Vidore Benchmark
Open visual document retrieval benchmark and model suite for enterprise RAG.
ViDoRe V3 is the benchmark to start with for any enterprise RAG project involving visually structured documents. Its human-verified annotations and real-world datasets give you a credible, reproducible evaluation baseline that synthetic benchmarks can't match. If your documents are plain text, skip it for BEIR or MS MARCO. If you lack GPU capacity, plan for infrastructure before adopting the models.
Verified 1d ago · liveness 74/100 · cite: rightaichoice.com/tools/vidore-benchmark
- Enterprise teams building RAG on visually rich documents
- Researchers evaluating multimodal retrieval models
- Developers comparing ColPali, ColQwen2, ColSmol, or ModernVBERT
- Data scientists needing a multilingual visual retrieval benchmark
- Text-only retrieval scenarios (use BEIR or MS MARCO)
- Teams without GPU infrastructure for vision-language models
- Quick hosted evaluation API needs
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Skip ViDoRe if your documents are plain text (use BEIR or MS MARCO instead) or if you lack GPU infrastructure to run vision-language models.
Private datasets in V3 are not publicly available, so you can't fully reproduce results on those domains.
ViDoRe is free and open-source, making it the most cost-effective benchmark for visual document retrieval — there are no licensing fees. Paid alternatives like enterprise RAG evaluation platforms charge for similar coverage, so it's ideal for startups and research teams.
In short
Vidore Benchmark — Open visual document retrieval benchmark and model suite for enterprise RAG. Best for Enterprise teams building RAG on visually rich documents, Researchers evaluating multimodal retrieval models, Developers comparing ColPali, ColQwen2, ColSmol, or ModernVBERT. Free to use.
What's new in Vidore Benchmark
Checked yesterdayAcross the latest 5 updates: 5 changelog entries.
Granular Feature Access
Hugging Face Hub now allows controlling feature access per resource group, enabling finer permissions.
Filter Jobs by Label
Jobs can now be filtered by labels with clickable chips and key=value input.
MCP Server Enhancements
HF MCP server updated with hf_fs tool for unified Hub access and Sandboxes.
Egress Metrics for Users and Organizations
Users and orgs can now view egress usage in dashboard with per-user breakdown.
Build Spaces with AI Agents
New Space creation page includes 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.
- +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: September 2026
How we score →Key Features
- V3 benchmark: 10 datasets (8 public, 2 private)
- 26,000+ pages with human-verified annotations
- 3,099 queries in 6 languages
- Per-query bounding box ground truth
- Reference answers for each query
- Late-interaction matching mechanism
- ColPali v1.3 vision-language retrieval model
- ColQwen2 v1.0 retrieval model
- ColQwen2.5 v0.2 retrieval model
- ColSmol 256M and 500M lightweight models
- ModernVBERT v1.0 retrieval model
- Hugging Face hosted leaderboard
- Benchmark versions V1, V2, V3
- Open-source models on Hugging Face
About Vidore Benchmark
ViDoRe is an open benchmark and model suite for visual document retrieval, developed by ILLUIN Technology with contributions from NVIDIA. It targets teams building retrieval-augmented generation (RAG) systems on visually rich documents like invoices, technical manuals, and reports. The latest V3 release expands the benchmark to 10 datasets (8 public, 2 private), over 26,000 pages, and 3,099 queries across 6 languages, with ground truth—relevant pages, bounding boxes, and reference answers—created and verified by human annotators rather than synthetic pipelines. This makes it a practical, industrial-grade evaluation standard for multimodal retrieval. Alongside the benchmark, ViDoRe hosts a family of retrieval models on Hugging Face, including ColPali v1.3, ColQwen2 v1.0, ColQwen2.5 v0.2, ColSmol 256M and 500M, and ModernVBERT v1.0. These vision-language models embed document pages as images and use late-interaction matching to outperform text-only pipelines on visual features like tables, layouts, and figures. The leaderboard, datasets, and weights are all hosted on Hugging Face, so you can evaluate and compare without leaving the platform. ViDoRe V3 targets production RAG pain points: it prioritizes enterprise relevance and data quality over clean academic texts. By incorporating real-world industrial domains and human-verified annotations, it gives you a credible baseline for measuring retrieval accuracy before you deploy. It is the reference point for serious multimodal retrieval work, filling the gap left by text-only benchmarks like BEIR. It is not for text-only retrieval scenarios—if your documents are plain text, stick with BEIR or MS MARCO. It also assumes you can run vision-language models; teams without GPU infrastructure will face a steep barrier to using the models effectively.
Behind the Verdict
ViDoRe is more than a benchmark; it's a curated model suite designed for a specific, painful problem: retrieving answers from documents where layout, tables, and figures carry meaning that text extraction loses. The V3 release is a genuine step up because it prioritizes enterprise relevance and data quality. The 10 datasets span diverse industrial domains, 8 are public, and — critically — the annotations are human-created and human-verified. That means when you evaluate a model on ViDoRe, the scores reflect real-world accuracy, not just performance on synthetic queries. This is a major advantage over older benchmarks like BEIR, which rely on clean academic texts and often fail to capture the messiness of real business documents. For teams, the practical value is twofold. First, you get a standardized way to compare retrieval models side by side before you commit to one in production. The leaderboard lets you see at a glance how ColPali v1.3, ColQwen2 v1.0, ColQwen2.5 v0.2, ColSmol, and ModernVBERT stack up against each other and against text-only baselines. Second, the per-query bounding box annotations mean you can not only measure accuracy but also understand where models fail — a crucial diagnostic for improving your RAG pipeline. What ViDoRe is not: it's not a turnkey solution. It doesn't provide a hosted API to run retrieval instantly. It's a benchmark and model weights — you bring your own GPU and your own engineering to integrate the models into your stack. If you're a team without GPU infrastructure, the barrier to entry is real, and the documentation assumes familiarity with Hugging Face, transformers, and vision-language models. Where ViDoRe shines is in the evaluation phase of a RAG project. If you're an ML engineer or data scientist tasked with picking a retrieval model for a document-heavy enterprise application, ViDoRe gives you the evidence you need to make a defensible choice. It's also a strong fit for research teams working on visual document retrieval, as the diverse, multilingual dataset collection supports novel approaches and comparisons. In short, ViDoRe is a high-quality, open resource that fills a real gap. It's not a product you buy; it's a standard you adopt. If you're serious about multimodal retrieval, start here.
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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.
Needs to evaluate retrieval models for invoice processing RAG
Outcome: Uses ViDoRe V3 leaderboard to compare ColPali v1.3 vs ColQwen2 on diverse datasets, selects ColPali, and integrates it into a prototype within a week.
Building RAG on medical reports with complex layout
Outcome: Runs ViDoRe V3 evaluation on ModernVBERT, finds it outperforms text-only baselines, and deploys with production RAG after confirming accuracy gains.
Developing new visual retrieval models
Outcome: Downloads ViDoRe V3 datasets, uses human-verified annotations to train and evaluate a novel model, and publishes results against the leaderboard.
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 2026-09-01
Limitations
- ViDoRe is an open visual document retrieval benchmark and model suite for enterprise RAG.
- It comprises 10 datasets, 26,000+ pages, and 3,000+ queries across 6 languages, with human-verified annotations.
- Some datasets are private, which limits full reproducibility of results.
- The models may require significant computational resources to run.
as of 2026-09-01
Verification history
We have re-verified Vidore Benchmark 8 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 8 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
Individual researchers and small teams who need open access to the benchmark and models without a budget.
What this tier adds
Starting tier with no cost; includes open datasets, model weights, and leaderboard access.
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 is free and open-source, making it the most cost-effective benchmark for visual document retrieval — there are no licensing fees. Paid alternatives like enterprise RAG evaluation platforms charge for similar coverage, so it's ideal for startups and research teams.
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.
A team with GPU access can set up and run ViDoRe evaluations within a few hours. Model integration into a RAG pipeline takes a few days, depending on existing infrastructure.
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.
- ↗To BEIR: If your documents are plain text, switch to BEIR for text-only retrieval evaluation.
Integrations
Resources & Guides
- Resourcehuggingface.co
Vidore · Vidore Benchmark
Helpful link from huggingface.co
- 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
Tutorials & Learning
Tools that pair well with Vidore Benchmark
Common stack mates teams adopt alongside Vidore Benchmark, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Vidore Benchmark vs Screenplayiq
These tools serve completely different domains: ScreenplayIQ supports film industry professionals with data-driven script analysis and financial forecasting, while Vidore Benchmark targets technical teams building multi-modal document retrieval systems. Your choice depends solely on whether you need feedback for screenplays or an evaluation framework for document RAG pipelines.
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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DeepSeek-OCR
Open-source OCR model for dense math, tables, and multi-column documents with vision token compression.
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Frequently Asked Questions
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