TabFM Models by Google
1 model2026
Details
ResearcherGoogle
LicenseNoncommercial
Commercial useCommercial use: non-commercial
Models1
Released2026
About
TabFM is Google's tabular foundation model for zero-shot classification and regression on structured tables. It frames tabular prediction as an in-context learning problem, using training examples and target rows as a unified prompt so users can generate predictions in a single forward pass without per-dataset model training, hyperparameter tuning, or manual feature engineering. Google Research released TabFM with public GitHub code and downloadable pretrained weights on Hugging Face, and says BigQuery integration via AI.PREDICT is planned.
Current Variants
Use-when guidance is based on each model's tracked capabilities, context window, release date, and replacement status.
1 in view
| Model | Use when | Released | Signals | Status |
|---|---|---|---|---|
| TabFM 1.0.0 | Use when the workload needs tabular. | 2026-06 | tabular | Current |
Release Timeline
1 release group2026-06
1 current
TabFM 1.0.0
Currenttabular
Specifications(1 models)
| Model | Released |
|---|---|
| TabFM 1.0.0 | 2026-06 |
Frequently Asked Questions
- What is TabFM used for?
- TabFM is used for tabular, coding, and structured outputs. The family description and listed model capabilities point to those workloads as the best fit.
- How does TabFM compare to Google Cloud Text-to-Speech?
- TabFM by Google is strongest where you need tabular, while Google Cloud Text-to-Speech by Google is the closest related family to check for audio. TabFM has 1 listed variant, so compare the specs and pricing tables before choosing a production model.
- Which TabFM model should I use?
- If price is the main constraint, use the pricing table first because TabFM does not have complete provider pricing in the local data. For the most capable/latest local choice, evaluate TabFM 1.0.0.