Docs
Workflow
- Search or browse collections to build a shortlist of candidate models.
- Fetch each model's schema to compare inputs, outputs, and capabilities.
- Check pricing from model metadata or the Replicate website.
- Run a small batch of test predictions to compare output quality.
- Pick the model that best fits your constraints (cost, latency, quality).
What to compare
- Speed: Check
metrics.predict_time on completed predictions for actual inference time. Official models are always warm. Community models can cold-boot.
- Cost: Official models have predictable per-run pricing. Community models charge by compute time (GPU-seconds). Run a few predictions and check the
metrics field for actual cost.
- Quality: Run the same prompts through each model and compare outputs. Quality is subjective. Match it to your use case, not a leaderboard.
- Capabilities: Compare input schemas for supported features (reference images, masks, aspect ratios, streaming, multi-image input). Check output formats.
Key tradeoffs
- Lowest cost: smaller/distilled models. Accept slower inference and lower quality.
- Lowest latency: official models or schnell/turbo variants. Accept higher cost per run.
- Highest quality: pro/max/quality variants. Accept slower inference and higher cost.
- Most control: models with ControlNet, masks, or reference images. Accept more complex input setup.
- Official models: always warm, stable APIs, predictable pricing, maintained by Replicate.
- Community models: may cold-boot, require version pinning, maintained by the author.
- If a community model meets your needs and an official model doesn't, consider creating a deployment for consistent uptime.
Prompting guidance
For prompting techniques and task-specific guidance: