Codex now offers more flexible pricing for teams

2026-03-24 · Source: OpenAI News · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Codex, a service for developers, now offers pay-as-you-go pricing for teams on ChatGPT Business and Enterprise, effective April 2, 2026. This allows teams to add Codex-only seats without a fixed fee, billed solely on token consumption, providing clearer cost tracking. Concurrently, the annual price for standard ChatGPT Business seats, which include Codex usage limits, has been reduced from $25 to $20 per seat. To further encourage adoption, eligible ChatGPT Business workspaces can receive $100 in credits for each new Codex-only team member, up to $500 per team, for a limited period. This initiative supports the accelerating adoption of Codex, which has seen a 6x growth in users within ChatGPT Business and Enterprise since January, with over 2 million builders using it weekly.

Key takeaway

For CTOs and VP of Engineering evaluating AI integration, the new pay-as-you-go Codex pricing and reduced ChatGPT Business seat cost significantly lower the barrier to entry. You can now pilot Codex in specific workflows with minimal upfront commitment and scale based on actual token consumption, making it easier to justify and track ROI for AI-driven development initiatives.

Key insights

Codex introduces flexible pay-as-you-go pricing and reduced ChatGPT Business costs to accelerate team adoption.

Principles

• Cost transparency drives adoption
• Flexible pricing lowers entry barriers

In practice

• Add Codex-only seats for usage-based billing
• Utilize the Codex app for macOS/Windows
• Claim $100 credits for new Codex team members

Topics

• Codex
• Pay-as-you-go Pricing
• ChatGPT Business
• Enterprise Workspaces
• Token-based Billing

Best for: CTO, VP of Engineering/Data, Machine Learning Engineer, Director of AI/ML, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by OpenAI News.