opencode-free-models — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited opencode-free-models (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
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.
Match work to the cheapest model whose ceiling clears its bar. Escalate on signal, never by default.
| Work type | Route to | Why |
|---|---|---|
| Deterministic / mechanical (rename, format, transcribe, poll, convert) | A script first; else the cheapest free model (GPT-5 Nano-class) | No reasoning; a frontier model is wasted spend |
| Bulk classification / triage / extraction at volume | A high-throughput free model (Nemotron-class) | Throughput + a capped thinking budget; per-item bar is low |
| Large-context agentic coding on a big repo | MiniMax M3 Free (1M context) | Only free option with frontier-band coding AND a huge context window |
| Long iterative autonomous coding loop | GLM-class | Re-reviews its own strategy across many iterations instead of getting stuck |
| Quality creation or judging — grading, eval scoring, audit verdicts, architecture, security, correctness-critical synthesis | A frontier model — NEVER a free/cheap model | A weak judge or author silently poisons every downstream decision built on it |
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.
| Model family | Best at | Quality ceiling / tradeoff |
|---|---|---|
| MiniMax M3 (Free) | Frontier-band coding + agentic work; 1M-token context | Highest-capability free lane; the strongest free default for large-context coding |
| NVIDIA Nemotron 3 (Super/Ultra) | High-throughput reasoning, long-running agents, capped thinking budget | Great for volume/throughput lanes; not the top-end coding choice |
| GLM-5 / GLM-5.1 | Extended autonomous coding; strategy re-review over long loops | Strong agentic coding; weaker on pure reasoning/knowledge tasks |
| GPT-5 Nano / nano-flash-mini class | Cheapest, fastest; boilerplate, formatting, extraction, simple classification | Low ceiling for multi-step reasoning / correctness-critical synthesis |
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: 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.
Use this checklist to confirm a free-model routing decision was made correctly.
opencode models opencode) rather than assumed from a stale listInstead of opencode-free-models | Use | Why |
|---|---|---|
| Choosing or operating the OpenCode runtime (invocation, provider/model strings, Zen service) | opencode | That is the runtime layer; this skill is the per-task model choice |
| Authoring the agent loop / retry / escalation plumbing | autonomous-loop-patterns | This skill decides the model; the loop skill decides the control flow |
| Budgeting GitHub Copilot premium requests / AI credits | github-copilot | Copilot is a paid, IDE-native lane with its own economics |
| The work is quality-creating or quality-judging | A frontier model directly | A free/cheap model must never be the author or judge of the quality bar |
references/model-facts.md — current (2026-06-08) free-model capabilities, benchmarks, and the routing table with sources.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.