Multi-model API router
How to use
OpenRouter.
Comparing and calling many AI models through one interface, aggregating billing, and routing across providers for availability, cost, performance, or privacy.
OpenRouter is a unified API and routing platform for AI models. Comparing and calling many AI models through one interface, aggregating billing, and routing across providers for availability, cost, performance, or privacy. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to OpenRouter
Get into the official OpenRouter experience with the right account and a setup you understand.
- 01
Start at https://openrouter.ai/ and confirm the domain before entering account or payment information.
- 02
Availability: Web model catalog and chatroom plus API, client SDK, and agent SDK access for developers.
- 03
An OpenRouter account and API key, funded credits or eligible free models, and an application capable of calling its API or compatible SDK.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 05
Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
First session
Your first useful OpenRouter session
Learn the interaction loop using a small task with a clear outcome.
- 01
Create a narrowly scoped API key and choose one documented model that matches the task.
- 02
Send a minimal request through the official API or SDK and capture response, usage, model, and provider metadata.
- 03
Review privacy settings and provider policies before sending real or sensitive application data.
- 04
Add explicit timeouts, error handling, cost limits, and only capability-compatible fallbacks.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
Quality control
Check the quality of OpenRouter output
Establish that a model's output is reliable enough for the job you are handing it.
- 01
Restate the task, the acceptance criteria, and the failure cost before evaluating any response.
- 02
Test with a fixed evaluation set rather than ad-hoc prompts, so changes are measurable across model versions.
- 03
Check determinism and drift: record model version, parameters, and date, because providers update models underneath you.
- 04
Probe the failure modes deliberately — ambiguous inputs, adversarial phrasing, and out-of-scope requests.
- 05
Validate structure as well as content: confirm schemas, types, and required fields survive real inputs.
- Pin and record the model version with every result.
- Evaluate against a fixed set, not one-off prompts.
- Validate output structure before it reaches downstream code.
Privacy & permissions
Use OpenRouter safely
Understand what leaves your system on every call, and what the provider keeps.
- 01
Classify the data in each request before it is sent to a hosted model.
- 02
Check the provider's retention and training terms for your specific plan, not the marketing page.
- 03
Store API keys in a secret manager, scope them per project, and rotate them on a schedule.
- 04
Strip or tokenise personal and regulated data before it reaches the request body.
- 05
Set rate limits, spend caps, and monitoring so a runaway loop is bounded and visible.
- OpenRouter proxies requests to model providers whose behavior and policies differ. Enforce privacy and provider constraints, protect API keys, monitor actual routing and cost, and test fallback outputs.
- Follow your organisation's approved-use policy.
- Never treat fluent output as authorization to act.
Core workflows
Step-by-step ways to use OpenRouter for the work it does best.
Each workflow below has its own outcome, steps and review checkpoints.
Compare candidate models with a repeatable task set before committing production traffic.
↓ 02 WorkflowConfigure resilient and private OpenRouter routingRoute requests across providers while enforcing explicit fallback, performance, and retention requirements.
↓Workflow 01
Choose and evaluate models through OpenRouter
Compare candidate models with a repeatable task set before committing production traffic.
Outcome: Select a model and provider policy based on measured quality, cost, latency, and privacy.
- 01
Define representative prompts, expected outputs, safety cases, context sizes, and a scoring rubric.
- 02
Shortlist models that support the required modalities, tools, structured output, region, and context window.
- 03
Run identical tests with fixed parameters and record quality, latency, token usage, errors, and effective cost.
- 04
Inspect provider data policies and repeat tests with the routing and privacy constraints planned for production.
- 05
Choose a primary and fallback set, document failure thresholds, and rerun the evaluation when models change.
What good use looks like
- Use several realistic test cases.
- Measure provider behavior, not only model labels.
- Version the evaluation set.
Workflow 02
Configure resilient and private OpenRouter routing
Route requests across providers while enforcing explicit fallback, performance, and retention requirements.
Outcome: Keep an AI feature available without silently weakening its privacy or quality guarantees.
- 01
Classify request data and define allowed providers, regions, retention, training, latency, and price boundaries.
- 02
Set account privacy controls and enforce Zero Data Retention per request where the workload requires it.
- 03
Configure provider order or performance preferences and a small compatible fallback model list.
- 04
Test unavailable providers, rate limits, timeouts, malformed output, and privacy-policy mismatch failures.
- 05
Log model, provider, latency, usage, and error metadata without storing sensitive prompt content.
What good use looks like
- Make privacy constraints fail closed.
- Keep fallbacks capability-compatible.
- Monitor the provider actually used.
Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.