Last updated: 6 October 2026
Quick Answer: OpenCode suits teams that want an open-source agent across 75+ model providers, including local models, while Claude Code runs only on Anthropic's Claude models. OpenCode is free and MIT-licensed, and you pay your model provider; Claude Code comes with paid Claude plans or API credits and is the better choice for teams standardized on Claude.
The opencode vs claude code choice comes down to one question: do you want your coding agent tied to one model family or not?
Teams reach this comparison over model lock-in, spend they cannot attribute, or a policy that inference happens inside the network. The question gets typed both ways, claude code vs opencode and the reverse, with the same answer.
Is OpenCode a real alternative to Claude Code for teams?
Yes, for teams whose binding constraint is model choice or an internal gateway. OpenCode is an MIT-licensed terminal agent running against 75+ providers and several local runtimes, and its enterprise configuration can force every request through infrastructure you operate. It is not a substitute if your work depends on Claude models.
OpenCode is maintained by Anomaly, and its repository is anomalyco/opencode. The documentation states that OpenCode supports 75+ LLM providers and running local models, built on the AI SDK and Models.dev. Claude Code is Anthropic's own agent, and the trade is familiar: one model family, with the vendor's tooling around it.
| Dimension | OpenCode | Claude Code |
|---|---|---|
| License | MIT, open source | Proprietary, from Anthropic |
| Maintainer | Anomaly | Anthropic |
| Models | 75+ providers via the AI SDK and Models.dev | Claude models only |
| Local models | Yes: Ollama, LM Studio, llama.cpp, Atomic Chat, via OpenAI-compatible endpoints | No; non-Claude models unsupported through any gateway |
| How you pay | Free tool, you pay your model provider; or Zen, Go, or an Enterprise agreement priced by the seat | A paid Claude plan, or an API credential billed per token to its owner |
| Internal gateway | Yes: central config can restrict every request to your gateway and disable other providers | Yes: a base-URL variable, with allowedProviders pinning it as the only destination |
| Enterprise controls | Central config, SSO, provider allow-list, private npm registry | Managed settings files, a gateway credential per developer, allowedProviders pin |
| Data stored by vendor | "OpenCode does not store your code or context data"; sharing is manual by default, so a conversation reaches opencode.ai only when a developer runs /share |
Not on the gateway docs; your gateway holds the request log |
Capabilities as documented by each vendor on 6 October 2026; links in the text.
How do OpenCode and Claude Code differ on model choice?
The difference is categorical, not one of degree. OpenCode treats the model as configuration: one team can run a frontier hosted model while another runs an open-weight model on a box in the same rack. Claude Code treats the model as the product, and no setting changes that.
Can Claude Code use local or non-Claude models?
No. Anthropic's gateway documentation states that Anthropic does not support routing Claude Code to non-Claude models through any gateway. A gateway still buys a lot: server-side credentials, usage attribution, budgets, and an audit log of every model request. It does not widen the model list. Reading "self-hosted gateway" as "self-hosted model" imports a capability that is not there.
Which local model runtimes does OpenCode support?
Four are documented, each reached as an OpenAI-compatible provider: Ollama on port 11434, LM Studio on 1234, llama.cpp's llama-server on 8080, and Atomic Chat on 1337. The pattern is a baseURL plus a model ID, so anything speaking the same API can be added. Note the limit: the documentation describes local models, not a guaranteed offline mode, so prove network isolation yourself.
Which one can run on-premise or on your own cloud?
Both can be made to talk only to infrastructure you control, but only one can run the inference there. OpenCode can point at local models or at your internal gateway. Claude Code can route through your own cloud account or a self-hosted gateway, yet the model is still a Claude model.
For OpenCode, the enterprise documentation describes a central config that integrates with your SSO and your internal AI gateway, with the option to disable every other provider. One caveat: the optional /share command sends the conversation to pages hosted at opencode.ai, and the vendor recommends setting "share": "disabled".
For Claude Code, the supported upstreams are an Anthropic Console account, Amazon Bedrock, Google Cloud's Agent Platform or Microsoft Foundry, and Anthropic ships its own self-hosted gateway with SSO sign-in and OTLP telemetry. That covers data residency and account ownership, but not a network with no route to a hosted Claude endpoint, because there is no open-weight fallback.
How do you put an internal LLM gateway in front of both?
One gateway serves both agents, because both talk to an HTTP endpoint you choose. Give it the provider credential, then issue each developer a gateway credential instead of a provider key.
The client-side pins differ. OpenCode uses central config to allow only the internal gateway. The Claude Code gateway documentation describes a managed settings file carrying the base URL plus allowedProviders set to ["customEndpoint"], after which Claude Code refuses a session pointed anywhere else. On the gateway, an open-source option such as LiteLLM gives one OpenAI-compatible interface with spend tracking and per-key budgets, in front of hosted and self-hosted models at once. The trade-offs are in self-hosted LLM gateways compared.
What does each cost a 20-person team?
The two cost shapes differ enough that a straight per-engineer comparison misleads. With OpenCode you buy model tokens and, optionally, seats. With Claude Code you buy either plans or tokens, and the tool comes with them.
OpenCode's position is that the tool is free and you pay whoever serves the model. Three optional paid routes exist: OpenCode Zen, a hosted set of models the OpenCode team tests and bills through your Zen account; OpenCode Go, described as a low-cost plan for popular open coding models; and OpenCode Enterprise, which the vendor prices by the seat and says carries no token charge if you bring your own LLM gateway. For twenty engineers already running a gateway, that clause decouples seat count from the token bill.
Claude Code's cost rides on the credential in play. A developer's paid Claude plan covers their own usage within that plan's limits. Point the tool at a gateway with a gateway credential and the documentation is explicit: the plan's limits no longer apply, and traffic is billed per token to whoever owns the credential. Set only the base URL without a credential and the plan stays the billing path, a common and expensive surprise. Figures for each plan are in Claude Code pricing.
Which is better for large codebases and multi-file changes?
Neither wins this on name. Quality on a big repository is mostly set by four things you control: the model behind the agent, how much relevant context reaches it, the project instruction file, and how tightly you review the diff.
Hold the model constant first, which is possible because OpenCode runs Claude models as one provider among many. Instruction files overlap too: OpenCode reads AGENTS.md in the project, with a global file at ~/.config/opencode/AGENTS.md and CLAUDE.md as a fallback. Claude Code reads CLAUDE.md and can also read a repository's AGENTS.md files, on their own or alongside it. One well-written instruction file can drive both, which makes a fair bake-off easier:
- Pick ten real tasks from your backlog, each touching three or more files.
- Fix the model and the instruction file so the harness is the only variable.
- Give each agent the same starting commit and the same time budget.
- Score the diffs: correctness, files touched that should not have been, review comments per pull request, tokens consumed. Keep the ten tasks as a regression set.
What should security and compliance teams check?
Ask for five artifacts rather than a vendor comparison: the data path, the request log, where keys live, the model allow-list, and who can change any of it. Both tools can satisfy a reviewer; the evidence differs.
| Check | OpenCode | Claude Code |
|---|---|---|
| Data path | Local processing or a direct API call to your provider; vendor stores no code or context data | Anthropic, your cloud account or your gateway, per the base URL |
| Request log | The internal gateway in front of it is your log | Anthropic lists audit logging of every model request as a gateway benefit |
| Credentials | Provider keys sit in ~/.local/share/opencode/auth.json unless SSO and central config supply gateway credentials |
The gateway keeps the provider key server-side and issues each developer a revocable credential |
| Model allow-list | Central config can disable other providers; whitelist and blacklist restrict the model picker |
allowedProviders pins the gateway as the only permitted endpoint |
| Residual exposure | /share uploads that one conversation to opencode.ai; "share": "disabled" removes the option |
A base URL set without a gateway credential falls back to the developer's own plan |
Controls as documented by each vendor on 6 October 2026; links in the text.
Two items belong on the list whichever tool wins: what the agent may execute without a human, and whether offboarding is a single revocation.
When should a team use both, or neither?
Run both when the work splits cleanly: Claude Code for Claude-heavy agent tasks, OpenCode where the model has to be something else, including an open-weight model you host. One instruction file and one gateway serve both, so the second tool costs a config entry rather than a parallel platform.
Run neither, for now, when policy needs one governed path before anyone gets an agent. If you cannot say which engineer spent what against which model, stand up the gateway and the audit trail first. The payoff shows up in delivery metrics rather than tooling spend, the subject of how much AI can cut release cycle time.
Teams weighing opencode vs cursor are asking a narrower question, about the editing surface rather than the model path; Cursor's own enterprise case is covered in Cursor vs Claude Code.
What mistakes should teams avoid when rolling out either agent?
Two errors recur, and both are organizational rather than technical.
- Handing out agents before the gateway exists. You lose attribution for the month that matters most, when usage patterns are still forming.
- Expecting a gateway to widen Claude Code's model list. Budget, logging and credential hygiene improve; the model family does not.
How Origins AI Coding Tool sits under both agents
Both agents above need an endpoint, and that endpoint is what Origins AI (originshq.com) sells. Its product page presents the Origins AI Coding Tool as a self-hosted AI coding assistant and LLM gateway for your own infrastructure, exposing an OpenAI-compatible REST API. OpenCode adds it as a provider with a baseURL; Claude Code can use it as the gateway its managed settings pin, for Claude models.
The company reports routing to OpenAI, Anthropic, Meta Llama, Mistral, CodeLlama, DeepSeek Coder or a model you supply, and says the gateway tracks every request by team, project and engineer with per-team quotas. The page also lists local request logging, secrets and PII filtering, and RBAC. Deployment modes listed are on-premise, private cloud in your own AWS, Azure or GCP account with VPC isolation, hybrid, and an air-gapped mode it says runs locally hosted models with no internet connection required.
In on-premise and air-gapped modes the page states no source code is sent to any external service; in hybrid mode, by its own wording, the code context submitted to the model does leave your network. The product is deployed in your environment by an implementation team rather than licensed by the seat, and the company does not publish a rate card. Where a team needs only model routing and spend visibility, an open-source gateway you run yourself is the cheaper answer.
Talk to an engineer
If the blocker is governance rather than tool choice, start with the gateway and the audit trail, then pick agents. Bring your model spend and your security review questions, and an engineer will map which controls you already have. Book a call.