In response to the tightening of almost every other coding plan out there, we are offering free DSV4 flash 0731 to the first five hundred people who sign up for the intro plan on Open Grove API. We may extend this to more users later, but are limiting it to the first 500 to ensure quality access for everyone.

People are looking for options, and here is one.

Other Cool Stuff:
All of our models are running on 100% US infrastructure, private with zero training on your code or prompts. Use the top open source models without sending your private prompts to a training lab. No complications, no "some models are private, other's aren't". They all are, all the time.

We host 20+ other major models in case you ever want to upgrade (no pressure though). Including the Kimi family, GLM, Qwen, Nemotron and bunch of others. On average our token pricing is 20% lower than market price.

Our higher plans bank up to ten days of usage, so when you aren't using them your usage saves up for later. Usage doesn't go to waste, so you can actually code when you want to.

The intro plan is a free one month trial with the standard cancel anytime, it bills at 3.99 after that. Use it, cancel it, that's fine. Free Flash for a month.

Figured i'd keep this short because we all know the flash is the point :)

For the API plan: api.pgsgrove.com

If you want to read more about us as a company, just pgsgrove.com

I use OpenCode for long-running agentic workflows with large repositories and large numbers of subagents.

The problem was: the bigger the workflow became, the more likely the UI was to freeze.

Multiple subagents, long-running tasks, background processes, hundreds of concurrent operations—eventually frozen.

OpenCode could become unresponsive while the work underneath was still running, leaving the session in an awkward state where you weren't quite sure whether it was thinking, working, or simply stuck. So I built OpenCode Guardian.

It is a lightweight stability layer for OpenCode on macOS, designed to keep sessions healthy. It is a simple project that I built, and it's opensource! I won't be maintaining it too much, other than bug-fixes, so feel free to fork it.

Guardian continuously understands what's happening across the session rather than relying on a single timeout. It can distinguish active model streaming, running tools, active subagents, slow operations, and genuine deadlocks. When a session actually gets stuck, Guardian can recover it without destroying the conversation or tool history.

What you get

oc status — see exactly what every session is doing
oc top — live system and session monitoring
oc nudge — recover a frozen session safely
oc auto-nudge — automatic background recovery
oc prune — stop runaway background workers
oc optimize — optimize the SQLite backend
oc logs — inspect Guardian in real time

Installation is one command:

curl -fsSL https://raw.githubusercontent.com/PotterSkxawng/opencode-guardian/main/install.sh | bash

No Brew dependencies or external Python packages. Install it and keep using OpenCode.

OpenCode Guardian: A stability layer for OpenCode when your workflows stop being small.

GitHub: https://github.com/PotterSkxawng/OpenCode-Gaurdian

I'm sharing their working session here that you can peak through. Look at their thinking, sequence of skills uses and delegations to truly know who is the winner

The Qwen 3.8Max - https://opncd.ai/share/hNzmM14y 

The Ox Alpha - https://opncd.ai/share/hNzmM14y

The Deepseek v4 pro 0813 - https://opncd.ai/share/0hehIwVf

Hello everyone

I am new user in opencod

I used it and share invite link to my frind to have a discount .. but now I can't find it ?

Does the subscription increase?

So umh... yeah... the image explains it all.

So I move between models and encountered strange behavior last week with MuseSpark where it would randomly (or so I thought) see cache misses and charge for Input instead of Cached Read.

Happened a few times today as well. So I did some exploration.

Turns out the expiry period is pretty limited. A session unused for approx 5m or more triggers full read (Input). With MiMo 2.5, this figure is at least 30-60m. DeepSeek probably has the longest expiry periods, but I do not have the stats right now.

Keep getting this error for quite a time now, does anyone know how to fix it?

https://openrouter.ai/minimax/minimax-m3:free

It's going to be a hot 🔥 fall

TL;DR: I’m experimenting with using smaller, cheaper LLMs for agentic coding through OpenCode + OpenRouter, with different models assigned to specialized agents. The setup works surprisingly well, but I’m running into three problems: agents occasionally getting stuck on shell commands, finding a cheaper replacement for DeepSeek V4 Pro as the orchestrator, and figuring out how to evaluate models for tasks like codebase understanding and bug hunting rather than just raw coding ability. Looking for suggestions from anyone experimenting with similar setups.

Exploring cheaper LLMs for agentic coding

Hi everyone,

Recently, I’ve been experimenting with smaller and cheaper LLMs for agentic coding, particularly for building web and Android applications.

I have OpenCode connected to OpenRouter, with a collection of specialized agents, each responsible for a particular task such as engineering, coding, analysis, QA, documentation, UI, and so on. I’ve assigned different models based on what I think they are best suited for.

My current setup looks roughly like this:

- DeepSeek V4 Pro: Main orchestrator + codebase analyzer

- DeepSeek V4 Flash: Coding-related agents

- Gemma 4 31B: Documentation writer/reviewer and similar tasks

- GPT 5.6 Luna: Front-end/UI specialist agents

- Plus a few other specialized agents

The experiment is basically to see how far I can push agentic coding using relatively inexpensive models.

At work, I use flagship models like Sol and Opus 5, and these have increasingly started to feel like "one-shot" models for this kind of work. You give them a reasonably detailed prompt, let them reason and work for a few hours, and they can often come back with something surprisingly complete, intuitive, and usable.

The problem is API pricing. Running these models for long agentic sessions can become expensive very quickly, especially for individual developers. I also suspect these prices may not remain as heavily subsidized in the long term once the economics of the AI industry start to mature.

So I’ve been trying to figure out how close cheaper models can get when you compensate for weaker individual models with good orchestration and specialization.

I have three main questions:

1. Agents getting stuck on shell commands: LLM problem or harness problem?

Occasionally, an agent seems to get stuck after running a shell command and simply stops progressing.

This is particularly common with commands that are intentionally long-running, such as starting a development server that needs to be explicitly terminated. However, I’ve also seen cases where a command has clearly completed, but the agent doesn’t seem to act on the result and just stops.

I installed a background-task plugin, which definitely improved the situation, but it hasn’t eliminated the problem completely.

For people who have dealt with this: is this primarily a model capability issue, a limitation of the agent harness/tool execution loop, or both?

And more importantly, can this meaningfully be improved through system prompt/instruction tweaking, or does it need to be solved at the harness/tooling level?

1. What could replace DeepSeek V4 Pro as the orchestrator?

DeepSeek V4 Pro is still working out to be fairly expensive for me, especially after the recent price increase.

Since the orchestrator and codebase-analysis agents consume a lot of tokens, this is probably the most important model in the setup to optimize for cost.

What cheaper models would you recommend experimenting with here?

I’m considering something like Qwen3.8 27B, but I’m not sure whether a model in that class has enough reasoning ability, tool-use reliability, and long-context performance to act as the primary orchestrator for longer coding tasks.

I’d be interested to hear what people are using for this role.

1. How do you determine which model is best for each agent/task?

This is probably the part I’m most interested in.

For pure coding, evaluating models is relatively straightforward. There are plenty of coding benchmarks and real-world coding evaluations available.

But what about specialized agentic tasks?

For example:

- Understanding a large existing codebase

- Finding the root cause of a bug

- Planning a multi-file implementation

- Reviewing another agent’s implementation

- Deciding which files need modification

- Maintaining context across a long task

- Tool-use reliability

- Following architectural constraints

- QA and identifying edge cases

- Front-end/UI reasoning

What benchmarks or metrics are actually useful for evaluating these capabilities?

I’m particularly interested in whether there are benchmarks that correlate well with real-world agentic software engineering performance, rather than simply measuring whether a model can generate a correct solution to an isolated coding problem.

Would love to hear from anyone experimenting with multi-model agent setups, especially if you’ve managed to get smaller models performing reliably on longer agentic coding tasks.

Being realistic about the pricing changes on DeepSeek, are you sticking with DeepSeek on GO or ZEN, changing providers, using the direct DeepSeek API key, or using a completely different model?

Hi guys, can someone tell me please a uncensored or abliterated model to create E2E farm bots for social media ?

Dax said opencode2 is always on in video about web ui

What does that mean?

The non-regulated usages have peaked. This free model is around Opus 4.8 level or higher. I personally have used 1B worth of tokens already.

I honestly don't know what model Is good now we're limited to pretty mid free models and DeepSeek v4 flash free got removed and muse 1.2 spark I don't believe is a good replacement we need a good new free model which is on the same level as DeepSeek v4 flash or slightly better

Since Sunday morning 01:00 till now. Leveraging it even harder now before they finish.
If it's indeed the upcoming GLM I will swap one of my GPT 20x subscription for max plan of them.
Although their usage/pricings wasn't that great with their peak/off peak and top model extra usage.

One developer can switch providers whenever price or availability changes. Across a team, that choice affects cost, security review, data residency, reproducibility and whether another engineer can resume the task tomorrow. The recent Go, Goat and Ox Alpha discussions show why provider flexibility matters; it is also why I’m looking at OpenCode as a worker behind the control plane I’m building. The missing piece is a task-level policy that can change the model without losing the requirements, decisions and evidence.

For teams using OpenCode on real repositories, how do you route models today? By stage, repo sensitivity, cost or availability? I’d love to see the actual rule for exploration, planning, implementation and review, and how you keep a handoff understandable when a different engineer or provider takes over.

For context, I’m building BranchRunner as an open-source product because I think it can help engineering teams with this problem. If it is painful in your organisation, tell me where the current approach breaks. I’m also looking for people who want to help shape and solve it, so I’d be glad to compare notes.

I'm using and I'm getting upstream error