OpenCode vs Claude Code: what differs, and what benchmarks show
Open source and any model against Anthropic's own client: plans, permissions, interfaces, and what published same-model tests show about the tool itself.
They are two answers to the same job. OpenCode is open source (MIT) and model-neutral: it runs Claude, GPT, Gemini, open models and local ones from 75-plus providers, can use a ChatGPT or Copilot plan, and works as a terminal app, a desktop app or a web interface. Claude Code is Anthropic’s own client, not open source, built around Claude models and the Claude plans, with a classifier-checked auto mode, an optional sandbox, hooks, and Remote Control from your phone.
On how well they do the work, the published evidence finds no reliable gap when the model is the same. The one official same-model result, Terminal-Bench 2.0 with Claude Opus 4.5, has Claude Code at 52.1% and OpenCode at 51.7%, and a smaller independent test that holds the model fixed finds gaps within its own margins, with the tools differing more in speed than in what got solved. So the choice turns on models, plans and how each works. One fact settles it for some people: a Claude Pro or Max plan can’t be used in OpenCode.
Side by side
As of October 9, 2026, with OpenCode 2.0.26 (the version its home page installs) and Claude Code 2.1.295:
| OpenCode | Claude Code | |
|---|---|---|
| Source | Open source, MIT | Closed: “All rights reserved” |
| Models | Any of “75+ LLM providers”, local models, and a few free ones | Claude models only, from Anthropic or a cloud provider |
| How you pay | Free, plus the model: an API key, a ChatGPT Plus or Pro plan, Copilot, or OpenCode’s Go ($10 a month) or pay-as-you-go catalogue | A Claude plan from $20 a month, or an Anthropic API key billed per token |
| Where you use it | Terminal, desktop app, a web interface from its own background server; editors through ACP | Terminal, VS Code, JetBrains, desktop app, web, and the Claude app on a phone |
| Out of the box | Everything allowed except paths outside the project and .env reads, which ask | Auto mode: a second model reviews each action and blocks what it judges unsafe |
| Sandbox | None: “OpenCode does not sandbox the agent” | An optional shell sandbox, off by default |
| Plan first | A Plan agent (Tab) that denies file edits; shell commands stay under the normal rules, which allow them unless you add one | Plan mode (Shift+Tab): no source edits until you approve |
| Instructions | AGENTS.md (version 1 also fell back to CLAUDE.md) | CLAUDE.md, or AGENTS.md when there is none |
| Skills | Its own folders, plus .claude/skills and .agents/skills | .claude/skills; nothing under .agents/ |
| Extending it | Plugins in JavaScript or TypeScript that subscribe to events; MCP | Hooks on 33 events, plugins, MCP |
| In CI | opencode run; version 1 has a GitHub agent | claude -p; a GitHub Action and app |
Two rows deserve a closer look. Permissions: OpenCode’s security policy calls its permission system “a UX feature to help users stay aware of what actions the agent is taking” that “is not designed to provide security isolation”, and its default rules let it run commands and edit files without asking. Claude Code starts in auto mode, where a classifier stands in for your approval, and can box its shell commands in a sandbox if you switch that on. The guide to running agents without prompts covers what each of those stops. And the phone: Claude Code’s Remote Control drives a session on any machine from the Claude app, while OpenCode’s route is its web interface opened in a phone’s browser, which the remote server guide sets up.
Does the tool change the result?
A coding agent is two things: the model that writes the code, and the program around it (the harness) that gives it tools, context and rules. Whether the harness changes the outcome with the model held fixed is what a comparison like this has to answer, and the published evidence is thinner than the number of “OpenCode vs Claude Code” articles suggests. The two strongest tests that put OpenCode and Claude Code on the same model, read at their sources on October 9, 2026:
| Source | Who ran it | Model | Result | Sample and limits |
|---|---|---|---|---|
| Terminal-Bench 2.0 leaderboard | The benchmark's own board | Claude Opus 4.5 | Claude Code 52.1% ± 2.5; OpenCode 51.7% | 89 terminal tasks. Claude Code run five times a task by the benchmark's team; OpenCode submitted by its maker, one run a task, version not recorded |
| OpenBench | One independent developer | GPT-5.6 Sol | OpenCode 81.0%, Claude Code 76.2%, Pi 76.2%, Codex 73.8% | 15 tasks, three runs each; intervals of about ±13 points, which its author says to read as ties. Median time: Pi 40 s, Claude Code 59 s, OpenCode 61 s, Codex 95 s |
The Terminal-Bench pair is the strongest result, because it is the benchmark’s own board, and it is still a weak one. The two entries were run differently: Claude Code’s by the benchmark’s team at five runs a task, OpenCode’s submitted by OpenCode’s maker at one run a task, below the board’s own five-run rule, which is why it shows no confidence interval. Four-tenths of a point between them means no detectable difference, not a winner. In OpenBench, Claude Code ran on another lab’s model, which is not how most people use it, and every gap sits inside the intervals it reports. Where it does separate the tools is time: they differ more in how long they take than in what they solve.
On whether the harness matters at all, the researchers disagree. The Terminal-Bench paper concludes “model selection is usually more important than agent scaffold when optimizing for performance.” A University of Michigan poster at an ICML 2026 workshop, fitting a model to the same leaderboard, finds “Harness ≈ LLM in impact”, though it draws on makers’ own submissions, one of which the board later removed for reward hacking. And TerminalWorld, 200 tasks from real terminal sessions, concluded “The results suggest that agent frameworks drive cost-effectiveness rather than shifting the model’s capability ceiling.” The same leaderboard shows one model ranging widely by agent (Opus 4.5 from 51.7% to 63.1%), but the top of that range is mostly makers scoring their own harnesses. And the benchmark’s team showed how fragile such gaps are: after fixing 28 broken tasks, Claude Code with Opus 4.6 went from 58.0% to 70.1% while their own minimal agent went from 62.9% to 63.8%, so the order of the two flipped.
What is missing: no large, independent test runs OpenCode and Claude Code on the same current model, and OpenCode’s only entry on an official board is the Opus 4.5 run above. Some comparison pages set OpenCode with one model against Claude Code with another; those measure the models, not the tools. For the closest pair we have tested ourselves, Claude Code against Codex on three real bugs, see Codex vs Claude Code.
Plans: what it costs to run each
Claude Code comes with every paid Claude plan, from Pro at $20 a month to Max at $100 or $200, or runs on an Anthropic API key billed per token; the Claude plans guide has the limits. OpenCode costs nothing itself, and you pay whoever runs the model: a key from any provider, a ChatGPT Plus or Pro plan through Sign in with ChatGPT (OpenAI lists OpenCode among the apps that may), GitHub Copilot, or OpenCode’s own Go and pay-as-you-go offers, set out in the OpenCode pricing guide.
The one hard line is Claude. Anthropic’s legal and compliance page says it does not permit “third-party developers to offer Claude.ai login into their own applications, or to route requests through Free, Pro, or Max plan credentials on behalf of their users,” OpenCode removed its Claude sign-in in March 2026, and its providers page now says “Anthropic explicitly prohibits this.” Claude models in OpenCode therefore run on an API key at API prices. If Claude is the model you want and you already pay for a Claude plan, Claude Code is where that plan applies; if you want one tool across several labs’ models, or a ChatGPT plan, OpenCode is.
Which OpenCode the reviews describe
OpenCode has two lines, and much of what is written about it describes the first. Version 2 (@opencode/cli, first released in September and the one the home page installs) changed things a comparison leans on: it reads AGENTS.md only, where version 1 also used CLAUDE.md; its docs say it “does not run language servers” and “does not support session sharing yet”, though the home page still advertises both; it has no github command, the GitHub agent being documented for version 1 only; and its plugins use a new interface. Version 1 (opencode-ai, 1.18.35) is still published. Check which one a review used, and which one you install.
And Codex?
Codex sits between the two: its command-line tool is open source (Apache-2.0) but built for OpenAI’s models, and it starts inside an operating-system sandbox with the network off, the most locked-down default of the three. OpenCode can run the same GPT models on the same ChatGPT plan, which makes the pair the easiest to compare like for like; OpenBench, above, finds no gap between them beyond its own margins. Our own run of Codex against Claude Code on three real bugs has the detail for that pair, and the Pi explainer covers the most minimal of the open-source agents.
Using both
Nothing stops you running both on one machine, and some of what you write for one carries over. A project’s instructions in AGENTS.md serve OpenCode and, when there is no CLAUDE.md, Claude Code; OpenCode reads skills from Claude Code’s own .claude/skills folder; MCP servers work in both. Both need a computer that stays on for work that outlasts your laptop’s lid. Everpod’s developer pod is that machine ready made: a cloud computer that is yours, always on, with your pick of Claude Code, Codex, OpenCode, Pi, Hermes and OpenClaw installed, reached only through your own private network, from $24 a month. A developer pod is one machine that stays yours: what you installed, the servers you left running, Docker and your work in progress are where you left them tomorrow.