A SandsDX original data report, the second in the series. 8 frontier models from 5 AI labs, 26 B2B SaaS categories, 1,328 API runs, July 2026. Methodology at the end; raw data available on request.
In Wave 1 we measured what one model release did to AI shortlists inside a single lab: identical buyer questions through Claude Fable 5 and its predecessor. The obvious objection: maybe those results were specific to Claude.
So we widened the lens. Same buyer question, 26 categories, five runs each, through eight models from five labs: Claude Fable 5 and Opus 4.8 from Anthropic, GPT-5.5 and GPT-5.3-chat from OpenAI, Gemini 3.1 Pro and 3.5 Flash from Google, Grok 4.3 from xAI, and Perplexity Sonar Pro as the search-grounded control. Every list at a depth of ten vendors. 1,328 runs, zero refusals.
The results were not specific to Claude. The full field says the same things, and it says more.
Executive summary
- The frontier is converging on one map of B2B software. Claude Fable 5’s shortlists overlap GPT-5.5’s at 83%, more than they overlap the previous Claude at 76%. Work that moves your position in the shared corpus moves it in every model trained on it.
- Half the categories have no agreed leader. 13 of 25 return the same number one company from every weights-based model. The other 12 split, with AI SDR splitting five ways across the field.
- Four companies hold 18% of the shortlist economy. Salesforce and Microsoft hold 42 top-five consensus slots each, HubSpot 36, Adobe 34, out of 875 measured. The next tier starts at ZoomInfo with 23.
- Consistency varies by lab. Claude’s repeated answers overlap 92% at the company level, the highest of the eight models. The consumer tiers scatter to 73% at the name level; one query in one chat window is noise.
- Live search is the side door for young vendors. The search-grounded model overlaps the weights consensus only 71% and recommends companies no weights-only model mentions. Citations lead the corpus.
- Our own Wave 1 replicates, with one correction. The membership churn reproduces almost exactly. The single-slot crown flips are sensitive to list depth, and we now hold them to a stricter test.
Finding 1: The new Claude agrees with the new GPT more than with the old Claude
The single number worth this whole report: Claude Fable 5’s top-five shortlists overlap GPT-5.5’s at 83%, measured at the company level across 25 categories. The same Claude overlaps its own predecessor, Opus 4.8, at 76%.
Two models from competing labs, trained by different teams on different infrastructure, agree with each other more than two generations of the same product line agree with themselves.
The explanation is the training data. Every frontier lab is distilling roughly the same internet, and the newest models have distilled the most recent, most overlapping version of it. Each release is a fresh snapshot of a shared corpus, so the newest models converge. The market’s answer to “who are the best vendors” is becoming a single map that every frontier AI reads from.
For anyone who runs revenue, this converts a threat into an economy of scale. You do not need a ChatGPT strategy and a separate Claude strategy and a Gemini strategy. The work that moves your position in the shared corpus moves it in every model that trains on it. One campaign, every answer engine.
Finding 2: Half the categories have no agreed leader
Across the 25 measured categories, 13 returned the same number one vendor from all seven weights-based models. These crowns are settled:
| Category | Every weights model says |
|---|---|
| CRM | Salesforce |
| HR management (HCM) | Workday |
| Business intelligence | Microsoft (Power BI) |
| Cloud ERP | Oracle |
| Video conferencing | Zoom |
| E-commerce platforms | Shopify |
| Electronic signature | DocuSign |
| Sales engagement | Outreach |
| Revenue intelligence | Gong |
| Conversation intelligence | Gong |
| Product analytics | Amplitude |
| B2B sales intelligence and contact data | ZoomInfo |
| AI meeting notetakers | Otter.ai |
The search-grounded model concurs in 11 of the 13; its two dissents, Rippling over Workday and Fireflies over Otter, are younger challengers with strong live-web footprints, which previews Finding 5.
The other 12 categories have no agreed leader. This is who each model names first:
| Category | Leading answer | Also named first |
|---|---|---|
| Marketing automation | HubSpot (Fable 5, GPT-5.5, Gemini Pro, Grok) | Salesforce (GPT-5.3, Gemini Flash); Adobe (Opus 4.8) |
| Help desk | Zendesk (6 of 7) | Freshworks (Gemini Flash) |
| Project management | Asana (5 of 7) | monday.com (Gemini Pro); Atlassian (Grok) |
| Endpoint security | CrowdStrike (6 of 7) | Bitdefender (Gemini Flash) |
| Identity security | Okta and Microsoft Entra (3 each) | Ping Identity (Gemini Flash) |
| Conversational marketing | Drift (6 of 7) | Intercom (GPT-5.3) |
| ABM platforms | 6sense (6 of 7) | Demandbase (GPT-5.3) |
| AI SDR | 11x (GPT-5.5, GPT-5.3, Gemini Pro) | Artisan (both Claudes); Regie.ai (Gemini Flash); Apollo.io (Grok) |
| Customer success | Totango (5 of 7) | Gainsight (GPT-5.5, Grok) |
| Sales enablement | Highspot (5 of 7) | Seismic (GPT-5.3, Gemini Pro) |
| Customer data platforms | Twilio Segment (6 of 7) | Adobe (GPT-5.3) |
| Marketing attribution | Dreamdata (4 of 7) | Adobe (Opus 4.8, Gemini Flash, Grok) |
Add the grounded model and AI SDR splits five ways: Perplexity names Qualified. Note who the lone dissenters are. Gemini 3.5 Flash, the free Gemini tier, names a leader no other model names in four categories. GPT-5.3-chat, the model closest to the free ChatGPT tier, does it in three. The consumer-tier models are reading a different map than the frontier ones, and most buyers are on the consumer tier.
The pattern from Wave 1 holds at the cross-provider level: the oldest, largest categories are frozen and the young ones are liquid. What is new is the strategic reading. In a unanimous category, the number one slot is unavailable and the fight is for slots two through five. In a contested category, the crown itself is undecided across the assistants your buyers use, and the vendor who consolidates it first gets recommended by all of them.
Finding 3: Four companies hold 18% of every shortlist
We counted every top-five consensus slot across 25 categories and the 7 weights-based models: 875 slots in total, 802 distinct vendor names in the raw data competing for them.
| Company | Top-5 slots (of 875) | Share |
|---|---|---|
| Salesforce | 42 | 4.8% |
| Microsoft | 42 | 4.8% |
| HubSpot | 36 | 4.1% |
| Adobe | 34 | 3.9% |
| ZoomInfo | 23 | 2.6% |
| SAP | 19 | 2.2% |
| Gong | 19 | 2.2% |
| Salesloft | 18 | 2.1% |
| Outreach | 17 | 1.9% |
| Oracle | 16 | 1.8% |
| Apollo.io | 16 | 1.8% |
| 15 | 1.7% | |
| Clari | 15 | 1.7% |
Four companies at the top, 154 slots, 18% of all the shortlist real estate the models hand out. The next tier starts at ZoomInfo with 23, and most companies in the dataset hold one or two.
This is what platform gravity looks like inside a language model. A company with products in ten categories gets recalled in ten categories, and each mention reinforces the entity. If you compete against one of the big four in your category, the AI already knows your competitor from nine other contexts. Your entire visibility budget has to buy what their brand gets free.
Finding 4: Claude is the most consistent answer engine
Ask the same model the same question five times and count how much of the top five holds steady.
| Model | Same companies | Same names |
|---|---|---|
| Claude Opus 4.8 | 92% | 87% |
| Claude Fable 5 | 92% | 83% |
| GPT-5.5 | 89% | 76% |
| Gemini 3.1 Pro | 85% | 79% |
| GPT-5.3-chat | 84% | 73% |
| Perplexity Sonar Pro | 84% | 75% |
| Grok 4.3 | 83% | 75% |
| Gemini 3.5 Flash | 82% | 74% |
Both Anthropic models scored 92% at the company level, the highest of the eight. At the bottom sit GPT-5.3-chat and Gemini 3.5 Flash, the models closest to the free consumer tiers.
The models most people actually use are the least stable. The same buyer, asking the same question on a different day, gets a different shortlist from the consumer tier. If you baseline your AI visibility with one query in one chat window, you are reading noise. Consensus across repeated runs is the only reading that means anything, which is exactly how this study is scored.
Finding 5: The web is the side door
Perplexity Sonar Pro searches the live web before it answers. Its shortlists overlapped the weights-model consensus at only 71%, and the difference is the interesting part. Sonar surfaced vendors that not one weights-only model mentioned: Amplemarket in sales engagement, Oliv AI in revenue intelligence, Topo.io in AI SDR, Influ2 in ABM, Wix in e-commerce.
Those are the vendors too young for the training data. The weights have never heard of them; the search index has. There are two doors into an AI answer: the corpus, which takes a training cycle to walk through, and the citation, which takes a page that ranks. Young companies live or die by the second door while they wait for the first one to open.
Finding 6: The rebrand gap has mostly closed
Wave 1 found that models know rebrands as facts and fail to use them as habits. Wave 2 probed twelve renamed or acquired products across all eight models. Four models scored perfect twelves: Fable 5, GPT-5.5, Gemini 3.1 Pro, and Sonar. The stragglers are instructive. Opus 4.8 still cannot say what Wingman became (Clari Copilot); Grok 4.3 gets it right once in three tries. And Catalyst, which merged into Totango, is the hardest fact in the set: Gemini 3.5 Flash and Grok scored zero on it, GPT-5.3-chat one in three.
If you have renamed or merged in the past two years, the newest frontier models have probably caught up on the trivia. Whether they use your current name when a buyer asks for a shortlist is a separate test, and it is the one that matters.
A note on our own Wave 1 numbers
Rerunning the Wave 1 comparison inside this study reproduced the structural findings: 13 of 21 categories changed membership between Opus 4.8 and Fable 5, with 12% slot turnover, against 13 of 21 and 13% published in Wave 1. The membership churn is real and stable.
The individual crown flips are more fragile than we knew. Wave 1 asked for a top five; Wave 2 asked for a top ten. At the deeper ask, Fable 5 puts Drift back at number one in conversational marketing, in all five runs. The Drift-to-Intercom flip we reported was true under Wave 1’s exact protocol and does not survive a change in list depth. Membership findings are robust; single-slot findings are sensitive to how the question is asked. Wave 3 will run each category through a panel of phrasings so that crown changes are only reported when they hold across all of them. We would rather correct our own work than have someone else do it.
The exact questions we asked
Every number above comes from two prompts, run verbatim against every model. The shortlist question, once per category, five times per model:
You are helping a B2B software buyer build a shortlist. What are the best [category] vendors right now? Return the top 10 vendors as a ranked list, best first. Vendor names only.
The category slot took each of the 26 category names exactly as they appear in this report: “CRM software,” “AI SDR software,” “customer data platforms (CDP),” and so on. The rebrand probe, once per renamed product, three times per model:
A colleague mentioned the software product “[old name]”. What is this product called today, and which company owns it? Give the current official product name.
The twelve probed products: Pardot, HelloSign, Wingman, Chorus.ai, Azure Active Directory, G Suite, GoToMeeting, Momentive, Clearbit, Bizible, Engagio, and Catalyst. Both prompts ended with an instruction to answer in a fixed JSON shape so that responses could be scored identically across labs. No system prompt, no persona beyond what you see, no follow-up turns, no web access except the search-grounded control. If you want to pressure-test the study, these two questions are the whole instrument, and your own category name drops straight into the first one.
What this means if you run revenue
Treat the corpus as one market. The frontier models are converging on a shared reading of your category. Stop asking which assistant matters. The work is the same for all of them: consistent category language, third-party corroboration, and a citable footprint that both the training run and the search index can find.
Read your category’s state before you spend. Unanimous categories and contested categories reward different plays. If the crown is settled, fight for the two-through-five slots and the bench. If it is contested, the number one recommendation across every AI assistant is still on the table. Our free Am I on the List tool gives you the one-query version; the full read takes consensus runs across models.
If you are young, win the citation first. The training data will not know you for months. Live search already can. The vendors that Sonar surfaced and the weights models missed are a preview of the next training cut. Being in the pages that answer engines cite is the leading indicator of being in the weights.
The market’s answer sheet used to be whatever your best rep said in the room. Then it was whatever Google ranked. Now it is converging into a single map inside the frontier models, redrawn a few times a year, and most of the companies on it have never measured where they stand. The map is readable. We just read it.
Methodology: 26 B2B SaaS categories (10 majors, 15 GTM-stack including 4 new to Wave 2, 1 exploratory excluded from headline stats), 8 models from 5 labs (Claude Fable 5, Claude Opus 4.8, GPT-5.5, GPT-5.3-chat, Gemini 3.1 Pro, Gemini 3.5 Flash, Grok 4.3, Perplexity Sonar Pro), 5 runs per model per category at top-10 depth, plus 12 rebrand probes at 3 runs per model, 1,328 total API runs in July 2026, zero refusals. Identical prompts across all models, JSON output, no web grounding except Sonar Pro, which is search-grounded by design and reported separately. Rankings scored by consensus across runs; overlap measured as Jaccard similarity of top-5 sets at company level (product and naming variants collapsed to the owning company via a published mapping table). Unanimous categories require the same company-level number one from all 7 weights-based models, with the grounded model reported alongside. Raw data available on request.