Vidore Benchmark
ViDoRe Benchmark: an open visual document retrieval benchmark and ColPali model suite for enterprise RAG evaluation.
If your pipeline touches scans, dense tables, or design-heavy PDFs, ViDoRe V3 is the yardstick worth reporting against — the bounding-box ground truth is what elevates it above synthetic sets, and the ColPali/ColQwen/ColSmol weights let you benchmark and ship on the same scale. Budget GPU capacity and some wiring time before any number means anything, and know that two V3 datasets are private, so nobody outside ILLUIN can reproduce your score exactly. For plain-text corpora, BEIR still measures
Verified 4d ago · liveness 74/100 · cite: rightaichoice.com/tools/vidore-benchmark
- Enterprise RAG teams whose documents are scans, dense-table PDFs, or layout-heavy reports
- Researchers benchmarking multimodal retrieval against a human-verified standard
- Engineers comparing ColPali, ColQwen2, ColQwen2.5, ColSmol, and ModernVBERT on one scale
- Teams needing multilingual visual retrieval evaluation across 6 languages
- Plain-text corpora where BEIR or MS MARCO already measure the job
- Teams without GPU capacity to run vision-language retrieval models
- Buyers expecting a hosted evaluation API or a vendor support contract
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Skip Vidore Benchmark if your corpus is plain text that BEIR or MS MARCO already scores well, or if you need a hosted evaluation API rather than a self-run harness.
The benchmark itself is free, but running ColPali v1.3, ColQwen2.5 v0.2, or ModernVBERT to produce scores means paying for GPU compute out of your own budget.
ViDoRe's datasets, model weights, leaderboard Spaces, and code are all published free on Hugging Face, so the software cost is effectively zero at any team size. The real spend is compute: GPU time to serve the ColPali/ColQwen/ColSmol and ModernVBERT models. Research labs, academic groups, and enterprise RAG teams evaluating retrievers before a rollout get the most per dollar. Teams that just need text retrieval should spend their budget on a managed text-retrieval stack instead.
In short
Vidore Benchmark — ViDoRe Benchmark: an open visual document retrieval benchmark and ColPali model suite for enterprise RAG evaluation. Best for Enterprise RAG teams whose documents are scans, dense-table PDFs, or layout-heavy reports, Researchers benchmarking multimodal retrieval against a human-verified standard, Engineers comparing ColPali, ColQwen2, ColQwen2.5, ColSmol, and ModernVBERT on one scale. Free to use.
What's new in Vidore Benchmark
Checked 4 days agoAcross the latest 5 updates: 5 changelog entries.
Granular Feature Access
Hugging Face Hub now lets organizations control feature access per resource group rather than by organization role, so Jobs can stay open while Inference Endpoints are restricted to admins.
Filter Jobs by Label
Jobs can now be filtered by label, with clickable label chips and job counts above the list plus a free-form key=value filter for labels not shown as chips.
MCP Server Enhancements
The Hugging Face MCP server was updated with an hf_fs tool for unified Hub access plus Sandboxes.
Egress Metrics for Users and Organizations
Users and organizations can now see egress usage in the dashboard with a per-user breakdown.
Build Spaces with AI Agents
The new Space creation page includes an option to build with an AI agent.
What people actually say about Vidore Benchmark — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
34 mentions across 4 sources (Hacker News, YouTube, Bluesky, GitHub) · researched Jul 28, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Addresses a real gap in evaluating visually-rich enterprise document retrieval.
- +Includes 10 diverse datasets with human-verified annotations across 6 languages.
- +Provides per-query bounding boxes and reference answers for detailed analysis.
- +Hosted leaderboard enables easy model comparison on Hugging Face.
- +Supports multiple cutting-edge models: ColPali, ColQwen, ColSmol, ModernVBERT.
- −Non-deterministic evaluation results plague reproducibility.
- −Setup is non-trivial; users report CLI errors and missing documentation.
- −Small community with only ~278 GitHub stars limits support.
- −V1 benchmark quickly saturated, requiring version-aware usage.
- −Advanced skill level required; not for beginners in retrieval.
- • No hidden costs—datasets and models are open-source
Viability Score
How well maintained and how widely used is Vidore Benchmark? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- ViDoRe V3 benchmark with 10 datasets, 8 public and 2 kept private
- 26,000+ document pages spanning enterprise and industrial domains
- 3,000+ queries translated across 6 languages
- Human-created and human-verified retrieval annotations
- Per-query bounding boxes marking key elements on relevant pages
- Reference answers supplied for each retrieval query
- Page-image embedding with late-interaction matching in ColPali
- ColPali v1.3 vision-language retrieval model weights
- ColQwen2 v1.0 and ColQwen2.5 v0.2 retrieval models
- ColSmol lightweight retrievers at 256M and 500M parameters
- ModernVBERT v1.0 retrieval model on Hugging Face
- ViDoRe V1 and V2 collections for version-over-version comparison
- Leaderboard Space ranking public benchmark submissions
- Open datasets, model weights, and code released on Hugging Face
About Vidore Benchmark
ViDoRe Benchmark is an open visual document retrieval benchmark plus a matching family of vision-language retriever models, released by the vidore organization on Hugging Face and developed by ILLUIN Technology with contributions from NVIDIA. It exists to answer one unglamorous question: does your retriever actually pull the right page out of a scanned invoice, a technical manual, or a layout-heavy slide deck where the answer lives in a table, a chart, or a bounding box rather than a clean paragraph? The current release, ViDoRe V3, spans 10 datasets (8 public, 2 kept private), 26,000+ pages, and 3,000+ queries translated into 6 languages, drawn from enterprise and industrial domains rather than academic text. Annotations are human-created and human-verified: each query comes with relevant pages, bounding boxes for key page elements, and a reference answer. V1 and V2 collections stay online for reproducing earlier results and tracking version-over-version progress. On the model side sits the ColPali line of vision-language retrievers. Rather than OCR then search, these models embed page images directly and score them with late-interaction matching. Published weights include ColPali v1.3, ColQwen2 v1.0, ColQwen2.5 v0.2, ColSmol at 256M and 500M parameters, and ModernVBERT v1.0. Everything lives on Hugging Face: datasets, weights, code, and the leaderboard Space that ranks submissions against each other. The practical contrast is with text-only IR benchmarks such as BEIR or MS MARCO. Those still do the job for plain-text corpora; ViDoRe is what you cite when the visual layer of the page is the whole point. There is no hosted scoring service and no support contract here — you run the evals yourself, and the two private V3 datasets mean a fully reproducible public score is not on offer.
Behind the Verdict
ViDoRe earns its place because it refuses to pretend documents are text. Page layout, tables, figures, and fonts carry meaning that a chunked-text retriever flattens away, and this benchmark is built to expose exactly that failure mode. The human-verified bounding boxes matter more than the headline page count: they let you diagnose *where* a retriever went wrong, not just that it did. Pick this when your corpus is enterprise-shaped. Invoices, engineering manuals, regulatory filings, slide decks, multi-language document sets. If you are choosing between ColPali v1.3, ColQwen2.5 v0.2, ColSmol at 256M or 500M, and ModernVBERT v1.0, evaluating all of them on one leaderboard beats assembling your own eval set. We'd reach for ColSmol first when latency or memory is tight and the accuracy gap is tolerable. Pass on it if your documents are already clean text. BEIR and MS MARCO are cheaper, faster, and better studied for that shape of problem, and running vision-language retrieval over plain paragraphs is wasted GPU. Pass too if you need a hosted evaluation API or someone to call when a score looks wrong — this is a research artifact maintained on Hugging Face, not a commercial service. The reproducibility caveat is real and worth stating plainly: 8 of the 10 V3 datasets are public and 2 are private, so your private split score cannot be independently checked by outsiders. That is a deliberate anti-overfitting choice, and it is also a limit on auditing. In practice the friction is setup, not cost. The datasets and weights are free to download; what you spend is engineering time wiring image preprocessing, embedding, and late-interaction scoring into something you trust. Teams that underestimate that step tend to report numbers they cannot explain. The closest alternative
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Real-world workflow fit
Concrete scenarios for the personas Vidore Benchmark actually fits — and what changes day-one when you adopt it.
You have a pipeline that ingests scanned invoices and technical manuals, and you want to know whether moving from OCR-plus-BM25 to ColQwen2.5 v0.2 buys you anything. You pull the ViDoRe V3 dataset collection off Hugging Face, run both retrievers against the 3,099 queries, and check your page-level scores against the ViDoRe Leaderboard Space.
Outcome: A defensible, citable accuracy number on documents your team actually handles, plus a bounding-box view of which key page elements the retrievers missed.
You are publishing a new late-interaction architecture and need an external standard. You evaluate on ViDoRe V3 alongside the V1 and V2 collections so reviewers can place your result on the same scale as ColPali v1.3 and ModernVBERT v1.0, then submit to the leaderboard.
Outcome: Comparable results across three benchmark versions and a public leaderboard entry, with human-verified annotations behind every query.
Before signing off on a vendor's document AI, you run its retrieval component against ViDoRe V3's industrial and enterprise datasets and compare the score to the open ColSmol 500M baseline you can host yourself.
Outcome: A factual basis for the buy decision instead of a vendor-run demo, and a fallback open model already benchmarked on the same queries.
Use Cases
- Evaluate multimodal retrieval models on enterprise documents with human-verified accuracy.
- Compare ColPali, ColQwen2, and other vision-language models on a standardized benchmark.
- Build production RAG pipelines that retrieve from complex layouts like invoices, reports, and forms.
- Drive research in visual document retrieval using diverse, multilingual datasets.
- Benchmark model improvements before deploying to document-heavy enterprise applications.
Models Under the Hood
as of 2026-10-17
Limitations
- ViDoRe is an open benchmark and model suite released by ILLUIN Technology (with contributions from NVIDIA for V3) on Hugging Face.
- V3 spans 10 datasets, 26,000+ pages, and 3,000+ queries across 6 languages, while being positioned for enterprise RAG evaluation.
- Two of the ten V3 datasets are kept private, which limits full reproducibility from public data alone.
- Running the ColPali, ColQwen2, ColQwen2.5, ColSmol (256M/500M), and ModernVBERT retrieval models requires your own compute; the evidence also does not show a hosted evaluation API.
as of 2026-10-04
Verification history
We have re-verified Vidore Benchmark 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Vidore Benchmark tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Research labs, academic groups, and enterprise RAG teams evaluating visual retrievers before committing to a production rollout.
What this tier adds
Starting tier: the V1, V2, and V3 benchmark datasets, the ColPali/ColQwen/ColSmol/ModernVBERT weights, and the leaderboard Spaces, all at no cost on Hugging Face.
Where the pricing makes sense
The company stage and team size where Vidore Benchmark's pricing actually pencils out — and where peers do it cheaper.
ViDoRe's datasets, model weights, leaderboard Spaces, and code are all published free on Hugging Face, so the software cost is effectively zero at any team size. The real spend is compute: GPU time to serve the ColPali/ColQwen/ColSmol and ModernVBERT models. Research labs, academic groups, and enterprise RAG teams evaluating retrievers before a rollout get the most per dollar. Teams that just need text retrieval should spend their budget on a managed text-retrieval stack instead.
Setup time & first value
How long it actually takes to get something useful out of Vidore Benchmark — broken out by persona, not the marketing-page minute.
Enterprise RAG engineer: a few hours to a day to pull the ViDoRe V3 collection and wire up an inference harness for ColQwen2.5 v0.2 or ColPali v1.3, assuming GPU access already exists. Researcher: half a day to reproduce a published score plus submission time for the leaderboard Space. AI lead comparing against a vendor: an afternoon if you reuse the ColSmol 500M baseline rather than training
Switching to or from Vidore Benchmark
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From BEIR or MS MARCO: keep your existing harness, add page-image embedding with a ColPali or ColQwen model, and re-score against ViDoRe V3's enterprise datasets.
- →From an OCR-plus-BM25 pipeline: replace the text extraction step with direct page-image embeddings, then validate the change on ViDoRe V1, V2, and V3 to see the version-over-version delta.
- →From a vendor-run document AI demo: run the vendor's retriever output against ViDoRe V3 annotations and compare it to ColSmol 500M as an open baseline.
- ↗To a hosted RAG or document AI vendor: use your ViDoRe V3 scores as the acceptance benchmark when you hand the pipeline over.
- ↗To a lighter text-retrieval stack: if your documents turn out to be text-dominant, drop the vision-language models and evaluate on BEIR or MS MARCO instead.
Integrations
Resources & Guides
- Resourcehuggingface.co
Introducing Vidore V3 · Vidore Benchmark
Helpful link from huggingface.co
- Resourcehuggingface.co
Colpali · Vidore Benchmark
Helpful link from huggingface.co
- Resourcehuggingface.co
Vidore V2 · Vidore Benchmark
Helpful link from huggingface.co
- Resourcehuggingface.co
Vidore Leaderboard · Vidore Benchmark
Helpful link from huggingface.co
- Resourcehuggingface.co
Vidore Benchmark V3 · Vidore Benchmark
Helpful link from huggingface.co
- Resourcehuggingface.co
Vidore Benchmark · Vidore Benchmark
Helpful link from huggingface.co
- Resourcehuggingface.co
Vidore Benchmark V2 · Vidore Benchmark
Helpful link from huggingface.co
- Documentationhuggingface.co
Docs · Vidore Benchmark
Full product docs from huggingface.co
Tutorials & Learning
YouTube returned 6 videos for “Vidore Benchmark”, and we withheld 6: 6 did not mention Vidore Benchmark. We are showing none, because we could not prove any of them are about Vidore Benchmark.
Tools that pair well with Vidore Benchmark
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Colpali Cookbooks
Open ColPali visual document retrieval models and the ViDoRe V3 enterprise benchmark, all free on Hugging Face.
Goodfire
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Arena AI
Arena AI ranks chat models, coding agents and fullstack builds on live head-to-head human votes.
Featured Head-to-Head Comparisons
Vidore Benchmark vs Screenplayiq
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Vidore Benchmark vs Praktika
Praktika and ViDoRe Benchmark serve completely different needs: one is for language learners seeking speaking practice, the other is a technical tool for evaluating document retrieval models. Your choice depends entirely on whether you want to improve your Spanish or benchmark a ColQwen2 model. There is no crossover — pick based on your domain.
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Colpali Cookbooks
Open ColPali visual document retrieval models and the ViDoRe V3 enterprise benchmark, all free on Hugging Face.
Frequently Asked Questions
Best-of guides
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