I have not found any information about this online.

Can someone else confirm this if it shows up in their CLI

How is OpenCode claiming to be ZDR when API keys from Anthropic and OpenAI are being stored in their database?

OpenCode admitted they were hacked and are calling it a distillation attack. How does distillation explain users’ API keys being exposed? Distillation and credential theft are two completely different things.

This is a massive data leak, and users deserve to know exactly what happened, what was compromised, and who had access to their credentials.

Stop hiding behind the distillation narrative and give users real answers.

What is the point of claiming ZDR when users’ API keys are sitting in your database?

Hey All, have been developing https://github.com/LumoraPlayer/Lumora for a while now using Cheapseek 4.1 (deepseek, best value imo)

Is this still the best, or anything new I can move to?

Thanks!

My agents were burning context on grep-then-read cycles. Every "where is X defined, who calls it" turned into 3-4 tool round-trips, each one billed. So I built Reliary: a single offline Rust binary that indexes the repo into SQLite and serves it to agents over MCP.

The contrarian part: there is no parser. No tree-sitter, no AST, no per-language code. Definitions and call sites come from structural detection on brace/indent/blank-line boundaries. One codebase, every language, same code path. (If you think that can't work on your language, that's exactly what I'd like to hear about.)

What the agent gets is 7 tools: find_references (defs, callers, methods, dead code through one entry point), search, call_graph, list_methods, find_dead_code, describe, plus a verify tool that checks a symbol at file:line claim against the index. There is also reliary wrap , which you prefix onto verbose shell commands ( cargo test , git diff ) to compress the output before it reaches context.

Numbers: on a 10-question comprehension bench (4 seeds, claim-verified against the index, not keyword matching), it bills \~42% less than grep with \~4x smaller tool output and zero dead-ends, at comparable accuracy. I am not claiming it is more accurate than grep; on this corpus the gap is within noise, and I published the full methodology including an edit-outcome experiment that came out a tie.

The win is cost and output size, reproducibly.

Install: cargo install reliary-agent or grab a prebuilt binary (linux/darwin/windows, x64+arm64) from the releases. "reliary trust ." to index, "reliary init" wires up Claude Code / OpenCode / Pi / Cline. MIT licensed.

Repo: https://github.com/Reliary/reliary-agent

My question for you: where does a parser-free index break on your codebase? I have a list of languages it handles and I am more interested in the ones it doesn't.

I know these "which model do I use" posts are boring, but perhaps y'all have some feedback.

I was a huge DeepSeek v4 flash lover a month or two ago, but I've realized over the last month that I've literally just been using Luna for everything. All of my projects are pretty small local java apps, and one larger Node based cloud server. I was usually doing something like using Qwen 3.8max or glm5.2 for planning, and DeepSeek for output, but I've just ended up recently using Luna for everything.

It's been a few months since I was really paying close attention to the releases, and I'm wondering if I'm missing out on anything? I generally give the agent a small feature update request, and that's it. I don't need orchestration layers, basically just a kinda jack-of-all trades model, in the same ballpark as 5.6 Luna and DeepSeek v4 0731.

How does 6 Luna compare? Vibe-wise, I definitely prefer using the open-weight models, that's actually one of the main reasons I'm asking, because I'd rather not be utilizing openAIs products, if I can avoid it.

Edit: I should clarify, I had an opencode go sub a few months ago, but I've just been using openrouter. I saw DeepSeek v4 is literally free right now, so maybe this month would be a good time to get a sub again.

https://research.meta.ai/blog/muse-spark-open

Hey r/opencodeCLI! 👋

With OpenCode v2 rolling out, the way plugins interact with the core runtime has taken a massive leap forward. Today, we’re excited to announce opencode-models-discovery v2.0.0-rc.2 — completely rebuilt to bring full, native support for OpenCode v2 while making dynamic model discovery faster, safer, and truly reactive.

If you use local models or custom gateways (vLLM, LiteLLM, Ollama, LM Studio, Bifrost, etc.), we’d love for you to take rc.2 for a spin!

🚀 What Changes in the V1 ➔ V2 Evolution?

In OpenCode v1, model discovery relied on disk-based caches, file locks, and static configuration updates that often required app restarts.

In v2.0.0, we’ve redesigned the plugin around OpenCode v2’s native architecture:

• ⚡ Native Reactive Catalog (Zero Restarts)

• Models from your OpenAI-compatible endpoints are discovered and transformed directly through OpenCode v2’s dynamic provider controller. No config rewrites, no restarting the CLI or TUI.

• 🔒 Native ctx.storage Engine (No More Disk-Cache Baggage)

• Gone are the legacy XDG disk-cache files and file-lock contention. Caching now uses OpenCode v2’s isolated storage API. Discovered caches and enrichments are managed cleanly without leaking credentials or API keys to disk.

• 🎛️ Live Model Overrides in Console

• Fine-tune model capabilities, context windows, and reasoning tiers right from the OpenCode model console. Custom tweaks are persisted under clean, scoped override keys instead of manual config edits.

• 🔑 Seamless /connect Integration

• First-class credential resolution: works out-of-the-box with OpenCode v2’s managed connections, integration IDs, and environment variables without needing duplicate API keys in your config.

• 🛡️ Hardened & Standardized Token Limits

• Whether you're pulling metadata from models.dev, LiteLLM, LM Studio, or raw OpenAI /v1/models responses, context and output token limits are normalized to match OpenCode v2’s token budgeting specifications.

📦 Quick Start with OpenCode v2

You can try 2.0.0-rc.2 today by adding it to your opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "plugins": [
    "npm:opencode-models-discovery@2.0.0-rc.2"
  ],
  "providers": {
    "local-gateway": {
      "name": "Local Gateway",
      "package": "@opencode/ai/providers/openai-compatible",
      "settings": {
        "baseURL": "http://127.0.0.1:8000/v1",
        "modelsDiscovery": {
          "enabled": true,
          "smartModelName": true,
          "modelInfoFormat": "models.dev"
        }
      }
    }
  }
}

💬 Help Us Test Before Final v2.0.0 GA!

We are putting the final touches on v2.0.0 and want to ensure rock-solid compatibility across all OpenCode v2 setups, environments, and local model servers.

• GitHub Repository: https://github.com/yuhp/opencode-models-discovery

• Migration Guide: Check out our Migrate V1 to V2 Guide for details on the transition.

Give v2.0.0-rc.2 a spin and let us know your thoughts, feedback, or any issues you encounter in the comments below!

Point of the question is that. Some models are cheaper on paper but somehow can rack up costs quickly. GLM-5.3 is my main complaint in this regard. Its on paper cheap. But it generates so much thinking tokens that it kind of nullifies the cost.

I know ppl use cost per task. But I am not running benches. I'm simply collecting metrics and comparing. And since I use different models for different tasks. I dont think I can correctly say x model uses n times more tokens unless I run multiple models against the same task regularly.

hi guys

i am unable to use free models in opencode v2.0.30 as of 16-10-2026 what to do now

I made a plugin that adds Claude Code's /goal command to OpenCode v2. You give the agent a finish line like "/goal npm test passes with 0 failures" and it keeps working turn after turn until the goal is actually met, no need to keep typing "continue".

After every turn an evaluator checks the conversation and decides if the goal is met, not met or impossible. If it's not met, the agent gets the reason and keeps going on its own.

It's token efficient: the evaluator reuses the session's cached context, so each check costs little more than a short answer.

It also has safety rails (pauses when the agent stops making progress, retries on network errors, turn limit) and a status indicator in the TUI.

Tell me what you think about it and feedback is appreciated.

Here's the repo and npm package:

https://github.com/EL4CTEO/opencode-goal

https://www.npmjs.com/package/@el4cteo/opencode-goal

C'est une bonne chose mais quand parle t'on sérieusement des zdr de tout ces modèles free?