Best synthetic data generation tool
4 models · updated 2026-08-14
The verdict
MOSTLY AI leads — 2 of 4 models rank MOSTLY AI the top pick.
Not unanimous: Claude picks Gretel; Gemini picks Gretel.
As of 2026-08-14, ChatGPT, Claude, Gemini and Grok collectively rank MOSTLY AI #1 for synthetic data generation tool on ModelsAgree by aggregate score. The models' case: Best overall for privacy-safe tabular, relational, and time-series synthesis. The models' main caveat: Enterprise deployment and pricing are excessive for small teams wanting a lightweight local library. The strongest alternative is SDV — The strongest open-source option and default for tabular/relational synthesis — mature Python library (from MIT's DataCebo lineage) covering. Not unanimous: Claude picks Gretel; Gemini picks Gretel. Source: https://modelsagree.com/best/best-synthetic-data-generation-tool (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #1Claude #3Gemini #3Grok #1
Best overall for privacy-safe tabular, relational, and time-series synthesis; strong fidelity reports, conditional generation, rebalancing, differential privacy, connectors, and self-hosting make it unusually production-ready. Assumes the typical practitioner is synthesizing sensitive enterprise data; near-tied with Gretel.
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GPT Best overall for privacy-safe tabular, relational, and time-series synthesis; strong fidelity reports, conditional generation, rebalancing, differential privacy, connectors, and self-hosting make it unusually production-ready. Assumes the typical practitioner is synthesizing sensitive enterprise data; near-tied with Gretel.
Grok Highest real-world tabular/relational fidelity with automated quality reports measuring accuracy, correlations, distributions, coherence and privacy distances against holdouts; production deployments in regulated finance/telecom; open-sourced core SDK plus no-code path delivers usable synthetic data for ML training and sharing without memorization risk
Claude Best-in-class fidelity and privacy for enterprise structured/tabular data; strong accuracy on complex multi-table datasets, solid built-in privacy metrics and regulatory-grade documentation, and it open-sourced its core generation engine in 2025, easing the vendor-lock objection. Popular in insurance/banking.
Gemini Gold standard for statistical fidelity and automated privacy preservation in complex multi-table relational schemas and behavioral time-series datasets.
Where it falls shortper GPT Enterprise deployment and pricing are excessive for small teams wanting a lightweight local library.
per Claude Enterprise-oriented and structured-data-focused; overkill and cost-heavy for individuals, and weaker for unstructured/text/image needs.
per Gemini Specialized strictly in structured tabular and relational data, offering virtually no utility for unstructured text, NLP pipelines, or computer vision modalities.
per Grok Not for greenfield schema invention or unstructured/text-heavy workloads and remains enterprise-priced beyond free tier
- 2GPT #3Claude #2Gemini #2Grok #3
The strongest open-source option and default for tabular/relational synthesis — mature Python library (from MIT's DataCebo lineage) covering single-table (CTGAN, TVAE, GaussianCopula), multi-table, and sequential data, plus the SDMetrics evaluation suite for quality diagnostics; free, self-hostable, no data leaves your environment.
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Claude The strongest open-source option and default for tabular/relational synthesis — mature Python library (from MIT's DataCebo lineage) covering single-table (CTGAN, TVAE, GaussianCopula), multi-table, and sequential data, plus the SDMetrics evaluation suite for quality diagnostics; free, self-hostable, no data leaves your environment.
Gemini The benchmark open-source ecosystem for tabular, multi-table relational, and time-series synthesis (featuring CTGAN, TVAE, and Copulas), offering complete algorithmic transparency, Python extensibility, and zero licensing overhead.
GPT Strongest practitioner-controlled option: an accessible Python ecosystem with multiple tabular, sequential, and relational synthesizers, constraints, conditional sampling, and excellent SDMetrics evaluation tooling; exceptional value for experimentation and reproducible workflows.
Grok Most mature open-source ecosystem (GaussianCopula, CTGAN, TVAE, multi-table, sequential) with SDMetrics for fidelity/privacy evaluation, reversible transforms and active maintenance; free community edition gives full control and no vendor lock for practitioners who can code
Where it falls shortper GPT The best scalable multi-table and differential-privacy capabilities require commercial bundles, while community multi-table modeling remains limited.
per Claude Library-not-platform — you own tuning, scaling, and privacy guarantees yourself; deep DP and managed governance aren't first-class, and large-scale/high-fidelity models need real ML effort.
per Gemini Requires dedicated ML engineering expertise to tune hyperparameters, audit privacy leakage manually, and scale compute across massive databases without managed UI workflows.
per Grok Requires more engineering effort to reach production-grade privacy guarantees and multi-table scale than the commercial platforms; enterprise features are paid
- 3GPT #2Claude #1Gemini #1Grok —
The most mature commercial synthetic-data platform for the typical practitioner — tabular, relational (multi-table with referential integrity), and text generation via managed APIs and SDK, with built-in privacy (differential privacy options) and a quality/privacy scoring report that most alternatives lack; strong fit for regulated tabular data (finance, healthcare). Acquisition by NVIDIA (2025) deepened GPU/enterprise backing.
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Claude The most mature commercial synthetic-data platform for the typical practitioner — tabular, relational (multi-table with referential integrity), and text generation via managed APIs and SDK, with built-in privacy (differential privacy options) and a quality/privacy scoring report that most alternatives lack; strong fit for regulated tabular data (finance, healthcare). Acquisition by NVIDIA (2025) deepened GPU/enterprise backing.
Gemini Best-in-class multi-modal API and SDK support spanning tabular, relational, and unstructured text with integrated privacy evaluation metrics, differential privacy guarantees, and turnkey LLM-assisted augmentation.
GPT Broad, developer-friendly platform spanning tabular, relational, time-series, and text data, with strong APIs, connectors, automated evaluation, privacy controls, and cloud or hybrid deployment. Near-tied with MOSTLY AI and preferable for API-centric mixed-data pipelines.
Where it falls shortper GPT Cost and platform complexity make it poor value for modest tabular experiments.
per Claude Commercial and usage-priced; not the pick for someone who wants a fully self-hosted, no-vendor free tool or who only needs a quick one-off generator.
per Gemini High enterprise pricing and usage-based cloud models make it excessive and cost-prohibitive for simple local test mock generation or strictly air-gapped offline environments.
- 4GPT #4Claude #5Gemini #4Grok #2
Strongest practical coverage for typical engineering/ML teams via Structural (production-data de-id with referential integrity), Fabricate (from-scratch relational generation with agentic schema planning) and Textual; direct DB connectors and free tiers make high-fidelity test + training data immediately usable in CI/CD and staging
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Grok Strongest practical coverage for typical engineering/ML teams via Structural (production-data de-id with referential integrity), Fabricate (from-scratch relational generation with agentic schema planning) and Textual; direct DB connectors and free tiers make high-fidelity test + training data immediately usable in CI/CD and staging
GPT Excels at generating coherent test data from prompts, schemas, or samples, including relational databases, JSON, documents, mock APIs, validation loops, and automated workflows; especially effective for application development and QA.
Gemini Unmatched practical utility for software engineering and QA teams needing realistic, referentially intact database synthesis, subsetting, and masking across production-grade DB engines.
Claude Best for realistic synthetic data derived from production databases for dev/test/staging — strong de-identification, subsetting, and referential integrity across relational schemas, with good CI/database integration; practical value for engineering teams needing safe test data rather than ML training sets.
Where it falls shortper GPT It is less compelling than the leaders when the primary goal is statistically rigorous reproduction of sensitive datasets for analytics or ML.
per Claude Oriented to test-data provisioning, not high-fidelity ML training generation; commercial, and less suited to pure model-training or unstructured data.
per Gemini Designed around realistic data provisioning and de-identification for testing rather than generating novel generative data distributions for training machine learning models.
per Grok Less specialized in pure statistical privacy guarantees or pure generative fidelity metrics than dedicated synthesizers when the goal is model training on sensitive records alone
- 5GPT #5Claude —Gemini #5Grok #4
Integrated data-centric workflow (profiling + quality assessment before synthesis) with
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Grok Integrated data-centric workflow (profiling + quality assessment before synthesis) with
GPT Combines data profiling and preparation with guided tabular, time-series, and relational synthesis through both UI and SDK, giving data teams a practical end-to-end workflow rather than a bare generator.
Gemini Effectively integrates synthetic data generation with automated data quality profiling, bias mitigation, and time-series augmentation directly into machine learning pipelines.
Where it falls shortper GPT Its ecosystem, deployment breadth, and independent validation are less mature than those of the higher-ranked options.
per Gemini Lacks the deep native database-mimicking toolset of dedicated dev test tools and the multimodal versatility of broader generative platforms.
- 6GPT —Claude #4Gemini —Grok —
The leading choice for synthetic visual/3D data — physically accurate rendered images with automatic pixel-perfect labels (bounding boxes, segmentation, depth) for computer-vision and robotics training, with domain randomization at scale; unmatched for perception pipelines.
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Claude The leading choice for synthetic visual/3D data — physically accurate rendered images with automatic pixel-perfect labels (bounding boxes, segmentation, depth) for computer-vision and robotics training, with domain randomization at scale; unmatched for perception pipelines.
Where it falls shortper Claude Narrow to CV/3D and requires 3D assets plus GPU/engineering investment — irrelevant to anyone doing tabular or text synthesis.
By use case
How this board's leaders rank when the same four models are asked a more specific question.
| Product | This board | Tabular Platforms for Privacy-Preserving Machine Learning | platform for ML |
|---|---|---|---|
| MOSTLY AI | #1 | #1 | #1 |
| SDV | #2 | #2 | #3 |
| Gretel | #3 | #3 | #2 |
| Tonic | #4 | #6 | #4 |
| YData | #5 | #4 | — |
Rank history
Just missed the top 5
GPT SAS Data Maker — credible enterprise, multi-table, time-series, and differential-privacy capabilities, but Azure-centric delivery and enterprise overhead reduce typical-practitioner value · Syntho — solid privacy-focused database synthesis and de-identification, but offers less differentiation and practitioner ecosystem depth than the top five
Claude YData — ydata-synthetic/Fabric — strong open-source + platform for tabular, but narrower reach and mindshare than SDV/Gretel · Faker — ubiquitous and trivial for schema-shaped fake records, but purely rule-based with no statistical fidelity, so it's not real synthetic data for ML
Gemini Hazy — strong multi-table relational synthesis for banking, but missed due to heavy consultative sales friction and lack of self-serve developer tooling
By model
ChatGPT
- 1.MOSTLY AI
- 2.Gretel
- 3.SDV
- 4.Tonic
- 5.YData
Claude
- 1.Gretel
- 2.SDV
- 3.MOSTLY AI
- 4.NVIDIA Omniverse Replicator
- 5.Tonic
Gemini
- 1.Gretel
- 2.SDV
- 3.MOSTLY AI
- 4.Tonic
- 5.YData
Grok
- 1.MOSTLY AI
- 2.Tonic
- 3.SDV
- 4.YData
Common questions
What is the best synthetic data generation tool according to AI models?
MOSTLY AI leads. 2 of 4 models rank MOSTLY AI the top pick. The current top 3: MOSTLY AI, SDV, Gretel. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-08-14. Source: modelsagree.com.
Which synthetic data generation tool did each AI model pick first?
ChatGPT: MOSTLY AI. Claude: Gretel. Gemini: Gretel. Grok: MOSTLY AI.
Do the AI models agree on the best synthetic data generation tool?
Not unanimous. Claude picks Gretel; Gemini picks Gretel.
What changed in the latest synthetic data generation tool ranking?
In the latest poll (2026-08-14): MOSTLY AI climbed 1 spot, SDV climbed 2 spots; Gretel dropped 2 spots, Tonic dropped 1 spot; NVIDIA Omniverse Replicator entered the ranking. The models are re-polled on demand, so this ranking moves.
How is this synthetic data generation tool ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
Cite this ranking
ModelsAgree, “Best synthetic data generation tool” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-08-14. https://modelsagree.com/best/best-synthetic-data-generation-tool (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand