Opencode Free Models

OpenCode Free Models

Concept of the skill

What it is: A cost-routing discipline for free and cheap agent models — deciding which open/open-weight model (and which free-tier lane) a given piece of work should run on, where each model's quality ceiling sits, and when the same work must escalate to a frontier model instead.

Mental model: Every task has a quality bar; every model has a quality ceiling and a cost. Cost-routing matches the cheapest model whose ceiling clears the task's bar, then escalates only on a concrete signal (looping, contradiction, low confidence, or the work crossing into reasoning/judgment). Free is the default lane only for work whose bar a competent open-weight model already clears.

Why it exists: Premium model capacity is finite and expensive; a large share of agent work (mechanical edits, bulk classification, high-volume triage) does not need a frontier model. Routing that work to a free/cheap model preserves premium capacity for work that genuinely needs it — but only if the ceiling/bar match is made honestly, not by reflex.

What it is NOT: It is not "always use the cheapest model" — that ignores the quality ceiling and silently corrupts work whose output is the quality bar. It is not the runtime (how to invoke a model) and not the loop (how to retry/claim). It is the per-task model choice.

Adjacent concepts: the runtime that reaches the model; the quality bar a task imposes; a model's quality ceiling and throughput; the escalation signal that promotes work to a frontier model.

One-line analogy: It is choosing the right gear for the grade — flat ground takes the cheap, fast gear; a steep climb needs the powerful one, and forcing the climb in the cheap gear strips the chain.

Common misconception: That a free model is interchangeable with a frontier model if you just retry enough — or, inversely, that free models are toys. Both are wrong: free open-weight models clear a real, sizeable band of work, but their ceiling is real, and pushing quality-creating or quality-judging work onto them produces confident garbage that poisons everything downstream.

Coverage

• The cost-routing decision: matching the cheapest model whose quality ceiling clears the task's bar
• What each free/cheap model family is good at (MiniMax M3, NVIDIA Nemotron, GLM-class, GPT-5 Nano-class) and its quality ceiling
• Which work belongs on the free lane: deterministic/mechanical edits, bulk classification/triage, high-volume extraction
• The escalate-on-signal rule — the concrete signals that promote work to a frontier model, and the warning against escalating by default
• The hard boundary: quality-creating and quality-judging work (grading, eval scoring, audit verdicts, architecture, security, correctness-critical synthesis) never goes to a free/cheap model
• The promotional, changing nature of free-tier rosters and how to confirm them at run time

Philosophy of the skill

Cost-routing is adversarial against two opposite reflexes. The first — "always use the cheapest model" — ignores the quality ceiling and silently corrupts any work whose output is the quality bar. The second — "free models are toys, use a frontier model to be safe" — wastes premium capacity on work a competent open-weight model already clears. The discipline is to refuse both reflexes and instead make the ceiling-vs-bar match explicitly, per task, escalating only on a concrete signal. The single non-negotiable line in that discipline is that retrying cannot raise a model above its ceiling, so quality-creating and quality-judging work — where a wrong answer poisons everything built on it — is always frontier work, never a cost tradeoff.

The routing decision

Match work to the cheapest model whose ceiling clears its bar. Escalate on signal, never by default.

Escalate to a frontier model when: the free model loops without progress, contradicts itself, returns a malformed or low-confidence result, the task crosses into reasoning/architecture/security, or the output itself becomes the quality bar (it gets graded, shipped as correct, or relied on as a decision). Do not escalate merely because a frontier model is available — that erases the cost lane.

Free-model capability table (2026 snapshot — verify the live roster)

The OpenCode Zen free roster is promotional and changes — treat any list as a snapshot and confirm at run time (opencode models opencode). Trial/free models warn against sending personal or confidential data.

Strengths and weaknesses of the free lane

Strengths: real cost savings on mechanical and high-volume work; open-weight models now clear a sizeable band of agentic/coding work; throughput options for background lanes; a free tier exists at all.

Weaknesses: the roster is promotional and unstable; trial models collect feedback and warn against confidential data; the quality ceiling is real and easy to overshoot; the cost saving is illusory if the output must be redone or, worse, silently corrupts a downstream decision. Free models are a mechanical-and-volume lane, not a frontier substitute.

Verification

Use this checklist to confirm a free-model routing decision was made correctly.

• The task's quality bar was named before a model was chosen (mechanical / high-volume / reasoning / quality-judging)
• The chosen free model's ceiling was matched to that bar — not picked purely on cost
• Quality-creating or quality-judging work (grading, eval scoring, audit verdicts, architecture, security) was routed to a frontier model, never a free/cheap one
• A concrete escalation signal (looping, contradiction, malformed/low-confidence output, scope crossing into reasoning) — not mere availability — was used to escalate
• The live free-tier roster was confirmed (opencode models opencode) rather than assumed from a stale list
• No confidential data was sent to a trial/free model that warns against it

Do NOT Use When

References

• references/model-facts.md — current (2026-06-08) free-model capabilities, benchmarks, and the routing table with sources.