The 70% factuality ceiling: why Google’s recent ‘FACTS’ benchmark is a wake-up call for enterprise AI

There's no shortage of generative AI benchmarks designed to measure the performance and accuracy of a given model on completing various helpful enterprise tasks — from coding to instruction following to agentic web browsing and tool use. But lots of these benchmarks have one major shortcoming: they measure the AI's ability to finish specific problems and requests, not how factual the model is in its outputs — how well it generates objectively correct information tied to real-world data — especially when coping with information contained in imagery or graphics.

For industries where accuracy is paramount — legal, finance, and medical — the shortage of a standardized method to measure factuality has been a critical blind spot.

That changes today: Google’s FACTS team and its data science unit Kaggle released the FACTS Benchmark Suite, a comprehensive evaluation framework designed to shut this gap.

The associated research paper reveals a more nuanced definition of the issue, splitting "factuality" into two distinct operational scenarios: "contextual factuality" (grounding responses in provided data) and "world knowledge factuality" (retrieving information from memory or the online).

While the headline news is Gemini 3 Pro’s top-tier placement, the deeper story for builders is the industry-wide "factuality wall."

In response to the initial results, no model—including Gemini 3 Pro, GPT-5, or Claude 4.5 Opus—managed to crack a 70% accuracy rating across the suite of problems. For technical leaders, it is a signal: the era of "trust but confirm" is much from over.

Deconstructing the Benchmark

The FACTS suite moves beyond easy Q&A. It consists of 4 distinct tests, each simulating a special real-world failure mode that developers encounter in production:

1. Parametric Benchmark (Internal Knowledge): Can the model accurately answer trivia-style questions using only its training data?

2. Search Benchmark (Tool Use): Can the model effectively use an online search tool to retrieve and synthesize live information?

3. Multimodal Benchmark (Vision): Can the model accurately interpret charts, diagrams, and pictures without hallucinating?

4. Grounding Benchmark v2 (Context): Can the model stick strictly to the provided source text?

Google has released 3,513 examples to the general public, while Kaggle holds a personal set to stop developers from training on the test data—a standard issue referred to as "contamination."

The Leaderboard: A Game of Inches

The initial run of the benchmark places Gemini 3 Pro within the lead with a comprehensive FACTS Rating of 68.8%, followed by Gemini 2.5 Pro (62.1%) and OpenAI’s GPT-5 (61.8%).Nonetheless, a better have a look at the info reveals where the true battlegrounds are for engineering teams.

Data sourced from the FACTS Team release notes.

For Builders: The "Search" vs. "Parametric" Gap

For developers constructing RAG (Retrieval-Augmented Generation) systems, the Search Benchmark is probably the most critical metric.

The information shows an enormous discrepancy between a model's ability to "know" things (Parametric) and its ability to "find" things (Search). As an illustration, Gemini 3 Pro scores a high 83.8% on Search tasks but only 76.4% on Parametric tasks.

This validates the present enterprise architecture standard: don’t depend on a model's internal memory for critical facts.

Should you are constructing an internal knowledge bot, the FACTS results suggest that hooking your model as much as a search tool or vector database just isn’t optional—it’s the only method to push accuracy toward acceptable production levels.

The Multimodal Warning

Probably the most alarming data point for product managers is the performance on Multimodal tasks. The scores listed below are universally low. Even the category leader, Gemini 2.5 Pro, only hit 46.9% accuracy.

The benchmark tasks included reading charts, interpreting diagrams, and identifying objects in nature. With lower than 50% accuracy across the board, this means that Multimodal AI just isn’t yet ready for unsupervised data extraction.

Bottom line: In case your product roadmap involves having an AI routinely scrape data from invoices or interpret financial charts without human-in-the-loop review, you’re likely introducing significant error rates into your pipeline.

Why This Matters for Your Stack

The FACTS Benchmark is prone to change into a typical reference point for procurement. When evaluating models for enterprise use, technical leaders should look beyond the composite rating and drill into the precise sub-benchmark that matches their use case:

• Constructing a Customer Support Bot? Have a look at the Grounding rating to make sure the bot sticks to your policy documents. (Gemini 2.5 Pro actually outscored Gemini 3 Pro here, 74.2 vs 69.0).

• Constructing a Research Assistant? Prioritize Search scores.

• Constructing an Image Evaluation Tool? Proceed with extreme caution.

Because the FACTS team noted of their release, "All evaluated models achieved an overall accuracy below 70%, leaving considerable headroom for future progress."For now, the message to the industry is evident: The models are getting smarter, but they aren't yet infallible. Design your systems with the belief that, roughly one-third of the time, the raw model might just be fallacious.

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