OpenAI will restore a five-hour usage limit for ChatGPT Plus subscribers using Codex and ChatGPT Work on August 25. The limit returns after several weeks in which Plus users faced only a weekly usage cap across the two now-unified platforms. Users who reach either the five-hour or weekly limit can wait for the relevant usage cycle to reset or buy additional credits. OpenAI may also issue free limit resets, and users can bank resets through some promotions and referral offers. The company reset weekly usage early several times during the temporary change, including after the platforms added another million active users. Thibault Sottiaux, OpenAI’s engineering lead for Codex and ChatGPT, said the five-hour cap helps smooth demand on the company’s compute capacity while preserving relatively generous weekly allowances. He also said Plus subscribers, whom he characterized as relatively casual and new users, can accidentally consume an entire week’s allowance and find the resulting restriction confusing. The five-hour limit will remain disabled for the coming months on the $100 and $200 Pro subscriptions. Sottiaux did not announce changes to Enterprise or Edu accounts, which use a separate credit-based usage system.

A developer who used OpenAI Codex more heavily than Anthropic Claude for a week found Codex generally more contained, technical in tone, and less prone to adding architectural complexity. In Ruby and Ruby on Rails changes, Codex produced fewer comments, while Claude more often introduced abstractions, concepts, Sorbet signatures, and type aliases. When both tools implemented the same requirement from the same documents, Claude produced somewhat more complex code but handled additional cases, according to the developer. The developer felt Codex made initial changes faster, but its extensive test reruns and review work eliminated any overall time advantage in finishing pull requests. Codex also encouraged smaller, focused sessions rather than long-running conversations. Claude remained the preferred tool for urgent debugging because of familiarity, not because it was judged superior. Codex made branch-management errors, including rebasing a branch onto main rather than its target branch and creating a pull request with more than 4,000 additions. Codex was also less effective in the developer’s Jira and Atlassian CLI-based environment, while its MCP CLI login flow was preferred because it explicitly requested the `codex mcp login` command and opened the appropriate authorization process. The developer characterized Claude as more likely to anticipate and act on inferred needs, while Codex more closely follows explicit instructions and stops once it appears finished.

A GitHub issue reports that Codex CLI 0.147.0 using its native Amazon Bedrock provider cannot enable explicit prompt caching for the openai.gpt-5.6-sol model through the Bedrock Mantle Responses API in us-east-1. The reporter says Codex emits a session-scoped prompt_cache_key, but its HTTP and WebSocket Responses request types do not support prompt_cache_options or prompt_cache_breakpoint fields. The built-in Bedrock provider configuration supports transport and authentication settings rather than request-body transformations, preventing configuration through config.toml. For completed days from August 5 through August 8, 2026, the reporter estimated 3,656 requests generated 171.94 million cache-write tokens, costing $1,182.09 of an estimated $1,386.46 total model spend. Cache writes represented about 85% of the estimate, which was derived from AWS Cost Explorer quantities and Bedrock rate-card pricing rather than finalized invoice amounts. A local session recorded 76 Sol requests, 6.709 million cache-write input tokens, no cached input tokens, and roughly 88,000 cache-write tokens per request; corresponding CloudWatch metrics showed no client errors. The requested changes include support for prompt_cache_options, typed prompt_cache_breakpoint input fields, provider and model capability checks, placement after stable instruction and tool prefixes, and per-turn cache-read and cache-write telemetry.

Codex was utilized to achieve a 232x faster kernel for the qr_v2 problem in GPU mode. The implementation involved optimizing the blocked Householder algorithm and introducing idea diversity to enhance performance.

Codex in ChatGPT enables AI coding agents to perform tasks such as building features, complex refactors, and migrations using OpenAI's advanced coding models. It supports multi-agent workflows with built-in worktrees and cloud environments, allowing agents to work in parallel across projects.

A customer experienced a sudden loss of 491.8 Codex credits, which vanished without any recorded usage. After purchasing an additional 1,000 credits for $48.40, those credits also failed to appear in the account.

OpenAI Codex Security provides a CLI and TypeScript SDK for identifying, validating, and fixing security vulnerabilities in code. It supports scanning repositories, reviewing changes, tracking findings over time, and running security checks in continuous integration environments, requiring Node.js 22 or later and Python 3.10 or later.

Codex is reportedly causing excessive wear on devices due to its resource-intensive operations. Users have expressed concerns about the longevity and performance of their hardware when running Codex.

Backporting refreshed bundled model metadata to version 0.144 is proposed in a pull request. Suggestions cannot be applied while the pull request is closed, and multiple suggestions cannot be batched if they involve deleted lines or subsets of changes.

OpenAI's Codex has experienced a total of 35 usage limit resets, with an average interval of 8.9 days between resets and the longest gap recorded at 67.7 days. Reset announcements are tracked daily over the last 26 weeks, providing updates for paid users of Codex and ChatGPT Work.