---
modelId: "tabpfn"
name: "TabPFN v3"
fullName: "TabPFN v3 (Prior Labs)"
org: "Prior Labs"
license: "Research & Non-Commercial"
elo: "936 (±65)"
rank: 3
browserExecution: "Server-side only (Requires Python runtime / CUDA / PyTorch)"
canonicalUrl: "https://carlahq.eu/datasets/models/tabpfn"
---

# TabPFN v3 (Prior Labs) Benchmark Profile

> Pioneering Prior-Data Fitted Network that approximates full Bayesian posterior inference over synthetic structural priors in a single forward pass without iterative training.

## Technical Specifications
| Dimension | Specification |
| :--- | :--- |
| **Global Leaderboard Rank** | **Rank #3** (Bradley-Terry Elo: 936 (±65)) |
| **Organization** | Prior Labs |
| **Architecture** | Prior-Data Fitted Network (PFN) with Causally Invariant Embeddings |
| **Pretraining Prior** | Bayesian structural equation models and synthetic Gaussian priors |
| **Context Window** | Fixed context window (typically 1k–10k training samples) |
| **In-Browser Execution** | Server-side only (Requires Python runtime / CUDA / PyTorch) |
| **Open-Source License** | Research & Non-Commercial |
| **Source Repository** | [TabPFN v3 GitHub](https://github.com/PriorLabs/tabpfn) |
| **Academic Paper** | [Research Paper](https://arxiv.org/abs/2207.01848) |

## Empirical Performance Across Benchmark Datasets
| Dataset | Task Type | Metric | Score | Latency |
| :--- | :--- | :--- | :--- | :--- |
| [Abalone](/datasets/undefined.md) | `regression` | undefined | **2.03** | — |
| [Adult](/datasets/undefined.md) | `binary_classification` | undefined | **0.0799** | — |
| [Airfoil Self Noise](/datasets/undefined.md) | `regression` | undefined | **1.02** | — |
| [Amazon Employee Access](/datasets/undefined.md) | `binary_classification` | undefined | **0.1542** | — |
| [Bank Marketing](/datasets/undefined.md) | `binary_classification` | undefined | **0.1883** | — |
| [Blood Transfusion Service Center](/datasets/undefined.md) | `binary_classification` | undefined | **0.2462** | — |
| [Breast W](/datasets/undefined.md) | `binary_classification` | undefined | **0.0056** | — |
| [Car](/datasets/undefined.md) | `multiclass_classification` | undefined | **0.0238** | — |
| [Compas Two Years](/datasets/undefined.md) | `binary_classification` | undefined | **0.2615** | — |
| [Credit G](/datasets/undefined.md) | `binary_classification` | undefined | **0.2031** | — |
| [Diabetes](/datasets/undefined.md) | `binary_classification` | undefined | **0.1607** | — |
| [Employee Salaries](/datasets/undefined.md) | `regression` | undefined | **8,308** | — |
| [Fitness Club](/datasets/undefined.md) | `binary_classification` | undefined | **0.1792** | — |
| [House Sales](/datasets/undefined.md) | `regression` | undefined | **102,039** | — |
| [Houses](/datasets/undefined.md) | `regression` | undefined | **39,523** | — |
| [Monks Problems 2](/datasets/undefined.md) | `binary_classification` | undefined | **0.0000** | — |
| [Phoneme](/datasets/undefined.md) | `binary_classification` | undefined | **0.0274** | — |
| [Spambase](/datasets/undefined.md) | `binary_classification` | undefined | **0.0085** | — |
| [Telco Customer Churn](/datasets/undefined.md) | `binary_classification` | undefined | **0.1486** | — |
| [Titanic](/datasets/undefined.md) | `binary_classification` | undefined | **0.1360** | — |
| [Vehicle](/datasets/undefined.md) | `multiclass_classification` | undefined | **0.2263** | — |