Claude Code vs Codex: Models, Costs and a Fair Comparison [2026]

Official-source correction: October 1, 2026. Earlier benchmark and cost extrapolations have been removed from the current recommendation.

The Claude Code model documentation lists Opus 5.5 as the account-type default on Pro, Max, Team, Enterprise and the Anthropic API, plus several cloud providers. Microsoft Foundry differs, and administrator or user settings can override defaults. Anthropic API aliases currently resolve to Opus 5.5 and Sonnet 5.5. Use /model to inspect or choose the actual model; a product name alone does not identify it.

The official Codex model guidance recommends GPT-6.1 Sol for complex coding when available, and GPT-6 Luna for focused tasks; GPT-6 Astra is another available option depending on account and client. GPT-5.5 is a previous-generation model scheduled to retire from ChatGPT-authenticated Codex on October 14, 2026. That retirement does not apply to the OpenAI API.

Do not assume every user has the same default. Save the model, reasoning effort, client version and configuration with any result you report.

The original April comparison discussed Codex on GPT-5.3 against Claude Code on Opus 4.6. Its 1.5-million-versus-6.2-million token example came from third-party reports, not a reproducible official head-to-head established in this update. We therefore do not present it as a verified vendor claim, a current benchmark, or evidence that Codex costs one quarter as much.

That historical model pair must not be silently relabeled with newer defaults. Nor does a tokenizer change establish the direction or size of a new efficiency gap. No new benchmark was run for this correction.

Token totals also do not explain why a tool used more tokens. Extra tokens could reflect useful verification, retries, repeated context, tool output or unsuccessful work. Quality and cost need separate measurements.

Consult Codex pricing and Claude Code cost guidance for your specific plan and billing route. Subscription usage, additional credits and API billing are different arrangements. Neither a subscription headline nor a raw token count guarantees the cost of a complex task.

For a useful comparison, record the actual charge or credit consumption, input and output usage where available, retries, elapsed time and your review time. Compare the same task with the same acceptance criteria. Remove the assumption that every task fits inside a fixed monthly allowance.

Running tools on a local machine is also different from running model inference on premises. Check the execution environment, model provider and data policies separately. The older claim that every Codex task runs only in the cloud was too broad; the official documentation supports local clients and model configuration.

Neither tool earns a universal win from an older benchmark. Start with the one you already have access to, test the work you actually do, and add the other only if it solves a concrete gap.

For broader model selection, use our canonical task-by-task model guide. For Claude Code permission handling, see the Channels and Auto Mode guide.

Written by

Paras Tiwari

Founder, Spectrum AI Labs

Founder of Spectrum AI Labs — testing AI tools and models, and writing up what actually ships.

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