compare-modelslisted

## Docs - Reference: <https://replicate.com/docs/llms.txt> - OpenAPI schema: <https://api.replicate.com/openapi.json> - MCP server: <https://mcp.replicate.com> - Per-model docs: `https://replicate.com/{owner}/{model}/llms.txt` - Set `Accept: text/markdown` when requesting docs pages for Markdown responses. ## Workflow 1. Search or browse collections to build a shortlist of candidate models. 2. Fetch each model's schema to compare inputs, outputs, and capabilities. 3. Check pricing from model metadata or the Replicate website. 4. Run a small batch of test predictions to compare output quality. 5. 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/tur