Best OpenCode Models: 7 Checks for Choosing Hosted, Local, and Free Options

The useful answer is not one universal winner. Start with a reliable hosted model for complex reasoning, use a balanced coding model for routine edits, choose a local model when privacy or offline work matters, and treat free or included models as a way to test a workflow rather than a permanent quality guarantee. This guide turns that choice into a repeatable check instead of a model-name guessing game.

A model that is excellent at a long architectural plan can be a poor default for every small edit. Separate the work into four buckets: reasoning and planning, implementation, explanation and documentation, and quick transformations. Planning needs consistency across many steps; implementation needs accurate code edits and useful context handling; documentation values clarity and speed; quick transformations should not consume an expensive model when a smaller one is sufficient.

The strongest starting point for best OpenCode models is therefore a task profile. Write down the expected output, the cost of a wrong answer, the amount of repository context, and whether the model must call tools. A read-only explanation can tolerate a fast model. A migration that changes dozens of files deserves a stronger model and a human-reviewed diff. This boundary keeps a recommendation useful even when provider catalogs change.

Do not use the phrase best model as a substitute for a requirement. If the task is mostly autocomplete-like editing, latency may matter more than maximum reasoning depth. If the task is a production incident investigation, reliability and traceability matter more than a small price difference. Use the model selection flow in the next sections to make that trade-off explicit.

Hosted models are usually the simplest first benchmark because the provider manages the runtime and the model is available through a stable endpoint. They are a good fit when you need stronger reasoning, a large context window, or predictable tool behavior and the project can send code context to that provider. The trade-off is network dependence, provider policy, and usage cost. Check the provider terms before sending proprietary material.

OpenCode local models are a different operating model, not merely a cheaper hosted model. They depend on memory, compute, quantization, runtime configuration, and the model's tool-calling behavior. A local model can be the right choice for sensitive code, offline work, or experimentation, but it may need smaller prompts and more explicit instructions. Use the site's dedicated OpenCode Ollama guide for the connection and runtime details; this page focuses on deciding whether local inference fits the job.

A subscription or included model path can be convenient for a team that wants one billing boundary and a quick start. It still needs a capability check: included access may have quotas, model availability can change, and a plan can be a poor fit for one large repository if context or tool limits are restrictive. Keep plan comparison separate from model selection so you do not mistake a pricing product for a capability guarantee.

Context length is only useful when the model can use the relevant context accurately. Sending an entire repository may increase noise and cost without improving the answer. Start with the smallest set of files that proves the task, then expand only when the model needs more evidence. For OpenCode, this means checking what the agent can read, which tools are enabled, and whether the prompt asks for a concrete output instead of a general opinion.

Latency changes how a model feels in an interactive terminal. A slower model may be worthwhile for one high-risk plan, while a fast model is better for repeated small edits. Reliability includes more than uptime: it covers consistent formatting, correct tool selection, resistance to unsupported assumptions, and predictable failure behavior. Privacy is a project constraint, not a marketing adjective. Decide what code may leave the machine before selecting a provider.

A practical comparison records one task on each candidate with the same prompt and context. Note time to first useful response, total time, changed-file count, factual errors, tool errors, and whether the result needed substantial correction. Keep the result next to the model identifier and date. This makes a model recommendation auditable instead of relying on a reviewer's memory.

OpenCode model configuration follows the provider/model pattern. The exact provider identifier and model ID belong to the provider's current documentation, so use placeholders until you verify those values. Keep the choice explicit in the project or user configuration that matches the scope you intend. A repository-wide default should be easy for another developer to read, while sensitive credentials should remain in the provider's credential flow or environment variables.

The smallest useful configuration is easier to review than a large copied catalog. Start with one default, make the first task read-only or low risk, and record why the choice fits the repository. If a teammate needs a different model, document the reason and the expected trade-off rather than silently changing the shared default. The official OpenCode config and models pages are the authority for schema and provider behavior.

{
  "$schema": "https://opencode.ai/config.json",
  "model": "provider/model-id"
}

Do not evaluate a model by asking for a clever demo. Use a small benchmark that resembles the work you actually do: explain one unfamiliar module, propose a plan without editing, implement one bounded change, and review the resulting diff. Use the same repository context, prompt, and acceptance criteria for every candidate. A model that sounds impressive but misses a constraint should not become the default.

Record the result in four dimensions: correctness, completeness, interaction cost, and risk. Correctness asks whether the code or explanation is right. Completeness asks whether it addressed the whole request. Interaction cost includes waiting and follow-up prompts. Risk covers secrets, destructive commands, unreviewed writes, and unsupported claims. This is more actionable than a single model score.

Switch models when the failure pattern is stable, not after one unlucky response. If only long-context tasks fail, reduce context or choose a model with a better context fit. If tool calls fail, inspect permissions and provider support before blaming the model. If local inference is slow, test a smaller model or narrower prompt before abandoning the privacy requirement.

1. Freeze the taskUse one representative task, repository snapshot, prompt, and acceptance checklist.
2. Run read-only firstAsk for assumptions and a plan before allowing a write-capable tool.
3. Measure the resultRecord useful output time, errors, follow-up prompts, and changed files.
4. Inspect the diffReject unsupported edits, missing tests, or unexplained files before comparing speed.
5. Choose a defaultKeep the model that meets the task threshold with the lowest practical cost and risk.

The first mistake is optimizing for a model leaderboard while ignoring the actual repository. Benchmarks may use different prompts, context, tools, and dates. A model can rank highly and still be inconvenient when it cannot access the required provider, exceeds the team's budget, or produces unstable patches. Use public comparisons as hypotheses, then verify with a small local benchmark.

The second mistake is treating free models as identical to paid or included models. Free access can be useful for learning the command flow and checking a prompt, but quotas, queue time, context, and availability are part of the experience. Keep free models in the low-risk lane until they pass the same benchmark as the intended production choice.

The third mistake is changing the model before checking configuration and permissions. A missing provider key, incorrect model ID, blocked tool, or oversized context can look like poor model quality. Confirm the provider, model identifier, authentication, and active configuration source first. Then use the existing JSONC config and MCP guides to isolate the layer that actually failed.

Model catalogs and provider policies change faster than a repository's basic workflow. Keep the durable part of the decision in your benchmark: task type, context boundary, acceptance checks, and rollback path. Update only the model identifier and measured notes when a provider changes its catalog. This keeps the article and the project configuration honest without claiming that one model is permanently best.

For the next step, use the related guides according to the boundary: the Ollama guide explains local model connection, the Go and Zen comparison explains subscription choices, and the JSONC guide explains configuration structure. Those pages should answer their own intents. This page is the decision layer that helps you choose which path to test first.