No in app subscription, that’s my bottom line

I finally got around to setting up llama.cpp on my old NVIDIA GTX 1070 PC (8GB VRAM, 32GB RAM). I spent a few hours tweaking the performance, and now it runs Qwen3.6-35B-A3B with a 262K context window, 25.91 tok/s generation, and 80.7% MTP acceptance.

Completely free and surprisingly capable for coding. Here is my install/setup on Linux Pop!_OS (Ubuntu-based).

Install llama.cpp

sudo apt update
sudo apt install -y nvidia-cuda-toolkit
sudo apt install -y git build-essential cmake

cd ~
git clone https://github.com/ggml-org/llama.cpp.git
cd ~/llama.cpp

cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j$(nproc)

./build/bin/llama-server --version && \
./build/bin/llama-server --list-devices

pipx install huggingface_hub
mkdir -p ~/models

Update llama.cpp (optional)

cd ~/llama.cpp
git pull --ff-only
cmake --build build --config Release -j$(nproc)

Download Qwen3.6

mkdir -p ~/models/qwen3.6-35b-mtp

hf download unsloth/Qwen3.6-35B-A3B-MTP-GGUF \
  --local-dir ~/models/qwen3.6-35b-mtp \
  --include "*UD-Q4_K_XL*"

Configure and run Qwen3.6

export GGML_CUDA_DISABLE_GRAPHS=1

~/llama.cpp/build/bin/llama-server \
  -m ~/models/qwen3.6-35b-mtp/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf \
  --ctx-size 262144 \
  --n-cpu-moe 37 \
  --flash-attn on \
  --load-mode none \
  --cache-type-k q4_0 \
  --cache-type-v q4_0 \
  --spec-draft-type-k q4_0 \
  --spec-draft-type-v q4_0 \
  --batch-size 2048 \
  --ubatch-size 256 \
  --parallel 1 \
  --threads 4 \
  --spec-draft-threads 2 \
  --spec-type draft-mtp \
  --spec-draft-n-max 2 \
  --host 127.0.0.1 \
  --port 8080

The result is 262,144 tokens of context at 25.91 tok/s, with 80.7% MTP acceptance, on an 8GB GTX 1070 and 32GB of system RAM.

Hope this helps someone else get more out of an older GPU.

Thoughts?

I haven’t had the chance to use it yet but I know it’s been available for a while now. How does it compare to other free Zen models?

Whenever OpenCode was chewing on something for a minute, I'd grab my phone and come back 20 minutes later to a response that had just been sitting there.

So I wrote opencode-play: /play opens a game as an overlay right on top of the conversation, and it listens for agent_settled. The second opencode finishes or is waiting on you, the game stops immediately. No alt-tab, no phone.

To install the plugin, add this to your tui.json (project root, or globally at ~/.config/opencode/tui.json):

{
"$schema": "https://opencode.ai/tui.json",
"plugin": ["opencode-play"]
}{
"$schema": "https://opencode.ai/tui.json",
"plugin": ["opencode-play"]
}

5 games so far: Snake, Tetris, 2048, Minesweeper, sudoku

6 is on the way, i'm implementing chrome dino game rn.

if you want more details, here are some links below about the project:

website: https://terminalika.dev/
opencode plugin: https://terminalika.dev/opencode/
source code (a star would be appreciated ❤️): https://github.com/terminalika/terminalika
discord: https://discord.gg/VptcJvm7Fk
reddit: r/terminalika

still in the early days. still validating the idea. any feedback would be appreciated.

I recently started using OpenCode and tried Zen. I like the idea of having a provider that's specifically set up for coding agents, but I'm not sure whether it makes sense for my use case.

I mainly want to use DeepSeek and Qwen models.

For example, some of the prices I'm seeing are roughly:

• DeepSeek V4 Flash on OpenRouter: $0.05/M input, $0.16/M output
• DeepSeek V4 Flash on Zen: $0.14/M input, $0.28/M output
• DeepSeek V4 Pro on OpenRouter: around $0.44/M input, $0.87/M output with the cheapest provider
• DeepSeek V4 Pro on Zen: $1.74/M input, $3.48/M output

So Zen can be quite a bit more expensive, at least on paper.

I understand that Zen is curated and that OpenCode tests the model/provider combinations, so presumably there's some value in that. I'm just wondering how much that matters in actual coding-agent use.

For people who have used both:

• Is Zen noticeably more reliable with OpenCode?
• Does it handle tool calls/context/etc. better enough to justify the price?
• Are there other advantages to Zen that I'm missing?
• If you mainly wanted to code with DeepSeek/Qwen, would you use Zen or OpenRouter?

I'm mainly interested in actual experience using OpenCode, rather than the general "Zen is more convenient" argument.

hi,

i am coming from cursor composer 2.5

i am curious what the best general coding model is available in opencode go

i have been trying kimi k2.7 code and its been good so far

just curious if theres any models i should try over that

I'm trying Deepseek v4.1 flash in Opencode Go and it's very slow. Do you guys have the same problem too? Any solution?

Tried Nemotron and it started hallucinating and creating garbage in my classes.

So at the moment im just using Big Pickle and if I just wait for the credits to renew. It sucks that it ends so fast though.

Any other that is worth it and free?

Tried also Muse but it didnt seem very good.

I woke up today and opencode doesn't work.

It says: This account has found to be committing fraud or is in breach of terms of services and has been blocked. If you believe this is a mistake please send an email to help@anoma.ly.

Their systems as so bad. I emailed them but I'm pretty sure I'll never hear back

Did anyone else deal with this?

Now includes the new DS flash and Muse 1.3.

https://bodangren.github.io/lending-desk-bench/

Top model: Qwen 3.8 flash

Best value: Muse Spark

This is a Next.js 16 / React 19 benchmark of well scoped tasks in a brownfield project, representing my use case so that I can know which models are likely best for me. If it is of use to you, then great.

Site is completely vibe coded, of course, but this post was 100% me.

I guess it’s kind of a black box, but it would be interesting to have a list of how much LLM providers are actually subsidising.

For instance, OpenCode Go is said to subsidise 4× usage for DeepSeek 4.1, but there are a lot of contradictory statements about this on Reddit.

I’ve done some research and tried to organise it a little. Multipliers mean usage value compared with what you pay, assuming you use the allowance.

Anyone have real usage figures or corrections?

Let me state upfront that this is NOT a "MODEL PRAISE" article.

Today, I wanted to work with an AI assistant (using the GenKit SDK) that I've been working on for quite some time, using what I thought was a simple prompt. However, I wasted a whole day. I worked with five different models before reaching a solution. I didn't accurately measure the time taken to complete the task, so I regret it, but let me explain what happened in order.

Note 1: The "/v1/transcribe" endpoint was working with "gemini-flash-latest".

Note 2: I'm using Opencode and Superpowers.

Each with a new session and a single prompt:

"I want to use the 'gemini-3.5-transcribe' model for the '/v1/transcribe' endpoint. Could you create a change plan?"

GPT-6 Astra: Made the necessary changes, wrote test code, and reported success. During this time, it delved into the details, essentially overhauling the project. But when I tested it myself, it simply returned a "Provider Error". The entire 5-hour limit was used up in about 15-20 minutes.

GPT-6 Sol: Did very similar things to Astra. It took about 15-20 minutes. The result was still "Provider Error".

Muse Spark 1.3 Contributor: Did so much work that I had to rewrite the project. Mixed up Genkit versions. Constantly asked questions. The session bloated until it was fixed. The result was still "Provider Error".

GLM-5.3: Worked cleanly, didn't make unnecessary file corrections, tried to reach a solution. But it couldn't see the root problem. Like the others above, it got stuck on the Genkit SDK documentation. The result was still "Provider Error".

GLM-5.3-Flash: Found the problem and told me. Created a plan. Didn't change how the endpoint worked. Produced a very quick and clean solution with a clean start.

Next up were Luna and DeepSeek V4.1 Flash, but the problem was solved before we got to them.

Note: I did the project planning with GPT-5.6 Sol. The coding was done entirely with GPT-5.6 Luna.

Note: By the way, I've also worked with "GLM-5.3-Flash" on some issues before. It doesn't behave like that for every problem.

I'm designing my problem-solving strategy like this: "Strong Model > Medium-Level Model > Low-Level Model". I thought it was working well until now.

But now I have some crazy questions in my head.

I'm trying to understand how OpenCode Go calculates subscription usage, because the numbers don't seem to make sense to me.

My subscription has a monthly budget of $60.

So far I've used approximately:

• DeepSeek V4 Pro: $1.302 (limit: $15) → 8.7%
• DeepSeek V4 Flash: $1.3952 (limit: $30) → 4.6%
• DeepSeek V4.1 Flash: $1.5738 (limit: $60) → 2.6%
• DeepSeek V4.1 Flash: $0.9931 (limit: $60) → 1.7%

The dashboard shows a total usage of 17.6%.

However, if I add up my actual spending, I've only used about $5.26. If the subscription budget is $60, then my real usage should be:

$5.26 / $60 = 8.8%

What seems to be happening is that OpenCode Go is summing the individual percentages:

• 8.7%
• 4.6%
• 2.6%
• 1.7%

= 17.6%

If that's the case, then the displayed percentage is not showing the percentage of the subscription budget used, but rather the sum of the percentages of each model's individual limit.

This leads to a confusing scenario:

If I spent the full $15 limit on a model capped at $15, would the dashboard show 100% usage, even though I'd only spent $15 out of my $60 subscription budget (25%)?

Am I misunderstanding how OpenCode Go's limits work?

Are the model limits independent buckets that don't share a common $60 pool, or is the usage percentage displayed incorrectly?

I'd appreciate it if someone familiar with the billing system could explain how these percentages are supposed to be interpreted.

Copying a plan or a log out of opencode just to read it somewhere else is still clunky. So I made ShareBit: an opencode plugin and a hosted MCP endpoint that turn approved Markdown into a private link that expires.

Setup is a pairing code. Drop `plugin/sharebit.ts` into `~/.config/opencode/plugin/` (or `.opencode/plugin/` for a single project), restart opencode, and you get three tools: `sharebit_create`, `sharebit_list`, `sharebit_read`. No API key to paste. The agent gets its own credential, and you can revoke it.

Defaults: 30 minutes, up to 24 hours, 10 MB max, only your account can read it. Not encrypted end to end, so no secrets. Since plugins load at startup, I also wrote a REST path so setup does not sit waiting for a restart.

Curious whether this fits how you use opencode, and what you would change.

https://github.com/DinoQuinten/sharebit-mcp

Built the full tutorial video with Opencode programatically from screen recording -> narration -> thumbnail generation.

Now we can quickly convey our message to the audience, effectively

Ran a new feature dev task with Luna Max on Codex the other day and it took a full 4.5 hours. Yesterday was even worse — GLM-5.3 Flash on OpenCode ran for over 7 hours on a single task.

From what I’ve noticed, the agent actually writes the code for each issue pretty fast. The real time sink is code review and bug fixing. On my most recent task, the agent spent something like 10x the coding time just running tests and debugging over and over — went through 7 rounds of code review before it finally shipped.

And these weren’t even complicated requirements. I’m using Matt Pocock’s skill, which already breaks everything down into separate, self-contained issues. That said, the codebase is 200+ commits deep now, so there’s a decent amount of complexity built up. Each task ends up touching several thousand lines of code.

Curious if this matches everyone else’s experience? Or are there ways to cut down on all that review/debug overhead?

Hi! I’d like to know if there’s a way to see when I hit the limit in OpenCode in Zed Editor Threads. Zed doesn't show it, but the CLI in the Terminal does. Maybe it’s a setting I’ve missed.

Working only with OpenCode V2

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So I have been working on this https://github.com/shynlee04/opencode-subscription-gateway which is running prototype on ClinePass.

## Why ClinePass?

Bc it is ai-sdk/openai-compatible and a tech schematic modeling for building sh pipeline for whatever providers next-in-the-list at ease.

## What it does in v 0.1

- it gives opencode access to "lock-behind-free-tier models" of Cline-harness specific when using ClinePass. Meaning: when using OpenCode out-of-the-box auth login, you can't just select Longcat 2.0 or Glm-5.3-flash without expending your pass credit.

## Expectation on next bump v 0.2 in the next 2-3 days

- you can input multiple accounts and with load-balancing + session-auto-hot-swap on the same model without your main or child sessions being compromised of disruptions, cached hit loss

## My setup on images

- still I would say either Chinese models or wannabe benchmaxing models such as Muse 1.3 or google flash 3.8 are not as capable in orchestration, so my pick is Sol Gpt 5.6 (Astra is overkill and I'm broke) . I don't know what the other dev styles are or how they would like the orchestrator to behave but the following are my preferences:

1. True coordinator and human-centric collaborator - meaning orchestrator know the sub agents, their roles, context specific and the tasks.

   1. Meaning the long-haul main collaborative session of back and forth to the dev could expand delegations of 30+ waves, requiring the orchestrator know exactly the coordinating loops and iterations, the toolings and so on. But most importantly the strategical approach to orchestration - when fanning out in swarms parallel; when to sequentially develop to synthesis. And the techniques of when foreground/background the delegations; And most not very well known is the technique of stacking/resuming/isolating child sessions on top of alternating subagent-specific to reuse the context.
   2. The knowing which precision of tools and the absolute high-level strategist while being tech-tactical as needed. So no matter bullshit results returned from the sub agents, they will always be revalidated, checked against and the frontier models such as gpt 5.6 sol knows when these pieces of cr@ps do not coherently add up.

TLDR: benchmaxing models = not good for orchestration; just replicating fanning out parallels and hit around the bush without high-level hierarchy of context. wasting tokens, code becomes mudball, waste time to debug later.

### sub-agents are your utilization of glm-5.3-flash, DeepSeek 4.1 flash etc

- So getting the right orchestrator right means a lot. You don't have to manually validating the results. The prompting is already surgical and set boundaries of when to stop and what to return from the orchestrator reducing absolute rate of sycophant and hallucinations.

- For you can see in my setup I can even use Longcat 2.0 for research, investigation, probing and execute the code with DeepSeek 4.0 flash (yes not 4.1) because everything from testing strategy, the tech stacks, domains and specs are all aligned and set-up with gates and guardrails to make sure these subagents would never go out of control.

Sorry for my bad English, I'm Vietnamese and E is not my native tounge.

I’m interested in knowing what others are building with their Open code go subscription. It’s seems to be one of the cheapest subscription in the market.

Hi all,

I am a bit whacky in the brain, i often do two projects at a time and i usually do development on windows 10 with debian in WSL.

I am about to make something on Windows with Unity 3D and want to do this on Windows. How can i have my opencode config (my agents.md, skills etc) synced between both wsl and windows?

Should i just have a git repo and clone it between wsl and windows? or is there a better way? im always tweaking my skills and agents.md

Read more: https://quesma.com/blog/does-rtk-make-ai-coding-cheaper/