How to improve your skills and optimize your code with GPT-5 Codex

In the ecosystem of AI-assisted development tools, GPT-5-Codex emerge como OpenAI's bid to bring coding assistance to a truly agentic level, capable of planning, executing, testing and polishing code changes within real flows.

This isn't just another auto-complete tool: its approach is to complete tasks, fit into PRs, and pass battery tests, with behavior closer to that of a technical colleague than a simple conversational assistant. That's the tone of this new iteration: more reliable, more practical, and designed for everyday engineering routines.

What is GPT-5-Codex and why does it exist?

GPT‑5‑Codex is, in essence, a GPT‑5 specialization focused on software engineering and agent flowsRather than prioritizing general chatter, its training and reinforcement tuning focus on “build → run tests → fix → repeat” cycles, judicious PR writing and refactoring, and following project conventions. OpenAI positions it as a legacy of previous Codex initiatives, but built on GPT-5’s reasoning and scaling foundation to delve into multi-file tasks and multi-step processes with greater reliability.

The motivation is pragmatic: Teams need something that goes beyond suggesting an isolated snippetThe value proposition lies in moving from "I'll write you a feature" to "I'll deliver you a feature with passing tests," with a model that understands the repo structure, applies patches, re-runs tests, and delivers a legible PR aligned with the company's standards.

How it is designed and trained: architecture and optimizations

Architecturally, GPT‑5‑Codex inherits the transformative basis of GPT‑5 (scaling properties, reasoning improvements) and adds engineering-specific tuning. Training focuses on real-world scenarios: multi-file refactorings, test suite execution, debugging sessions, and review with human preference signals, so the goal is not only to generate correct text, but also Maximize accurate edits, approved tests, and useful review feedback.

The “agentive” layer is key. The model learns to decide when to invoke tools, how to incorporate test outputs into its next steps, and how to close the loop between synthesis and verification. It is trained on trajectories in which it issues actions (e.g., “run test X”), observes results, and conditions their subsequent generation, enabling consistent behavior over long sequences.

Execution-driven training and RLHF applied to code

Unlike a generic chat setting, Reinforcement incorporates actual code execution and automatic validationFeedback loops derive from both test results and human preferences, addressing the assignment of temporal credit in multi-step sequences (creating PRs, executing suites, fixing bugs). The context scales to repository size to learn about dependencies, naming conventions, and cross-cutting effects across the codebase.

This approach with “instrumented environments” allows the model to internalize engineering practices (e.g., maintaining behavior across large refactorings, writing clear diffs, or following standard PR etiquette), which reduces friction when integrating into teams already operating with CI and formal reviews.

Use of tools and coordination with the environment

Historically, Codex combined its output with a lightweight runtime that could open files or run tests. In GPT-5-Codex, This coordination is intensified: it learns when and how to call tools and “reads” back the results., closing the gap between the language level and programmatic validation. In practice, this translates into fewer blind attempts and more iterations informed by feedback from the testing system.

What you can do: capabilities and adaptive “thinking time”

One of the differential bets is the variable reasoning duration: Trivial requests are answered quickly and cheaply, while complex refactoring can open a long “thinking” window for structuring the change, patching, and retesting. In short rounds, it also consumes far fewer tokens than GPT-5 in general, with Savings of up to 93,7% on tokens in small interactions, which helps contain costs.

En cuanto a funciones, Start projects with full scaffolding (CI, tests, docs), runs test-fix cycles autonomously, addresses multi-file refactorings while maintaining behavior, writes PR descriptions with well-presented changes, and reasons through dependency graphs and API boundaries more robustly than a generic chat model.

When you work in the cloud, supports visual inputs and outputs: You can receive screenshots and attach artifacts (e.g., screenshots of the resulting UI) to tasks, which is very useful for front-end debugging and visual QA. This visual-code link is especially useful for validating designs or verifying that a graphical regression has been fixed.

Workflow integrations: CLI, IDE, and GitHub/Cloud

Codex doesn't stay in the browser. The Codex CLI has been redesigned around agentive flows, with image attachments, a task list, support for external tools (web search, MCP), an improved terminal interface, and a simplified three-level permission mode (read-only, automatic, and full access). All designed to make collaboration with the agent from the terminal more reliable.

En el editor, The Codex extension for IDE integrates the agent into VS Code (and forks) to preview local diffs, move tasks between the cloud and on-premises while preserving context, and invoke the model with the current file in view. Viewing and manipulating results in the editor reduces context switching and speeds up iterations.

In the cloud and on GitHub, Tasks can automatically review PRs, raise ephemeral containers, and attach logs and screenshots to the review threads. The improved infrastructure brings significant reductions in latency thanks to the container cache, with time reductions of around 90% in some repetitive tasks.

Limitations and in which areas it performs better or worse

Specialization has its price: In non-code-related assessments, GPT‑5‑Codex may perform slightly below GPT‑5 GeneralistAnd its agentive behavior is coupled with the quality of the test set: in repos with low coverage, automatic verification falters, and human oversight becomes indispensable again.

Destaca en Complex refactorings, scaffolding of large projects, writing and correcting tests, PR expectation tracking, and multi-file bug diagnosis. It's less suitable where proprietary knowledge not included in the workspace is required or in "zero-error" environments without human review (critical to security), where caution is paramount.

Performance: benchmarks and reported results

In agentive-focused tests such as SWE‑bench Verified, OpenAI reports that GPT-5-Codex surpasses GPT-5 in success rate on 500 real software engineering tasks. Part of the value lies in the fact that the evaluation covers more complete cases (no longer just 477, but 500 probable tasks), and in visible improvements in refactoring metrics extracted from large repos. Notable leaps are cited in certain high-verbosity indicators, although nuances of reproducibility and test configuration are noted.

Critical reading remains mandatory: subset differences, verbosity, and costs can skew comparisons. Still, the pattern across independent reviews is that agentic behavior has improved, and that strengths in refactoring don't always translate to improved raw accuracy across all tasks.

Access today: Where to use GPT-5-Codex

OpenAI has integrated GPT-5-Codex into Codex product experiences: CLI, IDE extension, cloud and review threads on GitHub, in addition to its presence in the ChatGPT app for iOS. In parallel, the company has indicated availability for Plus, Pro, Business, Edu and Enterprise subscribers within the Codex/ChatGPT ecosystem, with API access announced as “coming soon” beyond native Codex flows.

For those who start via API, The call follows the usual SDK patternA basic example in Python would look like this:

import openai
openai.api_key = "tu-api-key"
resp = openai.ChatCompletion.create(
    model="gpt-5-codex",
    messages=[{"role":"user","content":"Genera una función en Python para ordenar una lista."}]
)
print(resp.choices[0].message.content)

Availability through OpenAI API-compatible providers is also mentioned, and that Pricing follows a token scheme with specific business conditions according to plans. Tools such as Apidog They help simulate responses and test extreme cases without real consumption, facilitating documentation (OpenAPI) and client generation.

VS Code via GitHub Copilot: Public Preview

En Visual Studio Code, Access is via Copilot In public preview (version and plan requirements apply). Admins enable it at the organization level (Business/Enterprise), and Pro users can select it in Copilot Chat. Copilot agent modes (ask, edit, agent) They benefit from the persistence and autonomy of the model to debug scripts step by step and propose solutions.

Conviene recordar que the implementation is released gradually, so not all users see it at the same time. Additionally, Apidog provides API testing from within VS Code, useful for ensuring robust integrations without production costs or latencies.

Security, controls and safeguards

OpenAI emphasizes multiple layers: Safety training to resist injections and prevent risky behaviors, and product controls such as default execution in isolated environments, configurable network access, command approval modes, terminal logging, and citations for traceability. These barriers are logical when an agent can install dependencies or execute processes.

Hay, además, known limitations that require human oversight: It doesn't replace reviewers, benchmarks have fine print, and LLMs can be misleading (invented URLs, misinterpreted dependencies). Validation with tests and a human review remains non-negotiable before committing changes to production.

Dynamic reasoning time: from seconds to seven hours

One of the most striking statements is that ability to adjust computational effort in real time: from responding in seconds for small requests to spending several hours on complex and fragile tasks, retrying tests and correcting errors. Unlike a router that decides a priori, the model itself can reallocate resources minutes later if it detects that the task requires it.

This approach makes Codex a more effective collaborator on long and unstable jobs (major refactorings, multi-service integrations, extended debugging), something that was previously beyond the reach of traditional autocompletions.

CometAPI and multivendor access

For teams that want avoid vendor lock-in and move quicklyCometAPI offers a single interface to over 500 models (OpenAI GPT, Gemini, Claude, Midjourney, Suno, and more), unifying authentication, formatting, and response handling. The platform commits to incorporating GPT‑5‑Codex in parallel with its official launch, in addition to exhibiting GPT‑5, GPT‑5 Nano and GPT‑5 Mini, with a Playground and API guide to speed up testing.

This approach allows iterate without redoing integrations Every time a new model arrives, control costs and maintain independence. In the meantime, you're encouraged to explore other models in the Playground and review the documentation for orderly adoption.

More product updates: hotfixes, front-end, and CLI

OpenAI indicates that GPT‑5‑Codex has been specifically trained to review code and detect critical errors, scanning the repo, running code and tests, and validating fixes. In evaluations with popular repos and human experts, a lower proportion of incorrect or irrelevant comments is observed, which helps focus attention.

On the front‑end, reliable performance is reported and improvements in human preferences for mobile site creation. On desktop, it can generate attractive applications. Codex CLI has been rebuilt for agent flows, with image attachments for design decisions, a task list, and improved formatting of tool calls and diffs; plus integrated web search and MCP for securely connecting to external data/tools.

Accessibility, plans and gradual deployment

El modelo está deployed in terminals, IDE, GitHub and ChatGPT for Plus/Pro/Business/Edu/Enterprise users, with the API planned for later. No detailed limit differences are provided by plan, and access may appear in a staggered manner, something common in previews and wave releases.

En cuanto a costes, Prices follow token schemes and usage levels; for businesses, the conversation typically revolves around Business/Pro and session and load assessment. Given the variable "think time," it's a good idea to define enforcement policies and limits clear to avoid surprises.

For testing and validation, Apidog fits well by simulating responses, importing OpenAPI specifications, and facilitating client generation; and vendors such as OpenRouter offer API support for alternative routes for cost or redundancy.

Looking at the whole picture, GPT-5 Codex consolidates the transition from “autocomplete” to “deliver features”An agent that thinks just enough, or just enough, depending on the task, integrated into everyday tools, with layered security and a clear focus on verifiable engineering results. For teams of all sizes, this is a real opportunity to gain speed without sacrificing control and quality.