Model Matrix & Project Recommender
Compare the four StackAI quality tiers across synthetic-data-specific dimensions — diversity, verbosity, creativity, complexity, structured output reliability, and cost efficiency — then get a deterministic recommendation for your dataset workflow.
Last updated: 2026-04-13
How these models were scored
This is not a generic leaderboard. The matrix is tuned for synthetic data generation work: instruction, preference, eval, and conflict dataset creation. Scores use a transparent ordinal scale — Excellent, Strong, Limited, Not supported — and diversity, near-dup rate, and average output length come from actual 1,000-record pilot runs.
- All four tiers are available on StackAI today. No bait-and-switch to models you can't actually run.
- Recommendations are rules-based and deterministic for v1 — not generated by a hidden model.
- Fast tier is excluded from preference and conflict recommendations because the quality floor for paired outputs is too low.
- Benchmarks source: Cost Pilot v2 (March 2026), 1,000-record jobs across 10 domains per tier.
The four tiers
Each card shows pricing, pilot benchmarks, and a real sample record so you can see the depth difference at a glance.
Fast
FastestGPT-4o Mini · OpenAI
Best default for fast, budget-sensitive instruction and eval generation.
Diversity
80%
Output depth
612 chars
Near-dup
20%
Price per 1K records
Ideal when you need throughput and cost efficiency more than nuance. Not available for preference or conflict schemas — the quality floor for paired outputs is too low.
Balanced
Best valueGPT-4.1 Mini · OpenAI
Production workhorse. Highest diversity and lowest near-dup rate per dollar across all four tiers.
Diversity
94%
Output depth
600 chars
Near-dup
6%
Price per 1K records
The default recommendation for most production fine-tuning jobs. 94% diversity at $3/1K is the strongest value in the lineup and the cost-per-quality winner for instruction and eval workloads.
Diverse
Highest diversityGPT-5.4 Mini · OpenAI
Highest diversity at scale. Rich scenario-based outputs with near-zero near-duplicate rate.
Diversity
98%
Output depth
1.1K chars
Near-dup
2%
Price per 1K records
Pick this when you need broad coverage or your domain is complex enough that you want longer, more scenario-driven outputs. 98% diversity and 2% near-dup rate — the best diversity numbers in the lineup by a wide margin.
Deep
Deepest analysisPAYG onlyClaude Sonnet 4.6 · Anthropic
Maximum per-record depth. Textbook-level analysis, averaging ~1,955 characters per output.
Diversity
82%
Output depth
2.0K chars
Near-dup
18%
Price per 1K records
PAYG-only. Best when record depth matters more than raw throughput — nuanced preference pairs, conflict scenarios, or long-form reasoning examples. Slower and pricier, but unmatched for hard synthetic data tasks.
Comparison matrix
The matrix emphasizes synthetic-data-specific tradeoffs. On mobile the table stays horizontally scrollable rather than collapsing dimensions away.
| Category | Fast GPT-4o Mini | Balanced GPT-4.1 Mini | Diverse GPT-5.4 Mini | Deep Claude Sonnet 4.6 |
|---|---|---|---|---|
| Behavioral Characteristics | ||||
Diversity How well the model produces varied examples without collapsing into repetitive patterns. | Strong | Excellent | Excellent | Strong |
Verbosity Control How reliably the model matches the intended response length and level of detail. | Strong | Strong | Strong | Limited |
Creativity How well the model generates novel, less templated examples when variation matters. | Limited | Strong | Excellent | Excellent |
Instruction Following How reliably the model obeys prompt constraints, formatting rules, and generation requirements. | Strong | Excellent | Excellent | Excellent |
Consistency How stable and predictable outputs are across similar prompts and batches. | Strong | Excellent | Strong | Strong |
| Quality Dimensions | ||||
Complexity Handling How well the model sustains layered, detailed, nuanced, multi-part examples without flattening them. | Limited | Strong | Excellent | Excellent |
Structured Output How well the model preserves schema shape, fields, and formatting expectations. | Strong | Excellent | Excellent | Strong |
Output Quality Overall coherence, usefulness, and polish of the generated records. | Limited | Strong | Excellent | Excellent |
Hard-Negative Generation How well the model can produce plausible but flawed, challenging, or contrastive examples. | Limited | Strong | Excellent | Excellent |
| Dataset Fit | ||||
Instruction Dataset Fit Suitability for generating instruction_v1 supervised fine-tuning datasets. | Strong | Excellent | Excellent | Excellent |
Preference Dataset Fit Suitability for generating preference_v1 RLHF/DPO datasets. | Not supported | Strong | Excellent | Excellent |
Eval Dataset Fit Suitability for generating eval_v1 benchmark datasets. | Strong | Excellent | Excellent | Strong |
Conflict Dataset Fit Suitability for generating conflict_v1 alignment decision datasets. | Not supported | Strong | Excellent | Excellent |
| Operational | ||||
Speed Relative generation speed and responsiveness for iterative dataset work. | Excellent | Strong | Strong | Limited |
Cost Efficiency Relative value for budget-sensitive synthetic data generation. | Excellent | Excellent | Strong | Limited |
Project recommender
Describe your project constraints. StackAI returns a best-fit model plus a backup option, with concrete reasons and tradeoffs.
Best fit
Diverse · GPT-5.4 Mini
Highest diversity at scale. Rich scenario-based outputs with near-zero near-duplicate rate.
Backup option
Balanced · GPT-4.1 Mini
Production workhorse. Highest diversity and lowest near-dup rate per dollar across all four tiers.
Ready to generate synthetic data?
Pick a model from the matrix, then head to StackAI to generate instruction, preference, eval, or conflict datasets.