US web searches, 19 Oct 2025 – 11 Oct 2026, weekly. Seven keywords on one scale. Method and code at the end.

The short version

  • ChatGPT is still far ahead, but its lead is shrinking. Across the seven chatbots, ChatGPT took 66.7% of the search interest in the first 8 weeks of the year and 54.0% in the last 8.
  • Claude's search interest more than tripled. Its weekly average went from 6.2 to 20.4 (on a scale where ChatGPT's peak week is 100). Its share of the seven went from 4.7% to 14.4%.
  • Claude overtook Gemini in three weeks: 24 May 2026 (33 vs 32), 28 Jun (27 vs 26) and 5 Jul (28 vs 25).
  • Gemini grew steadily. Average interest rose from 22.4 to 32.9, and its share from 17.0% to 23.3%.
  • The rest faded. Perplexity fell from 3.1 to 1.0 and DeepSeek from 2.0 to 1.5. Copilot and Grok stayed roughly flat.

Why this is hard to do in Google Trends

Google Trends only lets you compare 5 terms at once, and each comparison is scaled to its own peak. To put seven chatbots on the same 0–100 scale, you need to split them into batches that share a common keyword and rescale every batch against it. That is tedious by hand and easy to get wrong.

For this piece the batching and rescaling were automated (details below), and every row is flagged as rescaled, so you always know the numbers are an anchored estimate and not raw Google values.

What the year looks like

1. ChatGPT: dominant, with a falling share

First 8 weeks Last 8 weeks
ChatGPT 66.7% 54.0%
Gemini 17.0% 23.3%
Claude 4.7% 14.4%
Copilot 4.6% 3.7%
Grok 3.1% 2.8%
Perplexity 2.4% 0.7%
DeepSeek 1.5% 1.1%

Share of the combined search interest of the seven keywords. Weeks: 19 Oct–7 Dec 2025 vs 23 Aug–11 Oct 2026.

ChatGPT's own interest did not collapse: it averaged 76 over the year, and was 76 in the last 8 weeks against 88 in the first 8. What changed is that the others grew while it moved sideways.

2. Claude: the biggest mover

Claude's line is flat near 6 until January, passes 15 in the week of 1 Feb 2026, and peaks at 33 in the week of 24 May. It then settled around 20 and has been climbing again in the last weeks, to 23.

3. Gemini: slow and steady

Gemini sits between 19 and 35 all year. It reached 35 in April and again in late September and early October.

4. The long tail

Perplexity's interest is 1 in the last 8 weeks and DeepSeek's about 1–2. With values this small, a one-point change is within the noise of Google's rounding, so we would not over-read those two.

What the data can't tell you

  • Search interest is not usage. People reach chatbots through apps, bookmarks and browser bars. A search is only one of the ways to get there.
  • Some of these words mean other things. "Claude" is also a first name, "Gemini" a zodiac sign, "Copilot" a common word and "Grok" a verb. That inflates those numbers by an unknown amount.
  • US only, web search only. Other countries and YouTube or News search may look different.
  • Rounded integers. Google reports whole numbers, so small values are coarse.
  • Rescaled data. Values on the 0–100 scale come from batches linked by an anchor keyword, so differences of a point or two are not meaningful.

What you can do with this

  • Marketing and SEO teams: track brand vs competitors in one chart, weekly.
  • Analysts and investors: use search interest as one signal of attention for AI products, next to downloads and revenue.
  • Journalists: check a claim like "X is overtaking Y" against a year of data.
  • Anyone with a keyword list longer than five.

Reproduce it 

from apify_client import ApifyClient import pandas as pd client = ApifyClient("<APIFY_TOKEN>") run = client.actor("sondalabs/google-trends-scraper").call(run_input={ "keywords": ["ChatGPT", "Gemini", "Claude", "Copilot", "Perplexity", "Grok", "DeepSeek"], "geo": "US", "timeframe": "today 12-m", "dataTypes": ["interest_over_time"], }) rows = client.dataset(run["defaultDatasetId"]).list_items().items df = pd.DataFrame([r for r in rows if r["type"] == "interest_over_time" and not r["isPartial"]]) wide = df.pivot(index="date", columns="keyword", values="value") share = wide.iloc[-8:].sum() / wide.iloc[-8:].sum().sum() print(share.round(3))

The run returned 371 rows (7 keywords × 53 weeks, including the last partial week, which we left out of every figure above). It cost under $0.20.

Disclosure: I build the Google Trends Scraper used here. You can check every number with the code above.

Source