---
modelA: "TabICLv2"
modelB: "EXAONE Tabular"
canonicalSlug: "tabiclv2-vs-exaone-tabular"
winsA: 6
winsB: 13
ties: 2
winRateA: "28.6%"
winRateB: "61.9%"
eloA: "932 (±62)"
eloB: "1021 (±80)"
avgLatencyA: "4.26"
avgLatencyB: "158.67"
canonicalUrl: "https://carlahq.eu/datasets/comparison/tabiclv2-vs-exaone-tabular"
---

# TabICLv2 vs EXAONE Tabular Tabular Benchmark Comparison

> Empirical head-to-head evaluation across Carla's open-source tabular foundation model benchmark suite.

## Head-to-Head Scoreboard
| Metric | TabICLv2 | EXAONE Tabular | Comparison Result |
| :--- | :--- | :--- | :--- |
| **Head-to-Head Wins** | **6** (28.6%) | 13 (61.9%) | EXAONE Tabular leads (2 ties) |
| **Bradley-Terry Elo Rating** | **932 (±62)** | 1021 (±80) | Global tournament rating across repeated splits |
| **Improvability Regret** | 2.11% | 0.85% | Normalized average error against dataset optimal ceilings |
| **Average Inference Latency** | 4.26 | 158.67 | Measured per-split forward-pass timing |
| **Browser Execution** | Native WebGPU Browser Runtime (Carla Engine / ONNX Runtime WASM) | Server-side only (Requires Python runtime / PyTorch / CUDA) | — |
| **Open Source License** | Open Source (BSD-3-Clause) | Open Source (Apache 2.0) | — |

## Architectural Specifications
| Dimension | TabICLv2 | EXAONE Tabular |
| :--- | :--- | :--- |
| **Organization** | Inria SODA | LG AI Research |
| **Architecture** | In-Context Tabular Transformer with Prefix Attention & KV Caching | Multi-Task Columnar Autoregressive Tabular Transformer |
| **Pretraining Prior** | Synthetic Copula-based Quantile Priors & Prior.py DAGs | Hybrid pre-training on synthetic datasets and diverse real-world tabular corpuses |
| **Context Window** | 1,000+ context rows with zero fine-tuning required | Tokenized columnar context with sequence truncation |
| **Execution Engine** | Native WebGPU Browser Runtime (Carla Engine / ONNX Runtime WASM) | Server-side only (Requires Python runtime / PyTorch / CUDA) |
| **Repository** | [TabICLv2 Code](https://github.com/soda-inria/tabicl) | [EXAONE Tabular Code](https://github.com/LGAI-Research/EXAONE-Tabular) |
| **Paper / ArXiv** | [TabICLv2 Paper](https://arxiv.org/abs/2602.11139) | [EXAONE Tabular Paper](https://github.com/LGAI-Research/EXAONE-Tabular) |

## Dataset-by-Dataset Benchmark Results
| Dataset | Business Domain | Task Type | Metric | TabICLv2 Score | EXAONE Tabular Score | Delta (%) | Winner |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| [Abalone](/datasets/undefined.md) | Agriculture, Forestry & Fishing | `regression` | undefined | 2.03 | 2.03 | +0.0% | **EXAONE Tabular** |
| [Adult](/datasets/undefined.md) | Economics & Public Policy | `binary_classification` | undefined | 0.0793 | 0.0680 | +0.0% | **EXAONE Tabular** |
| [Airfoil Self Noise](/datasets/undefined.md) | UCI | `regression` | undefined | 1.12 | 1.16 | -0.0% | **TabICLv2** |
| [Amazon Employee Access](/datasets/undefined.md) | Information Technology & Enterprise Security | `binary_classification` | undefined | 0.1483 | 0.1232 | +0.0% | **EXAONE Tabular** |
| [Bank Marketing](/datasets/undefined.md) | Finance & Banking | `binary_classification` | undefined | 0.1970 | 0.1876 | +0.0% | **EXAONE Tabular** |
| [Blood Transfusion Service Center](/datasets/undefined.md) | Healthcare & Biomedicine | `binary_classification` | undefined | 0.2446 | 0.2480 | -0.0% | **TabICLv2** |
| [Breast W](/datasets/undefined.md) | Healthcare & Life Sciences | `binary_classification` | undefined | 0.0052 | 0.0054 | -0.0% | **TabICLv2** |
| [Car](/datasets/undefined.md) | Automotive & Fleet Management | `multiclass_classification` | undefined | 0.0231 | 0.0346 | -0.0% | **TabICLv2** |
| [Compas Two Years](/datasets/undefined.md) | Legal & Public Safety | `binary_classification` | undefined | 0.2701 | 0.2612 | +0.0% | **EXAONE Tabular** |
| [Credit G](/datasets/undefined.md) | Finance & Banking | `binary_classification` | undefined | 0.2016 | 0.1964 | +0.0% | **EXAONE Tabular** |
| [Diabetes](/datasets/undefined.md) | Healthcare & Biomedicine | `binary_classification` | undefined | 0.1632 | 0.1638 | -0.0% | **TabICLv2** |
| [Employee Salaries](/datasets/undefined.md) | Human Resources & Workforce Analytics | `regression` | undefined | 8,126 | 7,427 | +698.9% | **EXAONE Tabular** |
| [Fitness Club](/datasets/undefined.md) | Fitness, Sports & Recreation | `binary_classification` | undefined | 0.1795 | 0.1797 | -0.0% | **TabICLv2** |
| [House Sales](/datasets/undefined.md) | Real Estate & Property Valuation | `regression` | undefined | 111,524 | 99,520 | +12004.1% | **EXAONE Tabular** |
| [Houses](/datasets/undefined.md) | Real Estate & Urban Planning | `regression` | undefined | 40,971 | 39,971 | +1000.0% | **EXAONE Tabular** |
| [Monks Problems 2](/datasets/undefined.md) | Data Science & Artificial Intelligence | `binary_classification` | undefined | 0.0000 | 0.0000 | -0.0% | **Tie** |
| [Phoneme](/datasets/undefined.md) | Speech Processing & Acoustic Engineering | `binary_classification` | undefined | 0.0278 | 0.0276 | +0.0% | **EXAONE Tabular** |
| [Spambase](/datasets/undefined.md) | Cybersecurity & IT Infrastructure | `binary_classification` | undefined | 0.0075 | 0.0076 | -0.0% | **Tie** |
| [Telco Customer Churn](/datasets/undefined.md) | Telecommunications & Subscription Services | `binary_classification` | undefined | 0.1488 | 0.1484 | +0.0% | **EXAONE Tabular** |
| [Titanic](/datasets/undefined.md) | Maritime Safety & Actuarial Risk Analysis | `binary_classification` | undefined | 0.1229 | 0.1146 | +0.0% | **EXAONE Tabular** |
| [Vehicle](/datasets/undefined.md) | Automotive Engineering & Computer Vision | `multiclass_classification` | undefined | 0.2401 | 0.2393 | +0.0% | **EXAONE Tabular** |

## Canonical Resources
- **HTML Benchmark Page:** [https://carlahq.eu/datasets/comparison/tabiclv2-vs-exaone-tabular](https://carlahq.eu/datasets/comparison/tabiclv2-vs-exaone-tabular)
- **Carla Tabular Suite:** [https://carlahq.eu/datasets/](https://carlahq.eu/datasets/)