OpenCode vs Codex: Compare Tasks, Cost and Control

Published on October 28, 2026 by Steven Jones

Quick verdict: Choose Codex if you mainly use OpenAI models and want the shortest route from a task description to a reviewed code change. Choose OpenCode if switching providers, running local models, or customising agent behaviour is central to your workflow. Neither is automatically the better coder: the model, repository, permissions and acceptance tests can change the outcome.

This OpenCode vs Codex comparison separates the coding agent from the model powering it. It covers repository tasks, subscription and API costs, execution controls, recovery after interruptions and a reproducible way to compare the two. The task protocol below is a proposed evaluation, not a set of invented benchmark results.

OpenCode vs Codex: Which setup wins?

Your priority	Better starting point	Reason
Get coding quickly with OpenAI models	Codex	First-party ChatGPT sign-in and an OpenAI-focused agent workflow reduce setup decisions.
Switch between hosted and local models	OpenCode	Provider and model selection are central features, not specialist configuration.
Run tasks from scripts or CI	Either	Both support non-interactive commands. Reliability must be checked with your actual tasks.
Delegate work to a hosted environment	Codex	Its wider product includes cloud tasks for eligible accounts, beyond the local CLI.
Control model-generated shell commands	Codex for built-in sandboxing	Codex documents OS-enforced sandbox modes; OpenCode’s tool permissions need separate scrutiny and, where appropriate, external isolation.
Spend the least per accepted change	Measure it	A cheaper model can lose its advantage through retries, review time or failed tests.

The practical difference is in the default workflow. Codex supplies an integrated OpenAI coding environment, including CLI, editor and eligible cloud experiences. OpenCode is an open-source coding agent with terminal, desktop and other interfaces, designed to let developers choose providers and customise how an agent uses tools. Both can inspect a repository, edit files and run commands.

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The agent and the model are different variables

Comparing Codex running a current GPT model with OpenCode running a lower-cost open model may tell you which complete setup you prefer. It does not establish which agent writes better code. A model can miss a requirement while its interface works perfectly; an agent can waste time searching the repository even when its model knows the right fix.

OpenCode supports multiple model providers, local endpoints and optional model services. Codex is OpenAI-centred by default, although its CLI has configuration paths for compatible alternative providers and local inference. So it is inaccurate to describe Codex as absolutely restricted to one model provider, or OpenCode as inherently cheaper.

For a meaningful comparison, run two passes. Pass A holds the underlying model and, where technically possible, the API provider, billing route and reasoning settings constant. This is your best estimate of the difference made by the agent’s instructions and tool loop. Pass B compares the setups you would genuinely pay for and use every day, such as Codex with ChatGPT sign-in against OpenCode with a chosen provider. Label those results as complete-workflow comparisons, not agent-only benchmarks.

Three repository tasks that expose the real trade-offs

Start from one clean Git commit in a small, testable repository. Keep its lockfile and dependencies fixed, prepare acceptance tests before prompting either agent and use a separate worktree for each run. Don’t allow one agent’s changes or generated instructions to become the other’s starting point.

Task	Bounded instruction	Evidence of completion
1. Regression fix	Repair one reproducible input-validation bug without changing public function signatures.	The previously failing regression test passes, the existing suite stays green and the diff touches only relevant files.
2. Multi-file refactor	Remove duplicated logic from two modules while preserving their documented behaviour.	Type checks and tests pass, both call sites use the shared implementation and no unrelated APIs change.
3. Interrupted change	Add a small configuration-backed feature, interrupt execution after its first edit, then resume.	The agent recovers without duplicate edits, finishes the required tests, and leaves no unapproved files or commands behind.

These are task types, not claims that either product has completed them. Define exact filenames, failing tests and expected behaviour in your chosen repository before running the comparison. A vague prompt such as “improve this code” has no objective pass condition and makes the eventual winner impossible to defend.

To prepare independent runs, record the source commit and installed versions. The following commands assume Git and separate working directories; replace the placeholder with a real commit hash and use your repository’s own install and test commands.

git rev-parse HEAD
codex --version
opencode --version
git worktree add --detach ../codex-eval YOUR_COMMIT_SHA
git worktree add --detach ../opencode-eval YOUR_COMMIT_SHA

Run each task twice from fresh worktrees, with the same prompt and no additional hints. The Codex CLI supports codex exec for non-interactive work; OpenCode supports opencode run. For an interactive comparison, keep the same human approval rules and record every intervention rather than silently helping one agent more than the other.

For every run, log the tool release, operating system, repository SHA, model ID, provider, account and billing mode, permission configuration, start and finish time, tool errors, interruptions, manual hints, final diff and test output. Record billable input, output and cached tokens where your provider exposes them. A successful terminal message is not proof of a successful code change.

Our AI code-generation dataset covers broader product evaluation; DIY AI’s published dataset methodology explains that scoring scope. OpenCode is not a scored entry in that dataset, so we are not presenting its category scores as a matched Codex benchmark.

Cost: compare billing routes before comparing prices

Setup	What you pay for	What to record
OpenCode with your own API key	The model provider’s usage charges; the OpenCode client itself does not impose a software licence fee.	Model rates, input and output tokens, cache treatment, retries and provider invoices.
OpenCode with Zen, Go or another subscription	The selected model service’s credits, plan or usage allowance.	Plan spend, limits reached and completed tasks within those limits.
OpenCode using eligible ChatGPT sign-in	The applicable shared ChatGPT usage allowance, not a fresh allowance created by installing OpenCode.	Account eligibility, allowance consumption and whether further credits or billing apply.
Codex signed in with ChatGPT	Access and usage limits attached to the ChatGPT plan; cloud availability depends on eligibility.	Tasks completed before hitting limits, interruptions, and any purchased additional usage.
Codex with an API key	OpenAI API charges, billed separately from ChatGPT subscription fees.	The billed model, token categories and any other applicable usage charges.

At the October 2026 check, OpenAI included Codex access with Free and Go as well as paid ChatGPT plans, subject to usage limits; Codex Cloud has narrower eligibility. Do not label a subscription-backed Codex run “zero cost” and then compare it with an OpenCode API invoice. You paid for the subscription, even if the next task produces no separate invoice. Conversely, a local model isn’t cost-free if it requires dedicated hardware, electricity, or substantially longer review.

The useful metric is cost per accepted change: allocated subscription spend or metered model charges, plus the developer time spent prompting, reviewing, recovering and fixing mistakes. Keep the cash cost and the human effort visible as separate figures. A cheap first response that fails acceptance twice may be the most expensive route to working code.

Permissions: OpenCode 1 and 2 do not behave the same way

This is one of the easiest details to miss in an otherwise thorough comparison. OpenCode 1 uses a permission configuration with actions such as bash. OpenCode 2 uses an ordered permissions array, and its equivalent shell action is called shell. Copying permission examples between those releases can lead to controls that do not work as intended.

There is also a meaningful default change. OpenCode 1 normally asks before a tool accesses paths outside the project. OpenCode 2’s documented default allows external-directory access unless you add a rule. For OpenCode 2, an explicit starting restriction looks like this:

{
  "permissions": [
    { "action": "external_directory", "resource": "*", "effect": "ask" },
    { "action": "shell", "resource": "*", "effect": "ask" }
  ]
}

That configuration requires approval; it doesn’t create an operating-system sandbox. An agent running with your user’s filesystem permissions still deserves a disposable worktree or container, limited secrets and carefully scoped network access when the repository is untrusted.

Codex provides configurable approvals and sandbox modes, including read-only and workspace-write behaviour. It enforces its local sandbox through operating-system mechanisms rather than solely by a model deciding whether to request a tool. The official Codex approvals and security guide explains the separation between permission prompts and what commands can technically access. Do not use an unrestricted mode simply to avoid benchmark interruptions; record approvals as part of the result.

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Speed, reliability and recovery: the hidden costs of an agent

Developers who compare these tools regularly describe Codex as the easier default when they already rely on GPT models, while OpenCode earns loyalty through provider choice, custom agents, and a more configurable working environment. Some report different completion times even with the same underlying GPT model. Those experiences are useful leads, not controlled evidence of a universal speed advantage.

For each task, inspect the final git diff, run tests independently and check for generated or untracked files. Count how many times the agent rereads a large part of the repository, retries the same failing command or asks for information already supplied. Those loops can waste context and time without appearing in a feature comparison.

Recovery deserves its own result. Stop a run after the first meaningful edit, then use the supported session-resume workflow. Does the agent recognise what was already changed? Does it preserve the acceptance criteria? Does it repeat a risky command? If an interrupted task needs a fresh prompt and manual repair, include that effort in the comparison rather than timing only the final successful attempt.

For longer refactors, measure the number of accepted changes after review, not the number of files edited. A large diff that quietly changes unrelated behaviour is worse than a smaller, verified patch. Keeping an AGENTS.md file with test commands and repository conventions can reduce repeated explanations, but hold its contents constant across the two runs.

Which should you install for your development workflow?

Start with Codex if you already use ChatGPT for coding, mainly want OpenAI models and prefer documented sandbox controls with fewer provider decisions. Its trade-offs include allowance management and a workflow less focused on swapping models at every step.

Start with OpenCode if you evaluate several model families, want to use local inference or need custom agent and provider configurations. Its trade-offs are provider troubleshooting, more configuration responsibility and the need to check permissions against the exact major release.

You can keep both available. The safer division of labour is separate branches or worktrees, not two agents editing the same files simultaneously. If you later need external tools, follow our focused OpenCode MCP setup guide or Codex CLI MCP setup guide; configuration details belong there rather than in this buying decision.

Decision rule: If changing the model is an occasional exception, begin with Codex. If choosing the model is part of every task, begin with OpenCode. Before committing either to a team, run the three bounded tasks against a pinned repository and keep the unsuccessful runs in the results.

Frequently asked questions

Can I use a ChatGPT subscription in OpenCode instead of paying for another API key?

OpenCode documents a ChatGPT sign-in option for eligible plans. It uses your account allowance; signing in does not create a separate quota. Confirm model availability and usage terms for the OpenCode release and account you are using.

Is Codex restricted to OpenAI models?

OpenAI models are the primary, supported default experience, but the Codex CLI can be configured for certain compatible alternative endpoints and local providers. That is narrower and more hands-on than OpenCode’s provider-oriented model picker. Test the exact endpoint before assuming full model compatibility.

Which is safer for private repositories?

Neither is automatically safe just because the code is open source or the terminal runs locally. Check where inference goes, which account stores usage data, what the agent can read, whether network access is allowed and how commands are isolated. Start with minimum permissions and a separate evaluation worktree.