OpenAI Replaces Hard Rate Limits with Credit System for Codex and Sora

Quick Brief

• OpenAI deployed credit-based access system for Codex and Sora on February 13, 2026
• Users seamlessly transition from rate limits to paid credits within the same request
• Real-time decision engine processes usage tracking with provably correct billing
• System prevents double-charging through atomic database transactions and idempotent operations

OpenAI has fundamentally rethought how developers access Codex and Sora and the solution eliminates the frustrating “come back later” experience. The company announced on February 13, 2026, a hybrid access system that combines traditional rate limits with real-time credit purchasing, allowing users to continue working when they hit usage caps. This addresses a critical problem: rapid adoption pushed both products beyond expected usage levels, creating constant interruptions for engaged users.​

Why Traditional Rate Limits Failed for AI Tools

Rate limits serve essential functions smoothing demand spikes and ensuring fair access across users. However, OpenAI’s engineering team identified a fundamental mismatch between access control models and actual user behavior. Traditional approaches force an unworkable choice: rate limits create hard stops that frustrate productive users, while pure usage-based billing charges from the first token, discouraging exploration.​

The problem intensified with Codex and Sora. Users would discover genuine value in code generation or video creation, commit to complex projects, then abruptly hit walls mid-workflow. Simply raising rate limits would eliminate demand-smoothing controls and exhaust capacity for everyone. Asynchronous usage billing introduced lag, overages, and reconciliation issues that surfaced precisely when users were most engaged.​

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The Decision Waterfall: How Credits Work Seamlessly

OpenAI’s solution centers on what the team calls a “decision waterfall”, a conceptual shift from asking “is this allowed?” to “how much is allowed, and from where?”. Every API request passes through a single evaluation sequence that checks rate limits first, then automatically transitions to credit balances if limits are exhausted.​

The system operates in real-time during each request. Rate limits enforce until reached, then the engine verifies sufficient credit balance and allows the request to proceed all within milliseconds. From the user’s perspective, there’s no system switch or workflow interruption. Credits feel invisible because they’re simply another layer in the access decision stack.​

This hybrid model preserves fairness controls while eliminating hard stops. Free tier users explore products without immediate billing. Once they find value and push beyond included limits, credits activate automatically if purchased.​

Building a Provably Correct Billing System

OpenAI rejected third-party usage billing platforms because they couldn’t meet two critical requirements: real-time decision-making and complete transparency. When a user hits a limit with available credits, the system must know immediately delays manifest as surprise blocks and incorrect charges. The company needed full control over correctness, timing, and observability.​

The engineering team built a distributed usage and balance system with three interconnected datasets:

1. Product usage events: What the user actually did
2. Monetization events: What OpenAI charges for that usage
3. Balance updates: How much the system debited and why​

These datasets drive the system sequentially, with each triggering the next. Separating what occurred, associated charges, and debits enables independent auditing, replay, and reconciliation at every layer. Every event carries a stable idempotency key, ensuring retries or worker restarts never double-debit a balance.​

Balance updates occur asynchronously but near real-time, creating an audit trail. The system tolerates brief delays in updating visible balances to prove correctness and assure users against misbilling. When that delay causes overshooting a credit balance, OpenAI automatically refunds the difference, choosing user trust over strict enforcement.​

Technical Architecture: Synchronous Decisions, Async Settlement

The access engine operates through a sophisticated two-phase process. During each request, the system makes synchronous decisions by consuming from rate limits and verifying sufficient credits. It returns one definitive outcome immediately, then settles credit debits asynchronously through a streaming processor.​

This architecture tracks per-user, per-feature usage while maintaining both rate-limit windows and real-time credit balances. Atomic database transactions decrease credit balances and insert balance update records simultaneously, guaranteeing an audit trail for every adjustment. Balance updates serialize per account, preventing concurrent requests from racing to spend the same credits.​

The system maintains transparency by logging why each request was allowed or blocked, how much usage it consumed, and which limits or balances applied. This observability integrates tightly into the decision waterfall rather than existing in isolation, giving OpenAI complete control over correctness.​

Impact on Codex and Sora Users

The credit system transforms how developers and creators work with OpenAI’s most resource-intensive products. Codex received temporarily doubled rate limits for all paid plans, a two-month promotional boost launched February 2026 alongside the desktop app. When those limits exhaust, users purchase credits directly in the Codex Settings dashboard under Usage to continue without interruption.

Sora video generation follows the same model. Users hitting included limits buy additional generations through the app’s Settings menu under Usage. The system’s real-time balance tracking prevents the surprise billing that plagued earlier usage-based models.

Credits were rolled out in late 2025, with OpenAI documentation appearing as early as September 2025. The February 2026 announcement details the underlying architecture that makes seamless access possible. The company positions this foundation as extensible to additional products beyond Codex and Sora.

What This Means for API Economics

OpenAI’s approach suggests a broader shift in how AI platforms handle high-demand products. Traditional SaaS pricing models tiered plans with hard caps break down when users derive genuine value from resource-intensive features. Pure usage-based billing discourages experimentation, particularly for expensive operations like video generation or multi-file code analysis.​

The credit system offers a middle path: generous free tiers encourage adoption, rate limits maintain system stability, and optional credits remove artificial ceilings for committed users. The architecture’s emphasis on provable correctness addresses enterprise requirements, with OpenAI noting the system “originated with enterprise customers” who need audit trails and billing transparency.​

Limitations and Considerations

The system introduces complexity that users must understand. Credit balances update asynchronously, meaning the displayed balance may briefly lag actual consumption during heavy usage. While OpenAI refunds accidental overcharges, users need to monitor balances proactively to avoid mid-task interruptions.​

The credit model shifts cost structures. Users previously constrained by rate limits now face variable expenses that scale with usage intensity. For teams running automated workflows or generating large content volumes, credit costs require careful budget planning.​

The February 2026 announcement focuses on technical architecture rather than pricing strategy, leaving questions about long-term affordability and credit purchase tiers. Codex’s doubled rate limits expire after the two-month promotional period, returning to standard limits in April 2026.

The Road Ahead for Access Control

OpenAI frames this infrastructure as foundational for future products, suggesting credits will expand beyond Codex and Sora. The decision waterfall architecture supports layering additional access rules promotional allowances, enterprise entitlements, or feature-specific quotas without rebuilding core systems.​

The company’s emphasis on user momentum “when users are engaged, the system should help them continue, not get in the way” signals a philosophical stance on AI platform design. If the credit model proves successful, competitors may adopt similar hybrid approaches for high-value, resource-intensive features.​

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