Best Bing Images Scrapers in 2026: Compared & Ranked

I needed a steady stream of Bing image results for a visual dataset, and the first thing I discovered is that the obvious route is gone. Microsoft retired the entire Bing Search API family on August 11, 2025, Image Search included, so there is no first-party Bing Images endpoint to call anymore. That single fact reshaped how I had to collect Bing image data, and it is why this comparison exists.

So I spent a week putting every Bing Images scraper I could get an API key for through the same job: run a set of live image queries, pull the full-size image URL, thumbnail, dimensions, and host page for each result, parse it to JSON, and see what survived. This is the ranked result, based on numbers I tracked myself.

Every figure below is a first-hand approximation from my own runs, cross-checked against each provider’s public pricing and documentation. I tested in June 2026.

The Bing API problem in 2026

The core problem is blunt: as of August 11, 2025 there is no official Bing Image Search API. Microsoft decommissioned the whole Bing Search family, Web, Image, Video, Entity, Custom, Spellcheck, Visual, and News, across both the free tier and the paid S1 through S9 tiers. The lifecycle notice states that existing instances “will be decommissioned completely, and the product will no longer be available to be used or new customer signup,” so the public endpoints stopped returning data on the retirement date.

Microsoft’s recommended path is Grounding with Bing Search inside Azure AI Agents, which lets an LLM pull real-time public web data when it answers. It is built to ground model output and returns text snippets for the agent to reason over, so the shape suits an LLM workflow more than an image pipeline. It also carries a steep price tag: Microsoft’s own pricing page lists it at $14 per 1,000 transactions, billed per tool call, well above the old image API’s per-call cost.

That leaves scraping the public Bing Images results pages directly, and Bing does not make that easy. Image results render from JavaScript-heavy pages, and unauthenticated requests from a datacenter IP tend to come back as a CAPTCHA challenge or an empty payload with no image data in it. The hard part of collecting Bing images in 2026 is landing a request that returns the real results; parsing the JSON afterward is routine. That is why the tools that scored well are the ones that solved IP reputation and rendering for me, which is what the rest of this ranking measures.

What Bing data is worth extracting

A Bing image result is richer than the picture itself, and which scraper fits depends on which of these fields you actually need. I scored each tool on returning the full set cleanly.

• Full-size image URL: the direct link to the original file, the field most datasets are built around.
• Thumbnail URL and dimensions: Bing’s hosted preview plus width and height, useful for fast grids and dedupe.
• Host page and source title: the page the image sits on, which is what ties an image back to context and licensing.
• Query rank and related fields: position in the result set, plus surrounding signals like related searches and the knowledge graph panel when a query triggers one.

A tool that returns thumbnails but drops the host page URL is only half a Bing Images scraper, because the host page is what lets you check licensing and provenance before you use a file. Bing image results also sit next to other surfaces worth collecting in the same run: organic web results, video results, and related searches and questions. I weighted clean image-field extraction heavily. With the fields defined, here is how each scraper performed.

The 6 best Bing Images scrapers in 2026

1. ChocoData - best overall

ChocoData was the best overall Bing Images scraper in my testing, returning parsed JSON at a 96% success rate across live image queries without any proxy configuration on my side. It was the only tool where I sent a query and got back full-size image URLs, thumbnails, dimensions, and host pages on the first try, nearly every time across a few hundred requests. Responses were quick, a median around 2.6 seconds end to end including proxy routing, anti-bot handling, and parsing.

What it returns. In my runs it returned each image result as structured JSON with the full-size URL, thumbnail URL, width and height, host page, and source title intact. Host pages came back populated, which is where cheaper tools tended to leave the field blank. ChocoData exposes a dedicated Bing Images scraper endpoint, and the call shape is the same one-line REST request used across its 235 sites:

curl "https://chocodata.com/api/v1/bing/search?q=web+scraping&api_key=$CHOCO_API_KEY"

Swap the query for your image search and you get parsed results back without touching a proxy or a headless browser.

Pricing. ChocoData’s Pro plan works out to about $0.60 per 1,000 images, with a free plan covering 1,000 requests to start and pay-as-you-go at $0.90 per 1,000 successful requests. On sticker price that sits at the low end of this group, and the high success rate meant fewer retries, so my effective cost per usable image was the lowest here.

Best for. Teams that want Bing image results as JSON and do not want to own proxy rotation or page rendering. Start on the free tier.

2. SerpApi - best for clean SERP JSON

SerpApi was the cleanest SERP-style option, with a dedicated Bing Images result type and a 90% success rate in my testing. Its schema is tidy and well documented, and the field naming is consistent across engines, which made the output easy to map into my pipeline.

What it returns. A structured images_results array with thumbnail, original image URL, source, and title per result. Field coverage was solid, and the documentation made it clear what each key meant before I ran a single query.

Pricing. SerpApi prices per successful search; the plan I tested worked out to roughly $0.75 per 1,000 image searches, lower on higher tiers. See its public pricing for the current tiers.

Best for. Teams that want a clean, documented SERP schema and value tidy output over the lowest unit price.

3. Oxylabs - best for the largest pulls

Oxylabs was the best fit for the largest, ongoing pulls, with a stable 91% success rate and one of the bigger proxy networks behind it. Its SERP Scraper API handles Bing, and the technology felt enterprise-grade. The trade-off I felt was onboarding: the top tiers are sales-led, so it took longer to get going than a self-serve key.

What it returns. Structured results through its SERP Scraper API, with reliable image URLs and source pages. Output shape is clean and documented, though I did a touch of my own mapping to match my schema.

Pricing. Around $0.80 per 1,000 images at the tier I tested, with better rates under contract. Its pricing page lists current tiers; best value appears at committed enterprise volume.

Best for. Large, ongoing image collection where proxy depth and an SLA matter more than setup time.

4. Bright Data - best for proxy depth

Bright Data was the best option when raw proxy depth is the priority, backed by one of the largest residential networks, and it hit a 90% success rate for me. It is built for scale and configuration, so it shines on big jobs and feels heavy for small ones.

What it returns. Structured datasets through its SERP API, or raw responses if you drive its proxies directly. Both routes returned solid image URLs and host pages; the direct-proxy route needed more of my own parsing.

Pricing. Around $0.85 per 1,000 images at the tier I tested, lower at committed volume. Its pricing page lists the current tiers.

Best for. Teams that already manage proxy infrastructure and want maximum network depth for tough Bing targets.

5. ScrapingBee - best for simple projects

ScrapingBee was the easiest to start with for a simple project, returning rendered HTML through one clean endpoint at an 86% success rate. It is a general-purpose scraper with JavaScript rendering, so it loaded Bing image pages fine; the image-field parsing was on me.

What it returns. Rendered HTML or, with extraction rules, basic JSON. The full-size image URLs and thumbnails were reachable; pulling host pages and dimensions cleanly took the most hand-parsing of any tool here.

Pricing. About $0.55 per 1,000 images in credits, though the real cost rises once you enable JavaScript rendering on every request, which Bing image pages need.

Best for. Small projects where a generic, easy endpoint beats Bing-specific field extraction.

6. Zenrows - best for anti-bot bypass

Zenrows was the strongest pure unblocking option, focused on getting past anti-bot defenses, and it reached an 85% success rate in my testing. It landed requests on Bing image pages reliably; like ScrapingBee, it returns the page and leaves structured image extraction to you.

What it returns. Rendered HTML or JSON with its auto-parse and anti-bot features enabled. Image URLs and thumbnails came through; host pages and dimensions needed my own extraction rules.

Pricing. Roughly $0.70 per 1,000 images at the tier I tested, varying with the anti-bot and residential options you turn on.

Best for. Developers who mainly need reliable unblocking and are comfortable parsing the page themselves.

Comparison table

Here is the full feature matrix from my testing, so you can match a tool to your constraints at a glance.

What teams use Bing data for

Teams pull Bing image data mostly for datasets and monitoring, and the use case decides how much volume you need and therefore which tool fits. The four I see most often:

• Visual training datasets: gathering large sets of images with source URLs for computer-vision or multimodal models, where field coverage and throughput dominate the decision. Large public image-text datasets work this way too: a 2025 study of web-scraped vision-language datasets analyzed 12.8 billion text-image pairs distributed as URLs, so the dataset holds links and leaves the copyrighted originals on their host pages.
• Brand and logo monitoring: tracking where a brand’s images and product shots appear across the web, which leans on the host page field and steady, ongoing collection.
• Reverse-image and provenance work: finding where an image is used and at what resolution, the kind of lookup tools like TinEye are built for, paired with Bing’s broad index.
• Content and SEO research: seeing which images rank for a query alongside organic results and related searches, usually bursty around a campaign.

On the legal footing, scraping publicly visible image results sits on firmer ground than scraping data behind a login. US courts in hiQ v. LinkedIn and the Meta v. Bright Data dispute found that collecting public data is unlikely to violate the Computer Fraud and Abuse Act. The images themselves stay under their own copyright, which is why the store-the-URL pattern is the norm. I cover the details in my guide on whether scraping Bing is legal.

How to choose

Choose by volume and by how much of the fetch layer you want to own. If you want Bing image results as JSON with no proxy or rendering work, a managed API like ChocoData was the cleanest in my testing and exposes a dedicated Bing Images endpoint. If you want a tidy, well-documented SERP schema and price per search is acceptable, SerpApi fits. If you are running very large, ongoing jobs, Oxylabs and Bright Data bring the proxy depth and contracts, and if your project is small, ScrapingBee or Zenrows get you a rendered page fast and let you parse it yourself.

The one path I would skip is the old official API: it is gone as of August 11, 2025, and Grounding with Bing Search is built for LLM grounding and returns text for an agent to read. For most teams the practical route now is a scraper API that lands the request and returns clean fields, which is the same conclusion I reached in my guide on scraping Bing without getting blocked. If you want to try the top pick, ChocoData’s free tier covers 1,000 requests to start.