Choose an AI coding model for OpenCode by first confirming that its provider is available in your project, then comparing the model’s context and output limits, tool-calling support, and current provider billing against the work you actually do. OpenCode’s model suggestions are a useful starting point—not a current ranking or a guarantee that a model will suit your workflow.
What matters when choosing an OpenCode model
A model has to be usable in your current project before its capabilities matter. After confirming availability, compare the limits and capabilities that affect your tasks: how much input it can handle, how much it can return, whether it can use tools reliably, and what your provider charges for the tokens you use.
| Factor | What to check | Why it matters |
|---|---|---|
| Availability and setup | Provider credentials, project availability, selectable model ID, and any custom endpoint configuration. | A model listed elsewhere cannot be selected if its provider is unavailable or the model is not enabled for the project. OpenCode’s provider documentation covers provider setup; its model documentation explains model availability. |
| Context, input, and output limits | Check each limit separately, then consider the prompt, repository excerpts, tool results, and expected response size. | Context capacity is not the same as output capacity, and neither is a measure of coding quality. See OpenCode’s model configuration documentation. |
| Tool use | Look for documented tool capability; for custom or local models, verify the server and model configuration. | OpenCode cautions that code-generation ability does not guarantee reliable tool calling. Its Models documentation says only a few models are good at both. |
| Cost | Check current provider rates for input, output, and, where applicable, cached tokens. | OpenCode’s provider schema can represent per-million-token cost metadata, but rates and actual charges depend on the provider. |
| Hosted or local setup | Decide whether a hosted provider, a local model, or an optional OpenCode service fits your setup. | OpenCode supports hosted providers and local models, but the cited documentation does not provide a normalized price/performance comparison. |
Check that the model is available in your project
OpenCode draws on provider integrations and configuration, so a model’s appearance in a list or article does not by itself mean you can use it. Confirm the provider is configured and the model is enabled for the project. OpenCode says it supports more than 75 LLM providers, as well as local models; that is a vendor-published coverage count, not a measure of quality.
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Configure the provider and its credentials using the OpenCode provider documentation.
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In OpenCode, enter
/modelsto view and select models available to the current project. -
Use the provider/model identifier displayed by OpenCode. Do not guess an ID based on a model’s marketing name.
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If you want a default, configure it using the model identifier. For a single run, use the command-line
--modeloption with the identifier shown by OpenCode.
OpenCode also describes Zen as an optional curated offering with models its team has tested with OpenCode, and Go as an optional subscription for coding models tested by the team. These are additional provider and access paths, not proof that either is the best value for a particular user.
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Match context and output limits to the task
OpenCode’s model settings distinguish context, input, and output limits. Check all three rather than choosing by the largest context number alone. A task involving a few focused files may not need a very large context; a task that depends on substantial repository excerpts and tool results may need more input capacity. A long desired explanation or generated patch also calls for enough output capacity.
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Estimate what the model must take in: your instructions, relevant code, and results returned by tools.
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Separately consider how much the model must produce: a short fix, a larger patch, or a detailed explanation.
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Treat limits as capacity information, not evidence that a model will understand the repository better or use tools more reliably.
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When configuring a custom model, set known context, input, and output limits accurately. OpenCode documents fallback assumptions for custom models—including tool support and a 200,000-token context limit—but those assumptions are not facts detected about your model. Correct configuration matters if the actual model or server differs.
Verify tool-calling support, especially for custom and local models
Tool use is a separate selection criterion from code generation. OpenCode’s Models documentation warns: “However, there are only a few of them that are good at both generating code and tool calling.” A model that can write plausible code may still be a poor fit for a workflow that depends on invoking tools.
For a custom or locally served model, check the model’s documented capabilities and the server’s configuration. Discovery alone may not establish tool support: OpenCode’s vLLM discovery example does not report that capability. Make sure the configured capability reflects what the model and server actually support rather than relying on inherited defaults.
Ollama troubleshooting
If tool calls are not working with Ollama, OpenCode’s provider documentation suggests increasing num_ctx and starting around 16k–32k. This is a troubleshooting suggestion, not a guarantee that every model or local machine will support reliable tool use at those settings.
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Use OpenCode’s model list as examples, not a ranking
OpenCode’s Models page names GPT 5.2, GPT 5.1 Codex, Claude Opus 4.5, Claude Sonnet 4.5, Minimax M2.1, and Gemini 3 Pro as examples that work well with OpenCode. The list is not ranked. OpenCode explicitly says, “This is not an exhaustive list nor is it necessarily up to date”. The documentation does not establish that one of these models outperforms another in a controlled comparison, or that every one will be available in your project.
Compare cost using your provider’s current terms
OpenCode’s v2 provider schema represents input and output prices per million tokens, with optional cache pricing. To compare two available models, check each provider’s current billing terms and consider the same representative workload’s input/output mix, including cached tokens if applicable.
The OpenCode documentation cited here does not provide a consolidated live price table, a standardized cost-per-task test, or a basis for naming the cheapest model. Confirm rates directly with the provider before deciding; charges and pricing can depend on the provider and may change.
Test candidates on representative coding work
Since OpenCode’s examples are not a controlled benchmark, compare a small set of models you can actually select in your project. Give each the same kinds of work you expect it to handle, such as a focused code change or a task that requires using tools, and assess the results against your needs.
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Did the model handle the repository context and instructions you provided?
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Did it use the tools your workflow requires?
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Was its output within the limits you need?
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What would the task cost under the provider’s current billing terms?
Choose a default based on this fit rather than on a context-window figure, an example list, or a general claim that one model is best for every coding task.
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