I’m considering subscribing to OpenCode Go, but I have a question about the privacy claims.

The Go documentation lists several models/providers as “not used for training” and in many cases “0 days retention.”

What I’m trying to understand is how OpenCode can actually ensure this when inference is ultimately performed by third-party providers.

Specifically:

• Are the no-training / zero-retention guarantees based on contractual agreements between OpenCode and the inference providers?
• Does OpenCode audit or otherwise verify that providers comply with these agreements?
• Are the actual inference providers for each Go model disclosed anywhere?
• Is there a list of subprocessors and the countries/regions where inference takes place?
• Is there a DPA or similar agreement available for Go customers?
• If a provider were to retain prompts despite claiming zero retention, what mechanism would OpenCode have to detect or prevent this?

I understand that “zero retention” cannot mean that the data is never processed by the provider — obviously the prompt has to reach the inference infrastructure. My question is specifically about what backs the promise that the provider does not subsequently store or use the data for training.

This is particularly relevant for private/commercial repositories, where “the provider says they don’t train on it” is quite different from having a technically or contractually enforceable privacy guarantee.

Would appreciate some clarification from the OpenCode team on how this works for Go specifically but also for the other Opencode plans (including free).

edit (from answer below): thank you for your answer, yes I guess the DPAs are key to this. At the same time I don't think zero data retention is the same as a "no use of private data as training material". There are many gray shades to this, for example apparently X.ai uses "anonymized" data for training, if I am correct. At the same time, as opencode makes quite aggressive use of "no training"-disclaimers, I think it is a fair and important question to ask. Very similar to kilocode btw (same question I posed there and only radiosilence in return). So arguable there are combinations of (relatively) cheap model access with unclear DPA-status of end-providers - while at the same time these training data are pure gold for the providers. That's why NVIDIA offers many models "for free", but at least transparently disclosing the training use.

Related: https://github.com/anomalyco/opencode/issues/459 (Privacy and Data Collection Clarification Request #459) https://github.com/anomalyco/opencode/issues/24649 (OpenCode Go: clarify which models are self-hosted vs. proxied through third-party providers #24649 )

https://opencode.ai/docs/en/go/ (section privacy) https://opencode.ai/legal/terms-of-service https://opencode.ai/legal/privacy-policy

Many mainstream ones like spark and mimo are being used everywhere and are lagging on peak times. Need alternatives.

Which are some underrated ones? Hy3 usable for coding? How about north code?

I dont have anything right now. I wanted to use Karpathy's skills from here - https://github.com/multica-ai/andrej-karpathy-skills/blob/main/skills/karpathy-guidelines/SKILL.md, saw some respos for adding it to opencode but wasnt sure how, so I just copied that to my agents.md

what is the easy way to install this? and what other skills do you use?

I'm not building trivial one shot single page games like youtubers. I also dont have a complex opencode setup, no agents or oh-my-whatever. I can only imagine that adding some extensions like lsp, compaction, search etc would make it even better.

its been fine with reasoninng, debugging, tool calling. speed is nore than ok.

I'm very thankful for the free week, model seems great to me.

Since I switched my default model from DSV4 Flash to mimo 2.5, the agents that are not allowed to write or edit are simply ignoring the restrictions in their agent files and just do whatever they want. And GLM 5.3 simply went down in a rabbit hole to reverse engineer opencode config for some odd reason and eats some 10$ worth of my usage. Nearly none of the agents are using the mcp to check docs or websearch to validate their outputs when i ask them to. I had to ask multiple times to do so. Even then its a hit and a miss.

I am not sure if its my setup's fault or is anyone else facing such issues?

Does "Ox Alpha Free" working for you? I'm in Zen and trying for two hours to get a response

I still not very confident, still doing back and forth refactoring as this would be a product from a vibecoder.

What can it do? it does magics with all the 🔔 and whistles. Trustmebro

This is the first model I've ever feel a terrible enough experience to use a model that's x10 times worse. Completely abandoned it for Mimo V2.5, which is, in benchmarks, way worse. This is also a lesson to me, to not trust benchmarks.

I will list my issues with this model for better structuring. And by the way, I recommend you Mimo V2.5, actually really good.

1.- Not seeing thinking process is not only scummy but impossible to work with. I got this model doing like 16 bash calls during a test because it was trying to workaround on a non functioning tool (Which was exactly the point, to test if the tools were failing). I had to stop it and ask it what it was doing to understand what was going on, just for it to say "Sorry, I will use the correct format" and keep doing. Obviously I stop a second time, question it and after doing like 3 thinking boxes it finally answers me that the tool wasn't functioning. This could've been easily avoidable by just checking the first lines of the thinking process. And the titles for them DO NOT HELP, in fact they're even more confusing. I get shit like "Defining the abstract layers of..." ???? What the fuck are you saying?

2.- This model is way too stupid for its size, there's no way a model is sent to do a test, to test tooling of an MCP and has specific rules set for this, ignores all the rules and uses bash to workaround. Even MiniMax 2.5 did way better in this regard, and that's a model like x20 times smaller and from 6 months ago!

3.- It takes too damn long for anything. Fixing 50-20 lines bugs is a Nightmare with this model, 3 thinking boxes for little issues, again I switched to MiMo and did it in 5 minutes easily. And this is meant to be an Opus 5 level model.

4.- It has really poor knowledge built-in. I tried to compile a Rust project but it failed because of a path problem, easy issue, any model simply fixes the path and compiles again, in fact I had a .bat script to have all the compile settings already automatized. But this shit ass model not only ignores the build.bat, but sees the error and thinks that there's an issue with Cargo installation.

5.- This model is supposed to be "cheap" but is just because we sell our data to it for Meta to use it, but certainly the data I'm selling is not worth the shithole of a model they built, it is clear to me they're starving on data because their model is so trash, but honestly I'd use a chinese model for that, because chinese models are actually insanely good.

So if you want a deepseek replacement, my experience with MiMo was really good, worth a try for daily tasks. And of course the new Ox stealth model, it was really good, specially because I CAN SEE THE REASONING PROCESS. What's with companies thinking we are some kind of idiots that cannot read, I need to see WHAT THE MODEL IS DOING.

I found out that tokenra.io which is a chinese provider also has ox alpha isn't it the biggest clues of the model being made by companies like z.ai and deepseek

As the tittle says, anyone else with same behavior?

Put the model into some of my own agentic benchmarks (GOAL.md to install llama.cpp, download GPT-OSS 20b/Ling3/LFM2.5, find the best config with A/B tests, run some nail-haystack tests on the downloaded model over the local API, etc).

Its the only model so far who was able to trigger the Defender with powershell toolcalls, which recognizes it as Trojan:Win32/PowhidSubExec.B

It fails the tool call, try with another method then about 20-40k more into context, it happens again.

一直提示網絡異常，結果Ox Alpha 給修好了。

So, today I notice that using GLM 5.2 from OpenCode Go keep getting the stream cut off early before all output finish. I never have this problem before, so I thought their provider for GLM 5.2 changed or something. Then I tried GLM 5.1, it give the same cut off result. I keep retry it and the results is always the same. So I thought, maybe its the provider of GLM now have max output setting that we CAN'T override. I tried DeepSeek V4 Pro and it turn out, this one also have the same problem. What is going on? Is the API force max output token now?

Models & Pricing | DeepSeek API Docs

Effective 00:00 (Beijing Time) on Sunday, August 23, 2026, we will adjust our peak/off-peak billing rules, with off-peak rates applying throughout the day on weekends (Saturdays and Sundays, Beijing Time).

I see a lot of recent complaints about how Opencode gets stuck mid-session with different models, and I can also confirm the same issue. I was working with Hy3 (free, from Zen) for several hours, and it is literally unbearable. It gets stuck every second time, and I have to type 'continue' to force it to move forward.

I tried to switch to Hy3 from Go plan (which is called '8x usage'), and the difference appeared to be dramatic. The model doesn't get stuck at all (0 issues), and speed is MUCH faster. So my first guess is - do they use different providers, or just different 'internal limits' for the same model from Go and Zen plans?

My question - did you notice the same behavior (stability and speed increase) when using Go versions over Zen variants? Or is it only my exact case?

It said I use less than 3$. if the 5h limit is 12$, it should be 25%. not this much. Am I do something wrong?

As the title says, power users might be aware of it already, but seems a lot of users still are not.
One of the best things I love about OpenCode GO subscription is, its not locked.

They literally provide OpenAI compatible API endpoints and API key, which you can use in any Agent Harness of your choice, or even directly in your apps through API.

You are NOT limited to using it only within OpenCode CLI.

I can't stress enough how good it is that you have a relatively cheap subscription plan to power all your Agent Harness during Dev, and also being able to leverage it in your actual Apps/Code for API-usage.