanket the internet with generic content that drops your name next to every keyword, and you'll see diminishing returns in GPT-4o compared to GPT-3.5. The newer model's calibration machinery penalizes vague associations.

How does the context window difference affect brand selection in practice?

The jump from 16k to 128k tokens is not a technical footnote. It changes which documents can influence which recommendations.

With GPT-3.5's short window, content that front-loads the brand name and category terms wins. The model works from a limited slice of any document. Old SEO tactics (keyword in the first 100 words, repeat it in headers) still worked reasonably well.

GPT-4o can hold a multi-thousand-word comparison article in context and reason across all of it. A brand named prominently only in section four of a careful 5,000-word buyer's guide can still move GPT-4o's answer, as long as the surrounding content is credible and the mention is specific.

That resets what "good content" means for AI SEO. Less about keyword density near the top of a page. More about whether your brand appears with accurate, specific, appropriate detail anywhere in a document GPT-4o would trust. Models with larger context windows and stronger instruction tuning are also better at ignoring irrelevant filler in long documents when they form an answer [5].

One concrete result: technical documentation, long case studies, and detailed comparison tables on third-party review sites now beat short listicles for GPT-4o brand visibility. GPT-3.5 was fine with listicles. GPT-4o isn't.

The table below compares how document types affect brand selection across the two models, based on the architecture differences and retrieval research available as of early 2025 [5][8].

| Content type | GPT-3.5 brand visibility | GPT-4o brand visibility | |---|---|---| | Short listicle (under 500 words) | High if brand is named early | Low to medium | | Long comparison guide (2000+ words) | Medium (limited context) | High | | Structured product spec page | Medium | High | | Forum/community mention | High (volume counts) | Medium (quality-filtered) | | Third-party analyst report | Medium | High | | Brand's own website only | Medium | Low without external corroboration |

Does structured data or schema markup affect how GPT-4o finds brands?

Probably yes, but the path is indirect. That's the honest answer.

GPT-4o's parametric memory doesn't "read" schema markup the way a crawler does. The model learned from the rendered or parsed text of documents, not raw HTML. So schema won't change what's baked into the weights.

Browsing is where it counts. When GPT-4o retrieves live pages, that content gets parsed and fed into context. Schema markup improves how search engines index and rank pages. Google's own documentation states that structured data helps a page qualify for richer search features [6]. Pages with clean structured data are more likely to show up in the retrieval step that feeds GPT-4o's live answers.

The chain runs like this. Better schema, better Google indexing, higher ranking in the results GPT-4o's browsing tool pulls from, higher odds that content enters GPT-4o's context window.

For generative engine optimization, the advice is plain. Implement Product, Organization, and FAQPage schema. Not because it talks to GPT-4o directly, but because it improves your odds of being in the retrieval pool when GPT-4o goes looking.

GPT-3.5 has no browsing path, so schema does nothing for its brand selection.

How do brand mentions in forums and Reddit affect GPT-4o vs GPT-3.5?

This is where the two models split hardest, and it surprises people.

GPT-3.5 trained on a corpus heavy with Reddit, forum content, and user text. In that distribution, raw frequency of brand mentions in casual conversation was a strong signal. If your brand got mentioned positively on Reddit thousands of times before 2021, it lives in GPT-3.5's weights.

GPT-4o's training data is larger and curated differently. OpenAI hasn't published the exact composition, but its GPT-4 report describes substantially more data and more diverse sources than GPT-3.5 [2], and corpus-composition research on GPT-4 class models points to a higher share of curated web text relative to raw user-generated content [10]. The forum-frequency effect is diluted.

Here's the sharper point. GPT-4o's instruction-following and RLHF training make it apply quality filters when it generates recommendations. A brand splashed across low-information forum posts counts for less than a brand named in a few widely cited industry reports.

This doesn't make Reddit worthless. It still matters. But flooding forums with positive mentions to game recommendations works far better on GPT-3.5 than on GPT-4o. Brands that poured money into that in 2021 and 2022 are finding their GPT-4o visibility hasn't kept pace with their GPT-3.5 visibility.

Real forum content, where actual users discuss specific use cases, limitations, and comparisons, still feeds GPT-4o well. It has specificity and authenticity. Generic cheerleading doesn't.

What does GPT-4o's multimodal capability mean for brand selection?

Most brand selection still happens in text. Multimodality creates a few edge cases worth knowing.

Product images with good alt text and descriptive surrounding content now have a route into GPT-4o's reasoning that didn't exist in GPT-3.5. When GPT-4o browses a product page, it processes the image and the text together. A page that says "lightweight trail shoe, 9.2 oz, forefoot stack 24mm" next to a clear product image gets parsed more completely than one relying on text alone.

For visual categories (interior design, fashion, food, consumer electronics), GPT-4o can weigh visual brand identity signals more directly. This is emerging and the research is thin. Treat it as a reason to keep product images accurate and well-described, not as a reason to pour budget into visual SEO aimed at AI models.

Audio and video fall under GPT-4o's multimodal reach, but that content doesn't enter the model's knowledge base through training in any direct way. Text transcripts of video, if indexed, can influence training and retrieval. The video itself, without a transcript, adds little to how GPT-4o recommends brands.

The AI-powered search features that run on GPT-4o class models are starting to surface product recommendations from image searches. That's a separate pipeline from conversational brand recommendation, but the underlying model capabilities are the same.

How should I measure whether my brand visibility is better in GPT-4o or GPT-3.5?

Nobody has a perfect method yet. The measurement problem is real. But you can get useful numbers.

The most direct approach is systematic prompt testing. Run identical brand recommendation queries against GPT-3.5 Turbo and GPT-4o through the OpenAI API (temperature set to 0 for consistency), then log how often your brand appears, in what position, and with what surrounding context. Do it for 30 to 50 queries in your category. That's a rough baseline.

The catch: LLM outputs are non-deterministic even at low temperature. You need multiple runs per query for stable frequency estimates. Run each query five times and average the appearance rate.

For tracking over time, three variables carry the weight. Appearance rate (what percentage of relevant queries name you). Position (first or fifth). Sentiment framing (generic or specific, positive or neutral).

Tools built for AI visibility tracking automate this at scale. The manual version works for small categories but gets unwieldy fast once you're watching dozens of competitors across hundreds of queries.

Spawned's AI visibility audit does this kind of cross-model comparison, running your brand queries across GPT-4o, Claude, Gemini, and Perplexity to show where the gaps are and what content changes would close them. Worth running before you spend real money on content, because the answer differs by model more than most marketers expect.

For a wider view of the AI search visibility metrics that matter across every major answer engine, that's a good companion to this piece.

What content changes improve brand selection in GPT-4o specifically?

A few content moves reliably improve GPT-4o visibility in ways they don't for GPT-3.5. All flow from the architecture differences above.

Write longer, more specific comparison content. GPT-4o can use it. A 3,000-word article comparing your product to three competitors with attribute-level detail gives GPT-4o something a 400-word summary can't. GPT-3.5 barely benefits from length, because it often can't hold the whole document anyway.

Get mentioned in recent, high-authority sources. Because GPT-4o's training runs later and it can browse, a mention in a major industry publication from 2023 can move GPT-4o answers in ways it simply can't move GPT-3.5, which has no route to that content. Landing in analyst reports, major press, and credible buyer's guides published after 2022 is one of the highest-leverage moves you can make.

Build entity clarity. Make your brand name appear consistently with the same category terms, use-case descriptors, and differentiators across every major content property you own. GPT-4o's entity-consistency weighting means a brand that says the same specific things about itself everywhere beats a brand with a scattershot message.

Create genuinely quotable content. GPT-4o draws on specific phrasings from its training data more than GPT-3.5 does. If your site and external coverage contain clear, memorable statements about what your product does and for whom, those framings can show up in GPT-4o's recommendations nearly verbatim. It's the content equivalent of writing a soundbite.

For a full tactical breakdown, the generative engine optimization guide covers the content layer in depth. The AI SEO tools roundup covers the software that audits where you stand today.

Will optimizing for GPT-4o hurt your GPT-3.5 visibility?

Mostly no, with one exception.

The moves that help GPT-4o (authoritative long-form content, entity clarity, recent high-quality press) don't fight GPT-3.5 optimization. GPT-3.5 still counts brand mentions and still rewards clear category association. You're adding signals, not swapping them.

The partial exception is the forum-flooding tactic from earlier. If a brand leaned on low-quality, high-volume forum seeding as its main play, pivoting fully to authoritative long-form content might slightly trim GPT-3.5 frequency gains while lifting GPT-4o presence. That tradeoff is worth it, because GPT-4o serves the vast majority of ChatGPT users today.

As of early 2025, OpenAI reported that GPT-4o is the default model in ChatGPT across free and paid tiers [1]. GPT-3.5 is still available but no longer the default. The audience exposed to GPT-4o recommendations dwarfs the GPT-3.5 audience in production.

Optimizing for GPT-4o isn't an either/or. It's just where the audience is.

The direction of travel is one way. Future models will look more like GPT-4o than GPT-3.5. Building around quality, specificity, and authority is more than a GPT-4o tactic. It positions you for every model that comes next. The AI SEO practices that work here compound over time in a way volume tactics never will.

Sources

Frequently Asked Questions

Does GPT-4o remember my brand better than GPT-3.5 does?

"Remember" is loose here. GPT-4o has training data through early 2024 versus GPT-3.5's 2021 cutoff, so it has seen more recent brand information. Its larger context window means it can hold more brand detail in a single conversation. But neither model has persistent memory across sessions by default. GPT-4o knows more recent brands, not more brands absolutely.

Can I get my brand into GPT-4o's answers just by publishing more content on my own site?

Not reliably. GPT-4o weighs corroboration across multiple independent sources. Your own site matters, but a brand mentioned accurately and specifically across third-party publications, review sites, and analyst reports carries far more weight. Think of your site as the anchor and third-party coverage as the citations that make GPT-4o trust what the anchor says.

Is GPT-4o more or less likely to mention small or niche brands compared to GPT-3.5?

Slightly more likely, under the right conditions. GPT-4o's stronger instruction-following means it tries harder to match a brand to specific query constraints. A niche brand with a well-documented, specific reputation in its category can outrank a well-known generalist if the query is specific enough. GPT-3.5 tended to default to the most-mentioned brand regardless of fit.

How often does OpenAI retrain GPT-3.5, and could my brand get picked up in a new checkpoint?

OpenAI doesn't publish a retraining schedule for GPT-3.5 and hasn't announced a new checkpoint since it began prioritizing GPT-4 class models. As of mid-2024, gpt-3.5-turbo in the API uses a late-2021 knowledge cutoff. There's no reliable path to getting new content into GPT-3.5's weights without a retrain that OpenAI has given no sign of planning.

Does GPT-4o with browsing enabled change brand recommendations compared to GPT-4o without browsing?

Yes, meaningfully. When browsing fires, GPT-4o can surface brands that launched or gained authority after its training cutoff. Without browsing, it's limited to parametric memory. The delta is largest in fast-moving categories like software, AI tools, and consumer electronics where the landscape shifts year over year. In stable categories, the gap is smaller.

How does GPT-4o handle contradictory brand information across sources?

Better than GPT-3.5, but still imperfectly. GPT-4o tends to flag contradictions or hedge when sources conflict. GPT-3.5 often picks the more frequently stated claim regardless of quality. For brands with mixed reviews or contested claims, GPT-4o is more likely to present a nuanced framing than a clean recommendation, which can be a headache for reputation management.

What's the fastest way to improve my brand's GPT-4o recommendation rate?

Get mentioned accurately and specifically in authoritative third-party sources published after 2022. One well-cited analyst report or major publication article does more for GPT-4o visibility than dozens of low-authority blog posts. Pair that with entity clarity on your own site: a consistent, specific description of what you do, for whom, and why it matters, repeated across all your content properties.

Do negative brand mentions in GPT-4o's training data hurt recommendation rates?

Yes. GPT-4o's stronger reasoning and calibration make it more sensitive to negative sentiment in authoritative sources than GPT-3.5. A major critical review in a high-authority publication can noticeably suppress GPT-4o's recommendation confidence for a brand. GPT-3.5 averages across sentiment less carefully. Managing your presence in high-authority editorial content matters more with GPT-4o.

Is there a difference in how GPT-4o recommends B2B brands versus consumer brands?

The mechanism is the same, but the source pool differs. B2B brand authority tends to come from analyst reports, trade publications, and integration documentation. Consumer brand authority comes from review sites, major media, and retail platforms. GPT-4o pulls from wherever the credible sources are. B2B brands with no analyst coverage are more likely to be invisible in GPT-4o answers than in GPT-3.5 ones.

Can paying for API access to GPT-4o tell me how my brand scores relative to competitors?

Indirectly. Run systematic prompt tests through the API with temperature set to 0, querying "what are the best options for [category]" across many variations, and log which brands appear and how often. It takes time and some scripting, but it gives you a rough competitive benchmark. There's no built-in brand ranking score in the API itself.

Does GPT-4o cite sources when it recommends brands, and does that affect which brands it picks?

In browsing mode, GPT-4o often cites sources, and that creates a feedback loop: it tends to recommend brands that appear in sources it considers citable. In non-browsing mode, it doesn't cite sources but still applies similar internal quality filters. Brands in clearly citable, authoritative content are implicitly favored even when no visible citation appears.

How does GPT-4o's training data composition differ from GPT-3.5's in ways that affect brands?

OpenAI hasn't published detailed corpus breakdowns, but the GPT-4 technical report notes substantially more data and more diverse sources than GPT-3.5 [2]. Research on similar models points to a higher share of high-quality web, academic, and professional text relative to raw user-generated content [10]. That shifts brand selection away from forum-frequency signals toward authority and specificity.

Should I optimize my content for GPT-4o and ignore GPT-3.5 entirely?

GPT-4o is the default model in ChatGPT as of early 2025, serving the vast majority of users. Optimizing for GPT-4o doesn't conflict with GPT-3.5 visibility in most cases. High-quality, specific, authoritative content helps both. The only tradeoff is if you've relied on high-volume low-quality tactics that game GPT-3.5's frequency weighting. Those are worth phasing out regardless of model.

Do GPT-4o's brand recommendations change if I ask the same question multiple times?

Yes, with variation. LLMs are non-deterministic even at low temperature. Running the same brand query ten times may produce different brand orderings or inclusions. That's why measuring visibility requires averaging across multiple runs, not drawing conclusions from a single query. At temperature 0 the variance is lower but not zero.

how chatgpt gpt 4o selects

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