Skip to content
All Skills

Compare Models

Compare Replicate models by cost, speed, quality, and capabilities.

Data, AI & Research|v1|Updated 7/14/2026|GitHub source
MCP get_skill({ skillId: "compare-models-978d2a69" })

Use this skill with your agent

Create a free account and connect via MCP

Get Started Free
## 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/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 vs community models

- 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:

- Image generation and editing: see the [prompt-images](../prompt-images/SKILL.md) skill.
- Video generation: see the [prompt-videos](../prompt-videos/SKILL.md) skill.
#ml-inference#llm#evaluationreplicate

Related Skills

More skills in Data, AI & Research

Ablation Planner

Use when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.

#broad-capability#wanshuiyin-arisMIT

Ablation Planner

Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.

#broad-capability#wanshuiyin-arisMIT

About

Provides information about the bitwize-music plugin, its version, and its creator. Use when the user asks about the plugin, its purpose, version, or capabilities.

#github#broad-capabilityCC0-1.0

Ab Test Analysis

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.

#work-life#productivityMIT

Academic Search

Search and analyze academic literature. Find papers, understand research methodologies, and synthesize academic findings for research projects.

#work-life#officeMIT

Adaptyv

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

#broad-capability#scienceMIT