The most reliable OpenCode setup is a tiered route: keep a stronger model for multi-step debugging, architecture, security-sensitive edits and work where rework is expensive; send lightweight transformations, title ideas and short explanations to small_model or another low-cost model. OpenCode supports separate model and small_model settings, so you can make that policy explicit instead of paying premium rates for every request.
No official source establishes one universally fastest or highest-quality model. Provider latency, context length, limits, outages and pricing vary, so select a sensible starting pair and measure it against your own repository and tasks.
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What OpenCode model routing is
Model routing is the policy that decides which language model handles each OpenCode request. A single premium model is simple, but it can waste money on work that needs little reasoning. A single inexpensive model can lower the bill while increasing retries, incorrect edits and review time. Routing treats model choice as an engineering decision based on risk, context and the cost of failure.
OpenCode exposes two important configuration roles:
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model: your primary route for normal and demanding coding work.small_model: a lightweight route for tasks that do not need the primary model’s full capability. OpenCode documentation says it tries to use a cheaper available model by default for these lightweight operations.
The names do not require that the second model be literally small. They describe how you intend to use it. A fast, lower-priced hosted model, a local model or a provider-specific low-cost variant can fill that role if it meets your quality and context requirements.
Choose the route by risk, not by model reputation
Start with the consequence of an error. A typo in a title is cheap to fix; an insecure authentication change can create an incident. Context length matters as well: a model that performs well on a short function may struggle when it must reason over an entire monorepo.
| Task | Default route | Why | Escalate when |
|---|---|---|---|
| Title generation, short rewrites and simple explanations | small_model |
These tasks are short, easy to inspect and usually tolerant of minor variation. | The output must follow a strict style guide, legal wording or a large set of constraints. |
| Formatting, straightforward renames and mechanical transformations | small_model |
The intended change is explicit and can be checked with a diff or formatter. | The transformation crosses file boundaries or changes behavior rather than syntax. |
| Routine feature work with clear acceptance criteria | Primary model |
Implementation requires planning, tests and interpretation of repository conventions. | The task is isolated, highly repetitive and has strong automated coverage; evaluate whether the small route passes your checks. |
| Multi-step debugging and failing integration tests | Primary model |
Diagnosis requires hypothesis generation, evidence weighting and coordinated edits. | Keep the stronger route when failures are intermittent, cross-service or production-impacting. |
| Architecture, migrations and broad refactors | Primary model |
These changes have high rework cost and require long-range consistency. | Use a second review route or human design review for irreversible decisions. |
| Authentication, authorization, cryptography and data-handling changes | Primary model plus human review |
The cost of a plausible but unsafe answer is high. | Always require tests, threat review and a maintainer’s approval; routing is not a security control by itself. |
| Very long-context analysis | Whichever route has sufficient context and output limits | A cheaper model that truncates the relevant files is not cheaper in practice. | Split the task into verified stages or move to a model with documented limits that fit the input. |
Make the policy visible to the team. A short routing note in the project contribution guide can state which classes of changes may use the inexpensive route and which must remain on the primary model.
Configure a primary and small model
OpenCode reads configuration from opencode.json or opencode.jsonc. Set the two model roles in the project configuration used by the repository:
{
"model": "provider/primary-model-id",
"small_model": "provider/low-cost-model-id"
}Replace the IDs with models that your configured provider actually exposes. Model names, availability and limits change, so copy the current IDs from the provider integration rather than assuming that a name from an older example still works.
Use project configuration for repository defaults
OpenCode’s configuration documentation states that project-level configuration has the highest precedence among its standard configuration files. Put repository-specific defaults there when you want every contributor or automation job in that project to start with the same routes. Keep personal preferences in a lower-precedence configuration only when they are intentionally different.
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Precedence does not remove the need to verify the effective configuration. If a request is unexpectedly expensive or uses the wrong model, inspect all configuration layers and environment-specific overrides before changing code or provider credentials.
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Configure a hosted provider or compatible endpoint
Provider definitions contain the provider package, model IDs, endpoint options, headers and token limits. For an OpenAI-compatible service, the relevant pieces are:
- the provider integration package;
- the service’s documented
baseURL; - the exact primary and small-model IDs;
- authentication and any required custom headers; and
- the model’s context and maximum-output limits.
Keep secrets out of the checked-in JSON. Use the credential mechanism documented by your provider, and restrict custom headers to what the endpoint requires. A syntactically valid configuration can still fail if the endpoint expects a different path, authorization scheme or model identifier.
Set limits deliberately
OpenCode provider integrations expose model-limit fields. Set context and maximum-output limits to values the upstream model and your account support. A limit that is too low causes truncation or incomplete patches; one that is unnecessarily high can increase cost and latency. Record the limits alongside the model ID so a later provider change does not silently alter behavior.
Build a small evaluation set before routing widely
The documentation does not provide a universal latency or quality leaderboard. Your repository is therefore the meaningful test environment. Build a compact set that represents the work your team actually performs:
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- Define pass criteria first. Specify required tests, files that may change, style constraints, acceptable explanation quality and whether a human must approve the result.
- Run each route on the same tasks. Keep the prompt, repository revision, tool permissions and timeout policy constant. Do not compare a warm cache on one route with a cold run on another.
- Record outcomes, not just token prices. Capture first-pass success, rework, elapsed time, timeout and retry events, input tokens, output tokens and cached-read tokens where available.
- Review the failures. Classify them as misunderstanding, missing context, tool failure, provider error, timeout or unacceptable code quality. A route that is cheap but needs two rounds of repair may have a higher effective cost.
- Set routing thresholds. For example, allow the small route for isolated edits that pass automated checks, but escalate automatically when the prompt exceeds a context threshold, touches protected paths or fails a test.
This is a local evaluation recommendation, not a published benchmark. Keep the task set small enough to rerun after a model, provider, price or repository change.
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Compare models and providers on six practical axes
Model quality is only one part of route selection. Evaluate the complete provider/model combination.
| Axis | Questions to answer | Evidence to collect |
|---|---|---|
| Output quality | Does the model produce correct patches, useful tests and explanations that match repository conventions? | Pass rate, reviewer ratings, escaped defects and rework count. |
| Latency | How long until the first token and the complete response? | Time to first token, total time, streaming smoothness and percentile results rather than one best run. |
| Token economics | What are input, output and cached-read prices? | Current provider pricing and your measured token mix. |
| Context and output limits | Can the route hold the relevant files and return a complete patch? | Documented context and maximum-output limits, plus truncation observations. |
| Reliability | How often does the service reject, time out or throttle requests? | Error codes, timeout frequency, retry count and availability during your working hours. |
| Operational control | Can you set a base URL, headers, retries, timeouts and model-specific limits? | Provider documentation and a tested configuration in your environment. |
OpenCode documents provider timeout controls and model-limit settings, and its integrations cover hosted services and local models. A local model may reduce per-token billing but can introduce hardware, setup, throughput and maintenance costs; measure the complete workflow rather than treating the token price as the whole bill.
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Use actual contract prices in OpenCode Console
OpenCode Console records request cost using model pricing. It also allows custom cost tiers when your negotiated rate differs from a provider’s public list price. Entering the rate you actually pay makes dashboards, budgets and route comparisons meaningful. If your contract changes, update the custom tier and note the effective date so historical reports remain interpretable.
For a rough estimate, calculate each request as:
((input_tokens × input_price) + (output_tokens × output_price) + (cached_read_tokens × cached_read_price)) / 1,000,000Use the prices and token categories supplied by the provider. Cached reads can have a different rate from ordinary input, and output tokens often cost more than input tokens. A route with a lower input price can still cost more if it generates long, correction-heavy responses.
Understand the listed OpenCode Go plans
OpenCode Go currently lists two subscriptions: $10 per month — OpenCode, 2026 and $40 per month — OpenCode, 2026. The $40 plan provides higher usage limits. Its model table lists per-million-token input, output and cached-read prices; one example is GLM-5.3-Flash at $0.15 input and $0.50 output per 1 million tokens — OpenCode, 2026.
These prices and model availability are volatile. Treat them as the values shown by the service for 2026, not a permanent quote, and recheck the live plan and model table before committing a team budget. A subscription limit is not the same as unlimited capacity: monitor usage, throttling and the tasks that consume the allowance.
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Reduce spend without hiding quality costs
- Route short, low-risk prompts to
small_model. - Keep prompts and file selections focused so the model does not repeatedly ingest irrelevant context.
- Prefer concise, testable task batches over open-ended requests that produce large speculative patches.
- Use cached reads where your provider supports them, while still checking that cached content is current.
- Track rework. Two inexpensive attempts can exceed the cost of one successful primary-model attempt.
- Review actual spend weekly and investigate sudden changes in token volume, retries or model selection.
Improve speed without sacrificing the result
Provider speed is workload-dependent. Measure time to first token and completion time separately: a route may begin streaming quickly but take longer to finish a large patch. Record timeout frequency because a nominally fast model that frequently retries can have worse end-to-end latency.
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Use the small route for bounded tasks that can be verified quickly. For larger work, a stronger model may be faster overall if it avoids failed attempts and review cycles. Keep timeout settings long enough for the largest legitimate task on that route, but not so long that a failed provider blocks the workflow indefinitely. OpenCode’s provider controls support timeout configuration; align those settings with your measured response distribution.
Do not infer speed from a provider’s marketing label or from one interactive run. Compare the same prompt, repository state, context size and output requirement across multiple runs, then use percentile results and failure rates.
Handle context and long tasks safely
Context limits are a routing boundary. Before sending a large repository task to the inexpensive route, estimate the files, history and tool output it must retain. If the relevant material does not fit, choose a route with a documented larger context or divide the work into stages:
- Ask for a repository map and a list of affected interfaces.
- Review that map and select a bounded component.
- Implement and test the component with only the necessary files in context.
- Run integration tests and ask the primary model to diagnose any cross-component failures.
Staging reduces accidental truncation, but it adds orchestration and review work. Use it when the task can be decomposed without losing invariants; keep tightly coupled architecture changes together on the stronger route.
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Distinguish model failure from provider failure
A poor patch, a timeout, a rejected request and an unavailable model are different events. Log the provider, model ID, elapsed time, token counts, status or error class and whether a retry occurred. This lets you decide whether to change prompts, raise limits, switch models or change providers.
Use controlled escalation
When a small-route attempt fails a test or returns an incomplete change, escalate the same task to the primary model with the failure evidence attached. Do not silently accept a partial patch. Keep the original diff so a reviewer can see what changed and why the escalation occurred.
If the primary provider is unavailable, a fallback is useful only when its context, tools and safety properties are adequate. Test fallback configurations before an outage; an untested endpoint, missing header or incompatible model ID is not a recovery plan.
Protect credentials and sensitive code
Model routing does not replace access control. Review provider data-retention terms, keep secrets out of prompts and configuration files, and restrict automated write permissions for sensitive repositories. For authentication, payment, personal-data and cryptographic code, require human review and automated security checks regardless of which model generated the patch.
A weekly operating routine
- Export Console usage and compare it with your expected task mix.
- Check which requests used the primary and small routes, including unexpected escalations.
- Review timeout, retry and provider-error rates.
- Sample successful and failed diffs for correctness and rework.
- Rerun the evaluation set after a model, provider, price or context-limit change.
- Update project configuration and the team’s routing policy when the evidence supports a change.
OpenCode Zen documentation says its recommended list is based on benchmarking model/provider combinations. Use that list as a starting point, then validate it against your own evaluation set. The available documentation does not establish a workload-independent ranking of fastest or best models.
Decision checklist
- Have you assigned a primary model and a distinct inexpensive route in
modelandsmall_model? - Are project-level defaults in the repository configuration with the highest standard precedence?
- Do both model IDs, credentials, headers, endpoint and limits match the provider’s current documentation?
- Can you identify which tasks are safe for the small route and which require escalation?
- Have you measured quality, rework, latency, tokens, timeouts and retries on representative tasks?
- Does Console use your actual negotiated pricing where it differs from list price?
- Have you rechecked the 2026 OpenCode Go plan limits and model prices before budgeting?
- Is there a tested fallback and a human review path for security-sensitive changes?
The practical policy is simple: spend premium reasoning where mistakes are costly, use the small route where correctness is easy to verify, and let measured results—not a universal ranking—decide when to change the split.
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