Codex Limits REDUCED BY 50%: So, Codex is worse! What to do now?

Key Concepts

• Codex Rate Limits: The constraints on the number of requests or tokens a user can send to the OpenAI Codex model within a specific timeframe.
• Inference Costs: The computational expense incurred by AI companies to process user requests and generate model outputs.
• Dedicated Deployment: A business model where AI companies provide private, high-capacity model instances to enterprise clients.
• Model Agnosticism: The ability to use a specific AI model (like GLM) across various third-party coding tools and IDE extensions.

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1. The Reduction of OpenAI Codex Limits

Recent user reports indicate that OpenAI has reduced Codex usage limits by approximately 50%. The speaker argues this is a deliberate, permanent shift rather than a technical bug, citing the following evidence:

• Lack of Communication: The silence from OpenAI over an 11–12 hour period suggests a strategic policy change rather than an accidental outage.
• Unsustainable Economics: Providing high-volume inference at low costs is not a viable long-term business model. The initial high limits were likely a "growth hack" to onboard users from competitors like Claude.
• Resource Reallocation: OpenAI recently launched a deployment company to serve enterprise partners. The speaker posits that compute resources are being diverted from individual users to support these high-value, dedicated corporate contracts.

2. The "AI Business Cycle"

The video outlines a recurring pattern in the AI industry:

1. Aggressive Onboarding: Companies offer generous limits to attract users from competitors.
2. Normalization: As the user base grows, limits are quietly reduced to manage costs.
3. Retraction: Features or access tiers are restricted, often through minor updates to terms of service or plan descriptions.

• Key Argument: The speaker asserts that "if the service is too good to be true, they are taking your data, your money, or both."

3. Alternatives and Market Recommendations

The speaker suggests that users should look toward more stable, albeit sometimes slower, alternatives to avoid the volatility of major providers.

• GLM Coding Plan: Highlighted as the most stable option.
  • Pricing: Offers a yearly plan for $345 or a migration-supported $80 plan valid until July 2026.
  • Compatibility: Highly versatile; works with tools like OpenCode, Klein, Hermes, and OpenClaw.
  • Integration: Users can configure it via CLI commands (e.g., hermes model or open-code connect).
• Other Notable Mentions:
  • Anti-Gravity: Recommended for those comfortable with their specific editor; features a $20 entry-level plan.
  • Verdant: Described as a more refined version of Anti-Gravity, featuring a "manager" function that tracks tasks across threads.
  • Command Code: Offers a low-cost $1 entry point and includes access to DeepSeek V4 Pro.
  • Kimiko: A tiered subscription model ranging from $19 to $199 per month.

4. Strategic Synthesis

The core takeaway is that individual users are often "loss leaders" in the AI industry. OpenAI’s primary goal with public-facing tools like Codex is to build a user base that eventually attracts corporate interest. Once enterprises are onboarded, individual user limits are often sacrificed to prioritize the compute needs of high-paying corporate clients.

Actionable Insight: Users should avoid relying solely on a single, volatile provider. Diversifying tools and exploring stable, specialized coding plans (like GLM or Verdant) provides better long-term reliability for development workflows. The speaker concludes that while the landscape is unpredictable, the market is currently saturated with viable alternatives for those willing to migrate.