--- title: "Arnict free inference" description: "$5 in credits refresh on a published schedule. See verified limits, model IDs, privacy terms, and primary sources for Arnict." canonical_url: "https://freeinferencing.com/provider/arnict/" md_url: "https://freeinferencing.com/provider/arnict.md" last_updated: "2026-09-19" --- # Arnict > $5 in credits refresh on a published schedule. ## Classification - **Directory:** Current - **Free access:** Monthly credits - **Status:** Available now - **Confidence:** High - **Payment card:** No - **Account:** Required - **Equivalent paid value:** $5.00/mo - **API endpoint:** `https://api.arnict.com/v1` ## Models mentioned - `zai/glm-5.3-flash-uncensored` - `qwen/qwen3.8-27b` ## Limits and terms ```yaml included_credit_usd_per_month: 5 reset: 00:30 UTC on the first day of each month rollover: false default_requests_per_minute: 120 exhaustion: Paid requests are refused when available credit is insufficient. ``` ## What happens to your prompts? **Partially private.** Prompts and responses are excluded from archives, advertising, and training, but temporary content or derived caches have no fixed public maximum TTL. Privacy is audited separately from price. Review the current governing terms before sending sensitive or regulated data. ## Data governance and agreements ```yaml data_governance: review_status: reviewed plan_scope: Arnict hosted inference, including the recurring personal-account allowance. prompt_retention: Prompts are processed transiently and not archived as conversation history; short-lived caches may hold content or derived representations until expiry or eviction, with no fixed maximum TTL published. response_retention: Responses follow the same transient-processing and cache policy and do not appear in the usage ledger as content. ordinary_logging: Request ID, model, status, timing, token counts, rates, cost, account, security, and support metadata are retained without prompt or response text. model_training: Arnict states inference prompts and responses are not used for model training. product_improvement: Inference content is excluded from advertising; operational metadata can support service delivery, billing, security, and troubleshooting. human_or_operator_access: Arnict keeps no ordinary content archive; access to transient processing or caches is not documented precisely, while support and security records can be accessed as needed. subprocessors_and_routing: Service providers can process data for inference, hosting, authentication, email, security, support, and payments; their exact inference roles are not enumerated publicly. deletion_controls: Privacy requests go to support; some account, usage, security, financial, fraud, dispute, and legal records can survive account closure. caveat: The lack of a numeric cache TTL prevents treating this as strict zero retention. agreements: terms_of_service: https://arnict.com/terms privacy_policy: https://arnict.com/privacy ``` ## Eligibility ```yaml account_required: true email_verification_required: true active_personal_account_required: true payment_method_required: false ``` ## API compatibility and modalities - **Compatibility:** OpenAI Compatible - **Modalities:** Text Generation, Vision Language, Image / vision ## Before you build with Arnict ### Read the classification narrowly This record 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 account and region. “Monthly credits” should be interpreted together with the Current directory placement, High confidence, No payment-card status, and the plan-specific sources below. ### Resolve the live model route The snapshot records 2 model IDs. Match the exact ID against the provider's current catalog before using it in code because zero-price routes, aliases, context limits, and feature support can rotate while an older documentation page remains online. ### Match the quota to the workload shape Translate the allowance into peak requests per minute, input and output tokens, concurrency, retries, and every daily or monthly ceiling that applies to the intended account. The first limit reached by the workload is the practical ceiling. A large token pool can still fail interactive bursts, and a high request limit can still fail long-context work. Include tool calls and retry traffic, test the largest realistic payload, and treat research, experimental, and community access as conditional on their eligibility and fair-use terms. ### Preserve billing and privacy boundaries The recorded payment-card requirement is No, and account access is required. Confirm both in the live signup flow, set provider-side budgets when charges are possible, and observe the balance or usage fields after a complete request. The prompt-handling classification is **Partially private** because Prompts and responses are excluded from archives, advertising, and training, but temporary content or derived caches have no fixed public maximum TTL. Re-read the terms for the exact route and plan before sending sensitive, regulated, or proprietary content. ### Follow the evidence, then re-check it This record links 7 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest and most specific governing document or live catalog when sources disagree. A dated finding can become stale even when the page remains online, so retain the source and snapshot that supported the decision and submit a correction when a provider changes a material term. ### Plan fallback without policy drift A fallback should preserve modality, context length, streaming, structured output, tools, safety controls, and data terms, not only API syntax. Decide which errors may retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. Rotating aliases and free pools can change behavior without changing the endpoint, so retain the response model field and test at least one substitute route before the primary offer becomes unavailable. ### Monitor the offer as a dependency Capture the model ID, response model, rate-limit headers, usage fields, latency, HTTP status, and provider request identifier. Watch authorization failure, quota exhaustion, catalog removal, policy revision, and balance movement as separate failure modes. Re-check the live catalog and governing sources on a schedule proportionate to the workload's importance, and keep an owner and exit path for any production dependency on volatile free capacity. ### Test one complete request before scaling Start with the smallest permitted request using the exact credential, model ID, endpoint, region, and account type intended for deployment. Record the status, headers, usage fields, response model, latency, and dashboard balance movement. Then exercise an invalid model, quota exhaustion, or rate limit so failure is explicit and cannot silently switch to a paid route. Validate streaming, structured output, and tool calls separately because a free model can expose fewer features than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and do not send sensitive data until the observed route matches the reviewed agreement. ## Primary sources - [Pricing](https://arnict.com/pricing): Official Pricing - [Pricing and monthly allowance](https://arnict.com/docs/pricing): Official Docs - [Quickstart](https://arnict.com/docs/quickstart): Official Docs - [Errors and limits](https://arnict.com/docs/errors): Official Docs - [Terms of Service](https://arnict.com/terms): Official Terms - [Privacy Policy](https://arnict.com/privacy): Official Privacy - [Data handling](https://arnict.com/docs/data-handling): Official Docs ## Sitemap See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.