OpenAI API complimentary data-sharing tokens
A possible free offer exists, but a decisive term still needs verification.
Models mentioned
gpt-5.6-solgpt-5.5-2026-04-23gpt-5.4-2026-03-05gpt-4.1-2025-04-14o3-2025-04-16gpt-5.6-terragpt-5.6-lunagpt-5.4-mini-2026-03-17gpt-5.4-nano-2026-03-17gpt-4.1-mini-2025-04-14o4-mini-2025-04-16Limits and terms
- Reset
- 00:00 UTC daily
- Tiers 1 And 2
- Large Model Group Tokens Per Day
- 250,000
- Small Model Group Tokens Per Day
- 2,500,000
- Tiers 3 To 5
- Large Model Group Tokens Per Day
- 1,000,000
- Small Model Group Tokens Per Day
- 10,000,000
- Caveat
- A request that would cross the quota is billed in full; fine-tuning, evals, and tool use are excluded.
What happens to your prompts?
- Plan Scope
- OpenAI API complimentary data-sharing tokens watchlist offer; current free delivery or eligibility remains unresolved.
- Prompt Retention
- Shared prompts and completions are deliberately provided to OpenAI for improvement. Outside the opt-in, default API abuse-monitoring logs may contain prompts and responses for up to 30 days, while stateful endpoints have separate retention.
- Response Retention
- Shared outputs receive the same improvement use; Responses and other stateful APIs can store application state according to endpoint settings, in addition to abuse logs.
- Ordinary Logging
- Default API abuse-monitoring logs can contain customer content and derived metadata for up to 30 days unless a separately approved retention control applies.
- Model Training
- Data sharing is off by default, but this free-token mechanism requires an organization owner to opt in to sharing inputs and outputs for evaluation and future model training.
- Product Improvement
- Shared traffic is used to identify usage patterns, measure model quality, and inform future evaluation and training.
- Human Or Operator Access
- Shared traffic enters OpenAI improvement systems. Safety exceptions can also permit retention and human review, including for certain detected content.
- Subprocessors And Routing
- OpenAI processes the selected eligible API traffic; data sent through tools such as remote MCP servers is separately governed by those third parties.
- Deletion Controls
- Organization owners can opt out prospectively at any time. Opting out ends future sharing but does not promise deletion or model unlearning for data already used.
- Caveat
- Complimentary tokens apply only to eligible organizations and only to traffic intentionally shared with OpenAI. A positive account balance is required, overages are billed, and Zero Data Retention organizations cannot enroll.
No provider terms or privacy-policy link is captured in this snapshot. Review the provider’s current legal documents before sending sensitive data.
Eligibility
- Organization Must Be Selected By OpenAI
- Yes
- Input Output Data Sharing Opt In Required
- Yes
- Positive Account Balance Required
- Yes
- Unavailable To
- EnterpriseZero Data Retention organizations
Primary sources
Before you build with OpenAI API complimentary data-sharing tokens
Read the classification narrowly
This offer is not counted as verified because a material detail remains unresolved. 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 11 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. No stable base endpoint is published in this record, so use the linked provider documentation to identify the current request URL and protocol.
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 payment-card requirement is conditional or not clearly documented. Treat signup friction and billing exposure as unresolved until the account flow confirms them. 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”: Privacy protections are incomplete, conditional, or not fully documented. 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 4 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.