OrcaRouter
Operated by Continuum AI Corp.
4 model routes currently listed at zero price.
https://api.orcarouter.ai/v1Models mentioned
deepseek/deepseek-v4-flash-freetencent/hy3-freez-ai/glm-5.3-flash-freeorca/orcaverify-text1.0-freeEquivalent paid value
How this was valued: The published allowance or a defensible paid comparison is not precise enough to calculate.
Free request limits conflict across first-party surfaces and the free aliases do not have stable like-for-like paid rates.
Limits and terms
- Published General Numeric Limits
- No
- Exhausted Behavior
- HTTP 429 or free-quota-exhausted; the wallet is not charged by the free router.
- Recent Announcement Snapshot
- Requests Per Minute
- 10
- Requests Per Day
- 50
- After 20 USD Cumulative Workspace Payments
- Requests Per Minute
- 20
- Requests Per Day
- 1,000
- Caveat
- The launch announcement, localized offer page, and live catalog differed during the 2026-08-21 audit; prefer the signed-in offer/account state.
What happens to your prompts?
OrcaRouter says it does not retain or train on content, but independently governed upstream providers receive the requests.
- Plan Scope
- OrcaRouter gateway; upstream provider policies separately govern routed processing.
- Prompt Retention
- OrcaRouter states prompt and output content is processed in transit and not logged, stored, or retained by OrcaRouter.
- Response Retention
- Not retained by OrcaRouter.
- Ordinary Logging
- Request metadata needed for operation, security, and billing is retained without prompt or output content.
- Model Training
- OrcaRouter states it does not train models on user content.
- Product Improvement
- Performance and reliability improvement uses metadata rather than prompt or output content.
- Human Or Operator Access
- OrcaRouter states it keeps no content copy; the selected upstream provider necessarily processes the request under its own controls.
- Subprocessors And Routing
- Prompts and responses are disclosed to upstream LLM providers such as OpenAI, Anthropic, Google, Together AI, and Groq; their terms and privacy policies apply.
- Deletion Controls
- Users may delete accounts and exercise privacy rights; metadata follows the published retention schedule and legal exceptions.
- Caveat
- OrcaRouter's no-retention promise covers its gateway, not the independently governed upstream provider.
Governing documents
- Terms Of Service
- https://www.orcarouter.ai/terms.html
- Privacy Policy
- https://www.orcarouter.ai/privacy.html
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
- Free Models May Require Claim
- Yes
Primary sources
Before you build with OrcaRouter
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
This snapshot records 4 model IDs. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. The recorded base endpoint is https://api.orcarouter.ai/v1, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
An account is required, so public catalog evidence cannot by itself prove the limits, balance, or billing behavior attached to your workspace. The reviewed evidence explicitly says no payment card is required. Re-check the signup flow because eligibility and billing controls can change after the snapshot. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Partially private”: OrcaRouter says it does not retain or train on content, but independently governed upstream providers receive the requests. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 9 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.