| name | find-models |
| description | Find AI models on Replicate using search and curated collections. |
The AI model landscape changes weekly. New models ship constantly and older ones are deprecated or surpassed. Don't rely on model names you've seen before, including names from past conversations or training data. A specific model you "know" may no longer be the best choice, may be slower than newer alternatives, or may not exist anymore.
Always start by querying the Replicate API. Use search and collections to discover what's currently available, then read schemas to understand inputs and outputs before running anything.
https://replicate.com/{owner}/{model}/llms.txtAccept: text/markdown when requesting docs pages for Markdown responses.GET /v1/search?query=...) to find models by task. Returns models, collections, and docs.tags, generated_description, and run_count.official collection contains always-warm models with stable APIs and predictable pricing.GET /v1/collections. Get one by slug with GET /v1/collections/{slug}.GET /v1/models/{owner}/{name}).model.latest_version.openapi_schema.components.schemas.Input.propertiestype, description, default, minimum/maximum, enum, format (e.g. uri for file inputs).image-generation, video, audio, etc.).owner/name format (e.g. owner/model-name). Routes to the latest version automatically.owner/name:version_id. You must pin a specific version. Community models can cold-boot and take time to start.Prompting techniques for AI image generation and editing models on Replicate. Use when writing prompts for image models or building image generation features.
Prompting techniques for AI video generation models on Replicate. Use when writing prompts for video models or building video generation features.
Package and build custom AI models with Cog for deployment on Replicate. Use when creating a cog.yaml or predict.py, defining model inputs and outputs, loading model weights at setup time, building Docker images for ML models, serving locally with cog serve or cog predict, or porting a HuggingFace, GitHub, or ComfyUI model to run on Replicate. Trigger on phrases like "build a model", "package a model", "create a Cog model", "wrap a model", "containerize an AI model", "predict.py", "cog.yaml", "BasePredictor", or "Cog container", and when referencing cog.run, github.com/replicate/cog, or github.com/replicate/cog-examples. Covers GPU and CUDA setup, pget for fast weight downloads, async predictors with continuous batching, streaming outputs, and cold-boot optimization for image, video, audio, and LLM models. For pushing built models to Replicate, see publish-models. For running existing models, see run-models.
Push and publish custom AI models to Replicate, and set up CI/CD for releasing new model versions safely. Use when running cog push, deploying a model to Replicate, releasing a new version, validating a model with cog-safe-push before publishing, configuring a Replicate deployment, setting up GitHub Actions for model releases, or porting a community model to an official one. Trigger on phrases like "push a model to Replicate", "publish a model", "deploy a model", "release a new version", "cog push", "cog-safe-push", "model CI", "r8.im", or "schema compatibility", and when referencing github.com/replicate/cog-safe-push or github.com/replicate/model-ci-template. Covers cog push, the full cog-safe-push config (test cases, fuzz, deployment, official_model), GitHub Actions patterns, multi-model matrix pushes, and post-publish monitoring. Assumes you already have a working Cog project; see build-models if you need to package one first.
Compare Replicate models by cost, speed, quality, and capabilities.
Run AI models on Replicate via predictions, webhooks, and streaming.