BytePlus ModelArk
500K tokens for new accounts; it does not recur.
https://ark.ap-southeast.bytepluses.com/api/v3Models mentioned
seed-1.6seed-1.6-flashseed-2-1-turboseed-translationskylark-proskylark-visionskylark-embedding-visionEquivalent paid value
How this was valued: Three captured generation models are each assigned the documented typical 500K-token package and valued at their costlier output rate. A fourth current vision-embedding package is shown as an alternative but excluded from the subtotal because independent activation alongside the other packages is not explicit. Exact eligibility and expiry remain dynamic in the Model Activation and Billing Center.
Models without a captured matching rate are excluded, and the public offer describes 500K as typical rather than guaranteed for every model.
Limits and terms
- Typical Tokens Per Eligible Model
- 500,000
- Skylark Embedding Vision Free Tokens
- 500,000
- Skylark Embedding Vision Paid Rate
- $0.325/M image input or $0.125/M text input
- Recurrence
- once per account
- Exact Models And Expiry
- Dynamic in Model Activation and Billing Center.
- Exclusions
- pluginsknowledge basesbatch inferencecache storage
What happens to your prompts?
Campaign terms establish free tokens but do not resolve content TTL, training, improvement, or model-specific routing.
- Plan Scope
- BytePlus ModelArk and its free-token campaign; product-specific, regional, and model-provider terms may add conditions.
- Prompt Retention
- No ModelArk-free-tier prompt TTL was verified in the campaign or general legal terms.
- Response Retention
- not documented
- Ordinary Logging
- BytePlus collects service, account, device, security, and usage information; the legal pages do not clearly isolate inference content.
- Model Training
- No campaign-specific no-training commitment was found for ModelArk prompts and outputs.
- Product Improvement
- General service improvement uses are permitted; content scope remains unresolved.
- Human Or Operator Access
- BytePlus and service providers may process content; no zero-operator-access commitment was found.
- Subprocessors And Routing
- BytePlus infrastructure and selected third-party/open models can create additional model-specific terms.
- Deletion Controls
- General privacy rights exist; no inference-content TTL or per-request deletion control was found.
- Caveat
- Promotional token terms establish price eligibility, not privacy. They must not be treated as a data-processing agreement.
Governing documents
- Terms Of Service
- https://www.byteplus.com/en/legal/terms-of-service
- Privacy Policy
- https://www.byteplus.com/en/legal/privacy-policy
Eligibility
- Enterprise Information Submission Required
- Yes
- Payment Method Required
- not explicitly stated
Primary sources
Before you build with BytePlus ModelArk
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 7 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://ark.ap-southeast.bytepluses.com/api/v3, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
The public evidence does not settle whether an account is required. Confirm the signed-in flow before treating anonymous or credential-free access as available. 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”: Campaign terms establish free tokens but do not resolve content TTL, training, improvement, or model-specific routing. 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.