OpenCode Zen Review: Free AI Models for Coding That Actually Work

I spend most of my days on email infrastructure and Linux servers, which means a lot of repetitive work: config review, small scripts, log triage, cleanup chores. For that kind of thing a small model is plenty, and paying premium prices for it never made sense to me. OpenCode Zen currently hands you a pool of free models inside a tool that takes about two minutes to set up. This is the current lineup, where it falls short, what a real week of use looks like, and whether it deserves a spot on your machine. I originally wrote this review when the free tier had a different set of models, and the rotation since then is itself part of the story.

What OpenCode Zen is

Zen is not a model. It is a gateway run by the OpenCode team, and that distinction explains most of its value. OpenCode itself is an open source coding agent that runs in your terminal, and the annoying part of setting one up has always been collecting API keys. Instead of registering at OpenAI, then Anthropic, then Google, then chasing down which of them just changed their pricing page, you create one account, grab one key, and get a catalog of models the team has already tested with the tool. The team benchmarks model and provider combinations before listing them, because the same model served by two different providers can behave very differently behind a coding agent. When a model misbehaves in the catalog, they hear about it, and either fix the integration or drop the model.

Two things live under this roof. Zen is pay per use for premium models, with a handful of free ones on the side. There is also OpenCode Go, a $10 per month plan that covers their hosted open source models only. The free models sit on the Zen side, and that is the part this review covers. If you outgrow free later, the same account and key carry you into the paid catalog, so nothing you set up today gets thrown away.

One point that trips people up: the free models still require billing details at sign-in. Nothing gets charged unless you deliberately pick a paid model, but the card requirement is there from day one. If that is a dealbreaker for you, better to know now than after installing everything.

The current free lineup

Here is where the rotation story gets concrete. When I first wrote about Zen, the free pool had seven models including gpt-5-nano and DeepSeek V4 Flash. Today the free shelf looks like this:

• Ox Alpha Free (x-preview-f-free) - the anonymous stealth model everyone has been arguing about since August 2026. A 1M context window and coding-agent focus make it the headline of the free pool. I wrote a full hands-on evaluation of it separately, because a model with no named operator deserves its own privacy conversation.
• MiMo-V2.5 Free (mimo-v2.5-free) - Xiaomi's model on a zero-dollar ticket. Fast on routine edits and surprisingly tidy with structured output.
• Hy3 Free (hy3-free) - the experimental one. A 190K context window, the largest in the free pool after Ox Alpha. Worth watching, not worth depending on, since preview status means subject to change without ceremony.
• Nemotron 3 Ultra Free (nemotron-3-ultra-free) - NVIDIA's entry. Decent reasoning for its class, occasionally surprising in a good way on logic-heavy chores.
• Nemotron 3.5 Lightning Free (nemotron-3.5-lightning-free) - the speed variant. My pick for mechanical changes across known file types, the kind where you already know the shape of the answer and just want it typed out.
• Muse Spark 1.2 Contributor Free (muse-spark-1.2-contributor-free) - the newest addition, served through the responses endpoint rather than chat completions.

Notice what changed and what did not. Big Pickle, the community favorite with the ridiculous name and the output quality that had no right to be that good, moved to the paid side of the catalog along with MiniMax and the gpt-5-nano that used to anchor the free tier. That is the rotation tax in action. Treat the lineup as a pool, not a contract. If your workflow depends on exactly one free model existing forever, that is a dependency you should not have at $0.

A week of actual use

To be clear about what these models can carry, here is an honest sample of one week of mine, all done on free models:

Monday: reviewed a 40-line SPF record mess for a client and had a free model draft the flattened version while I checked it against the lookup limit. Tuesday: Nemotron Lightning wrote a log parser script that pulls bounces by reason code from a week of Postfix logs, my favorite kind of boring, perfect kind of task. Wednesday: quick questions about DMARC report XML structure, answered well enough that I only corrected one detail. Thursday: shell snippets for a server cleanup checklist, moving old logs and rotating certificates. Friday: summarized a client's month of DMARC aggregate reports into plain English for their non-technical owner.

Notice what is missing: architecture decisions, tricky cross-file debugging, anything with real stakes. Those still go to a paid model or a human brain. The free tier did not replace my main tools. It absorbed the pile of small work that was cluttering my day, which is a different and quieter kind of win.

Where free models fall short

Small models are small. The failure modes are predictable once you have seen them a few times. They invent function names that sound right and do not exist. They confidently explain a flag your tool does not have. They solve the problem you described instead of the one you have. On long refactors they fix the first file and quietly forget the second. None of this is fatal if you review output like you would review a junior's pull request, and all of it is fatal if you don't.

Rate limits exist too. At busy hours a free model may queue or refuse requests for a bit. For interactive work that is a minor annoyance, you wait two seconds or switch models. For a script or CI job it is a reason to add retries and a fallback model, or to just use a paid model where a few cents buys you predictability.

The last honest limit: free tiers exist to show you the product. That is fine, it is a fair trade, but it means the lineup, the limits, and the terms can shift whenever usage patterns shift. The rotation I described above is proof, not speculation. Build your workflow so any model in the pool can be swapped for another in a minute, and nothing here can hurt you.

What it costs next to the alternatives

The honest comparison, including the parts people forget:

Setup	Cost	What it takes to start
OpenCode Zen free models	$0	About 2 minutes, one account, card on file
Ollama with local models	$0 plus hardware	GPU, multi-GB downloads, upkeep
GitHub Copilot free tier	$0, capped completions	Account, editor setup
OpenCode Go	$10 per month	Account, covers hosted open source models
Claude Code or Codex with API keys	Pay per token	Keys, spending caps to configure

Ollama is the interesting one. It is free forever and private, but you trade money for time: downloads, driver quirks, model updates, and a machine that is always partly busy being a server. If you already have a spare GPU box, go local, it is genuinely great. If you don't, that free option costs a few hundred dollars of hardware you would have to buy first, and Zen gets you most of the way there for none of that.

Copilot's free tier is fine for editor autocomplete, but it lives inside the editor. If your work happens in terminals, over SSH, and in scripts, a terminal agent fits the shape of the work better.

Setup in about two minutes

1. Install OpenCode with the one-liner from the docs (curl -fsSL https://opencode.ai/install | bash on Linux and macOS).
2. Run opencode inside a project folder.
3. Type /connect and pick OpenCode Zen.
4. Sign in, add your billing details, and copy the key. Yes, billing details, even for the free models. Nothing gets charged unless you use a paid one.
5. Paste the key into the terminal prompt.
6. Run /models and pick one of the free models.

That is the whole setup. No config file, no environment variables to hunt for. If the model list looks short, run /models again after connecting, the list populates once the key is live.

Using Zen outside the editor

Zen also works as a plain API, and this is the part I care about most as someone who automates things. The gateway exposes OpenAI-compatible chat completions and Anthropic-compatible messages endpoints, so anything that talks to either vendor can talk to Zen: set the key, point the client at the Zen base URL, done. A minimal curl against a free model looks like this:

curl https://opencode.ai/zen/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nemotron-3.5-lightning-free",
    "messages": [{"role": "user", "content": "Summarize this log line: ..."}]
  }'

Free models are a natural fit for chores like summarizing logs, drafting commit messages, classifying bounce reasons, or pre-filling ticket replies, and since the endpoint is OpenAI compatible, existing scripts need only a base URL change. Anything you already wired to OpenAI can be pointed at Zen without touching the rest of the code.

Teams get a few extras worth knowing. Workspace admins can set monthly spending limits per member, which is exactly the control you want before handing agents to people who generate enthusiasm faster than they generate caution. Admins can switch individual models off, handy when a model's data policy does not fit yours. And members can plug in existing OpenAI or Anthropic keys while keeping the rest of the catalog, so a company that already has an OpenAI relationship does not have to choose between it and Zen.

Privacy and your code

Worth saying plainly: every cloud model, free or paid, means your prompts leave your machine. That is the deal, and it is the same deal with any hosted AI. Zen's published position is that all models are hosted in the US, providers follow a zero-retention policy, and your data is not used for training, with published exceptions. Two practical notes from running client infrastructure: read those exceptions before sending anything sensitive, and use the workspace model toggles, since an admin can disable a model whose policy does not pass review before anyone on the team sends it a single line. If you handle client data with strict confidentiality terms, the truly safe option remains local models, at the hardware cost that implies.

If you outgrow the free tier

The paid side of the catalog has grown in every direction: the GPT 5.x family up to GPT 5.6 variants, Claude through Opus and Sonnet and the new Fable, Gemini up to 3.7 Flash and 3.1 Pro, Grok, Qwen, DeepSeek V4, MiniMax M3, GLM 5.2, and the Kimi line through K3. Pricing is public and per request, the balance auto-tops-up $20 when it drops to $5, and per-workspace caps keep the bill from surprising you. It is not automatically cheaper than going direct for every model, so compare for the ones you actually burn tokens on.

A sane default: start on a free model and switch to a paid one only when a task clearly needs it. You will feel the moment. The free model gives you three paragraphs of confident guessing on something architectural, and that is your signal. Most requests never hit that point, and for the rest, a few cents of paid model is cheaper than your time.

Common questions

Do the free models really cost nothing? Yes, with the card-on-file caveat at sign-in and the rotating lineup caveat over time. No hidden metering on the models marked free.

What happened to gpt-5-nano, MiniMax M2.5, and Big Pickle? They rotated to the paid side of the catalog. This is normal behavior for the free tier and the reason this review describes the lineup as a pool rather than a promise.

Can I use Zen without using OpenCode? Yes, through the OpenAI-compatible API endpoint. The gateway works fine as a plain model API with any agent.

Is my data used for training? Zen's published policy says zero retention and no training, with listed exceptions. Check the current terms for any model you plan to send sensitive code to, and keep the workspace toggles in mind as your enforcement lever.

What happens when a free model disappears? You pick another from the pool. If your workflow is model-agnostic, and it should be, the swap takes under a minute.

Why not just run Ollama? If you have the hardware and enjoy the upkeep, do. Zen is for everyone else: the setup cost is two minutes instead of a weekend.

Who should skip it

If you do heavy architecture work all day, you already know you need Claude or GPT Pro, and this will not replace that. If you work offline or air-gapped, Ollama is the right answer and always was. If a rotating free lineup annoys you more than a $10 subscription would, bring your own key or buy a plan and stop thinking about it. Everyone else has little reason not to keep Zen's free models in a back pocket.

Verdict

Most free tiers in this space are demos with extra steps: caps that run out by lunch, models from two generations ago, or a checkout page hiding behind the second prompt. This one is not that. A real pool of usable models, one account, two minutes of setup, no subscription, and an API you can wire into real automation the same afternoon. The rotation means the specific names will keep changing, and that is fine, because the thing worth trusting here is not any single model. It is the gateway: one key, tested integrations, honest limits, and a free shelf that has stayed stocked through every reshuffle so far. For the daily pile of small work it is real money saved and real minutes given back. If the current pool is missing a model you want, the newest mystery addition has its own writeup in my Ox Alpha review.

Updated August 2026 to reflect the current free lineup and catalog. The model pool rotates, treat specifics here as a snapshot, not a permanent menu.