Google Search Console’s Multimodal Report: How to Measure Visual Search Traffic

Google Search Console now separates text-based and multimodal web searches, giving publishers a clearer view of traffic generated when people search with images instead of typed keywords.

Google announced the new reporting capability on September 24, 2026. It covers searches originating from Google Lens, Circle to Search on Android, images uploaded to Google Search, and Chrome's right-click image search. The filter is available in the Search results performance report and the Generative AI performance report, and Google says the rollout is global.

This is useful because visual discovery does not behave like conventional keyword search. A person may point a camera at a product, circle an object on a screen, or upload a photo without ever typing a query. Until now, much of that activity was difficult to isolate from ordinary web performance data.

This guide explains what the new report measures, what it does not reveal, and how publishers can turn page-level multimodal data into practical image and content improvements without inventing conclusions the report cannot support.

What Is Web Multimodal Search in Search Console?

In Search Console, Web: multimodal refers to web searches that begin with an image or camera-based input. Google's documentation lists four examples:

• Google Lens searches;
• Circle to Search on Android;
• images uploaded directly to Google Search; and
• Chrome's "Search this image" action.

This is different from the separate Image search type. The Image filter reports results shown in the Google Images tab. The multimodal filter reports web-search activity initiated with an image, even when the destination is a normal web page.

That distinction matters. A product guide, repair tutorial, plant-identification page, travel landmark article, recipe, or visual comparison may receive multimodal exposure even when the page is not primarily designed as an image-gallery result.

What Google Officially Announced

The following points are documented facts rather than predictions:

• Google announced web multimodal Search performance reporting on September 24, 2026.
• The data appears in the Search results performance report and the Generative AI performance report.
• The rollout began globally on the announcement date.
• A property will only see relevant metrics if it is receiving traffic from multimodal searches.
• The Search results report can show clicks, impressions, click-through rate, and average position.
• The Generative AI performance report displays impressions and supports grouping by pages, countries, dates, and devices.
• Specific query text is unavailable for multimodal traffic because these searches primarily use images rather than typed words.

Google does not claim that enabling the report changes rankings. It is a measurement feature. Any optimization decision should therefore come from patterns in your own data, not from assuming that the existence of a new filter creates a new ranking factor.

How to Find the Multimodal Filter

1. Open your verified property in Google Search Console.
2. Open Performance and choose either Search results or the Generative AI report.
3. Select the search-type filter at the top of the report.
4. Choose Web: multimodal.
5. Set a useful date range, such as the last 28 days or last three months.
6. Review the available Page, Country, Device, and Date dimensions.
7. Use Export if you want to compare periods or work with the data in a spreadsheet or analytics system.

If the option is absent, do not immediately assume that your property has a technical problem. Google states that these metrics appear when a site receives traffic from the relevant searches. A new property or a site with little visual-search exposure may have no multimodal rows to display.

The Most Important Limitation: No Query Dimension

Traditional SEO analysis often starts with the query table. Multimodal analysis cannot follow that workflow exactly.

Google's help documentation says that specific text-query data is not available when the multimodal search type is selected. This is logical: the user's input may be a camera frame or a selected portion of an image rather than a string of words.

As a result, Search Console may tell you that a page received multimodal impressions or clicks, but not whether the user photographed a product label, circled a landmark, searched for a similar item, or tried to identify an object.

Practical implication: treat the page as your main unit of analysis. Examine what visible entities, objects, products, locations, diagrams, screenshots, or problems are represented on that page. Then form testable hypotheses instead of assigning an imaginary keyword to the traffic.

A Practical Multimodal Analysis Workflow

1. Establish a baseline

Select Web: multimodal and record total impressions, clicks, CTR, and average position in the Search results report. In the Generative AI report, record impressions. Use a date range long enough to reduce daily noise.

Do not compare a partial week with a complete month and present the percentage as meaningful growth. Search Console may also mark the newest data as preliminary, so allow recent values to settle before making large decisions.

2. Rank landing pages by opportunity

Open the Pages dimension and identify URLs with the strongest multimodal visibility. Classify them by page type, for example:

• product or comparison pages;
• how-to tutorials;
• travel destinations and landmarks;
• recipes and ingredients;
• visual reference guides;
• software tutorials with screenshots;
• charts, diagrams, or infographics.

This classification is an editorial analysis, not a Google-provided label. Its purpose is to reveal which types of assets on your site appear to attract visual discovery.

3. Compare devices

Camera-led searches are naturally associated with mobile devices, but do not assume every property will show the same distribution. Use the Devices dimension to verify your own pattern. Desktop exposure can still arise from uploaded images or Chrome image search.

If a page earns mobile impressions but very few clicks, inspect its mobile experience: image loading, intrusive overlays, text legibility, tap targets, main-content visibility, and whether the answer is immediately understandable.

4. Compare countries

The Countries dimension can reveal where visual discovery is occurring. A travel or ecommerce site might find multimodal exposure in markets not prominent in its text-query report.

That pattern can justify further investigation into translated captions, localized product details, measurement units, shipping information, or regional image examples. It does not, by itself, prove that localization will improve rankings.

5. Inspect the page and its images

Review the actual landing page rather than optimizing only a spreadsheet row. For each important image, check:

• Is the image genuinely relevant to the page?
• Is the main object clear and sharp?
• Does the surrounding text explain what the image shows?
• Does the file have a short, descriptive filename?
• Is the alt text useful and contextual rather than keyword-stuffed?
• Is the image embedded with a standard <img src> element?
• Is there a responsive version for smaller screens?
• Does the page load the image efficiently?

Google's image SEO documentation recommends standard HTML image elements, descriptive filenames and alt text, responsive images with a fallback src, supported formats such as WebP and AVIF, and a balance between visual quality and page speed.

6. Make one measurable improvement at a time

Possible improvements include replacing a generic stock image with an original photograph, adding a close-up, improving a diagram, rewriting vague lalt text, adding a useful caption, or reducing an oversized file.

For production assets, Rubic8's Image Resizer can help prepare appropriately sized image files. Resizing alone is not an SEO strategy; it is one part of delivering a high-quality, fast visual experience.

Record the change date and compare a reasonable before-and-after period. Avoid changing the image, title, page structure, and internal links simultaneously if your goal is to understand which change helped.

How to Interpret Common Data Patterns

High impressions, low CTR

What the data establishes: the page was shown frequently in multimodal web results but received relatively few clicks.

Possible explanations to investigate: the result may not look sufficiently relevant, the image may answer the user's need without a click, the title or snippet may not communicate additional value, or the visual could be competing with stronger alternatives.

Do not claim a single cause without evidence. Review the page, search appearance, content value, and changes over time.

Low impressions, high CTR

This can indicate that a page is compelling when shown but has limited exposure. Inspect whether the page has only one useful image, whether important visuals are difficult to crawl, and whether related pages could provide better contextual internal links. It may also simply be a narrow topic with limited demand.

Strong mobile performance

This is consistent with camera-based discovery, but it is still your site's observed pattern rather than a universal benchmark. Prioritize mobile rendering, responsive images, readable labels, and fast loading.

Strong impressions in the Generative AI report

This means the site appeared in relevant Google generative AI experiences under the report's impression rules. It does not prove that a particular image caused the inclusion, that the page was cited for every visual search, or that the traffic came from a specific typed query.

Example: A Product Comparison Page

Imagine a page comparing two portable coffee grinders. This is a hypothetical workflow, not a benchmark.

The page begins receiving multimodal impressions. Because query text is unavailable, the publisher reviews the page itself and finds several plausible visual intents: identifying a model, comparing burr assemblies, checking size, or finding a similar product.

The publisher then:

1. replaces a generic hero with an original side-by-side photograph;
2. adds close-ups of the adjustment mechanism and burrs;
3. writes descriptive alt text for each image;
4. adds visible captions that explain meaningful differences;
5. serves responsive WebP files with explicit width and height; and
6. records the publication date of those changes.

After enough data accumulates, the publisher compares page-level multimodal performance before and after the update. The result can support a decision about similar comparison pages, but it should not be presented as proof of a universal ranking factor.

What Not to Do

• Do not invent missing queries. Page context can guide hypotheses, but Search Console is not revealing the user's image input.
• Do not confuse Web: multimodal with the Image search tab. They are separate search types.
• Do not optimize alt text as a keyword list. Describe the image naturally and in context.
• Do not publish decorative images only to increase image count. Useful, relevant visuals are the goal.
• Do not treat the filter as a ranking signal. It reports performance; it does not grant visibility.
• Do not overreact to small samples. A few impressions or clicks are not a reliable trend.

A 30-Minute Multimodal Search Audit

1. Open Web: multimodal for the last 28 days.
2. Export the Pages table.
3. Mark the five pages with the most impressions.
4. Record device and country patterns for those pages.
5. Inspect every meaningful image on each page.
6. Check filenames, alt text, captions, surrounding copy, dimensions, file size, and mobile rendering.
7. Select one page with clear improvement opportunities.
8. Make one coherent update and document the date.
9. Review the same report after enough data has accumulated.

Frequently Asked Questions

Why can't I see the Web: multimodal filter?

Google says sites will start seeing the metrics when they receive traffic from multimodal queries. If the property has no qualifying data, the filter or rows may not appear.

Does multimodal traffic include Google Images?

Web: multimodal and Image are separate search types. Multimodal covers web searches initiated with images, while Image covers results shown in the Google Images tab.

Can I see the image a user searched with?

No such capability is described in the official report documentation. Specific query data is unavailable for multimodal searches, so publishers should not expect Search Console to reveal the user's source image.

Does the report include AI search?

Google announced that multimodal reporting is available in both the Search results performance report and the Generative AI performance report. The latter currently focuses on impressions and supports page, country, date, and device dimensions.

Will adding alt text increase multimodal rankings?

Google says alt text helps its systems understand an image and improves accessibility. That does not guarantee a ranking increase. Alt text should accurately describe the image in the context of the page.

Should every page contain multiple images?

No. Add visuals when they improve the user's ability to understand, compare, identify, or complete a task. A useful original diagram can be more valuable than several decorative stock images.

Final Takeaway

The new multimodal filter closes an important measurement gap. Publishers can now separate visual-input web discovery from ordinary text searches and analyze which pages, countries, devices, and dates contribute to that visibility.

The report is not a source of hidden visual keywords. Its greatest value is page-level diagnosis: identifying which content attracts multimodal exposure, inspecting the visual experience, and testing improvements grounded in image SEO and user usefulness.

Start with your own data, document what you change, and distinguish measured facts from plausible explanations. That discipline will produce better decisions than chasing a speculative "visual search hack."

Official Sources

• Google Search Central: Announcing web multimodal Search performance reporting in Search Console
• Google Search Console Help: Performance report overview
• Google Search Console Help: Dimensions and data groupings
• Google Search Console Help: Generative AI performance report
• Google Search Central: Image SEO best practices

Last reviewed: October 4, 2026. Search Console interfaces and documentation can change, so verify current behavior against Google's official sources.