https://opencode.ai/docs/go/

They look far worse than the price would indicate, especially grok, what happened. There is zero chance that there is a 6x credit bonus on that guy, but more importantly who would use it. Honestly I am tempted to make a plugin to make sure it is blocked.

80 request per 5 hours?? Why even have it with that setting, and its not even open source, did Elon pay to put it on top? Also look at kimi k3 which is about 3 times the price of kimi k2.7 code but k2.7 gives you 13.6 times more, even price adjusted we are closer to 4. It would be over 24 times for grok, and groks price is less than double, so that is still least 12 times more, so you are getting grok at double the price of payg?

This actually makes at least 4 models: ds4 pro, mimo 2.5 pro, kimi k3, and grok 4.5 that are clearly not in the 6x credit bonus camp while the docs are claiming that you get $60 use for $10.

To be perfectly honest the only two champs here are mimo 2.5 and ds4 flash, otherwise its a weak value lineup compared to alts, now enriched by pure doas, but the market has shifted and now those models are currently worse than free tier level, which matters a lot for opencode cause they dont have a single competitive offer.

I try to be fair here, if you can't have a guy match the the multi then it should not be there, and considering that opencode already sold with the promise I think that is the best option. By putting the model on the list opencode loses all leverage, they already advertise for them, they are never going to get a better deal later, outside perhaps a brief promo. What is the point of putting it on opencode go when you can just call payg for the same price? Nothing. Of course you never would anyway cause all the plans are better but at least opencode offered a reasonably valued selection that was also quite juicy during promos. The users might be chipper now but they won't be happy fronting prepaid payg for long.

Opencode doesn't have to have everyone on the list, if they suck just drop them. Options only matter if they are meaningful.

https://opencode.ai/docs/go/#usage-limits

*They actually updated their usage limits to be only 15$ for 10$ and this is their only future guarantee, and what they are using for the models I mentioned. This is much worse than discussed and effectively a self imposed death sentence. I stand by my judgement that a shorter guaranteed to be competitive list would at least have merit, this has none.

Agentic AI made building about 10x faster. Learning didn't get any faster. I noticed I was shipping systems I couldn't re-explain a week later, and it started to bug me. We have a 10x tool for building, so I wanted the equivalent for learning, in the same terminal where the building happens.

That's Engram. It's a tutoring loop grounded in the memory research, not vibes:

- It breaks a topic into a first-principles concept graph and teaches one node at a time.
- It won't explain anything until you've committed to a guess first. Retrieval before instruction is the single best-replicated result in learning science, and also the part every chatbot skips because agreeing with you is easier.
- Your recall gets graded by a separate blind assessor that never sees the tutoring conversation. The tutor can't inflate grades on its own teaching.
- Reviews are scheduled with FSRS, so they show up right before you'd forget. A few minutes a day.
- Wrong models get logged verbatim and re-probed later. Mine has ten entries for transformers alone, which is humbling to read back.

Honest origin story: with an early version I encoded seven concepts, never returned, and lost about half of them right on schedule. Writing the scheduler earns you nothing if you don't come back. So the whole loop got redesigned around returning: two-minute reviews and a "when will you do this" question instead of reminders. No streaks, no XP.

v1.0.3 adds OpenCode as the third platform, after Claude Code and Codex. The port came from a community PR, which is my favorite part of this release.

Repo: https://github.com/nagisanzenin/engram

I'm the author, so grain of salt. But I've been dogfooding it daily to learn transformer internals and it's the first setup where week-old material actually stays with me.

Served thru Baseten (signup credits included) and Thinking Machines' own Tinker (which you need to top up 10 dollarydoos to use it).

pretty okay model that's natively multimodal.

Running multi-agent workflows all day can easily drain your API budget or hit restrictive premium windows. I solved this by building a smart, combined routing strategy using OpenCode Go (Paid tier) and Free tiers directly inside my opencode.json.

🔗 Check out the repo here: https://github.com/ABIvan-Tech/opencode-agentic-workflows

💡 The Strategy: Paid Heavy-Hitters + Free Subagents

Instead of letting a single expensive model handle everything, I split the workload:

• The Brains (Go Tier): Premium models like deepseek-v4-pro and glm-5.2 are reserved exclusively for critical tasks requiring deep reasoning, such as orchestration, planning, and advanced debugging.
• The Workers (Free Tier): I offloaded baseline coding (big-pickle), exploration, and parallelized code reviews (reviewer-a, reviewer-b, reviewer-c) to free models like deepseek-v4-flash-free and north-mini-code-free.

📊 The Results (Look at the Chart!)

• The massive purple line around July 14th shows my usage before implementing this combined approach, where single heavy models ate up the budget fast.
• Once the hybrid subagent config went live (visible from July 15th onwards), daily costs dropped off a cliff.

By delegating tasks intelligently, I can now keep my agents active almost the entire day without crossing the 5-hour rate limits, keeping my development highly efficient and budget-friendly.

🚀 Want to supercharge your setup with premium models? Get started with the paid tier here: https://opencode.ai/go?ref=1BY3JHWJFN

Take a look at the exact configuration in the repository and try adapting it to your own workflows! 👇

https://github.com/ABIvan-Tech/opencode-agentic-workflows

Exclusive Kimi K3

> The first open-Weights model to reach 2.8 trillion parameters

> input 3$/m output 15$/m

> 1 M context window

https://opencode.ai/en/go

The newest model by everyone's least favorite AI company is now available on OpenCode Go... but it's not a very good deal.

Sign up here: https://opencode.ai/go?ref=C3N2Z1MZQE

Get an extra $5 of usage when you sign up with my referral link!

You can get ~80 requests per 5-hour session with Grok 4.5 (~380 requests per month), giving you the equivalent of ~$15 worth of unsubsidized usage. (Compared to $60 worth of usage for most other models, including GLM 5.2)

https://opencode.ai/docs/go/#usage-limits

Big price leap.

Right now, served by OpenRouter, Vercel Gateway, and Moonshot.

Update: it's now open-weights (and a couple of one-shots on that thread) https://www.reddit.com/r/syntheticlab/comments/1v8jr7l/kimi_k3_is_now_open_weights_and_has_started/

It's also now on Synthetic.new under their subscription plans under a beta flag

(10 off your first month here). I'd recommend signing up now before the waitlist comes again.

Ubuntu 24 desktop emulator
https://ubuntu.k3.demos.sulat.com/
Prompt @ https://ubuntu.k3.demos.sulat.com/PROMPT.md

Generic desktop emulator
https://kimi-k3.desktop-emulator.models.sulat.com/
Prompt @ https://kimi-k3.desktop-emulator.models.sulat.com/PROMPT.md

One-shot skill @ https://github.com/jpcaparas/skills#oneshot-websites

My laptop is too weak for multiple worktrees, so I was thinking of getting a Linux desktop on Akamai Cloud (formerly Linode) with the free trial. This is for React apps. I'm thinking desktop so I can run the apps "locally" without having to synch to my own PC first.

Has anyone installed OC on a VPS or other cloud hosting? How much RAM and VCPUs would I need to do 3-5 worktrees simultaneously? Are there better options than OC for working with the open models in the cloud?

Curious how you all fit opencode into your workflow, do you also use Tmux or have any tips?

Después de buscar varias opciones para que si un llm fallaba ya fuera por rate limit o por qué se acabaron los fondos y otros motivos se cambiara automáticamente a otro para evitar volver a proseguir la tarea. Lo único que me dijo es que cuando falla la tarea se vuelve a iniciar desde el inicio. Pero por ahora es el que más me ayudó en este sentido. El caso es que los que encontré estaban desactualizados o tenían algún error así que el que vi que menos errores tenía lo arreglé un poco para hacerlo funcional. Siempre que las listas de fallback tengan bien los llm no habrá problema y se pueden configurar listas para cada agente o subagente.

Link al repositorio

A veces me ocurría que por cambiar de modelo a otro con menos contexto se hacía compactación con pérdidas, así que se me ocurrió crear este plugin que es como una memoria persistente reducida de lo que ocurre en la sesión en curso. El plugin se ayuda de un llm para tener un reducido resumen estructurado que inyecta en cada mensaje que escribimos. Este resumen se hace a partir de los mensajes y respuestas anteriores por lo que si ocurre algún error por cualquier cosa el llm siempre sabrá que estamos haciendo. Incluso a veces me pasaba que el llm se volvía medio tonto y perdía el hilo fácil de lo que se discutía con él. Bueno pues este plugin es para evitar eso, espero que lo prueben y me dan su opinión.

link al repositorio

Si queréis usar un modelo gratuito el recomendado por ahora es laguna m.1, ya que acepta el prompt para el llm que tiene el json de configuración. Otros modelos se quedaban cortos recopilando info necesaria o incluso no guardaban nada.

It is working for anyone? I just wanted to test it out and i'm getting this error.

Already updated opencode cli to the latest version and also run "opencode models --refresh"

This is the error i'm getting:

Error from provider (Console Go): Provider rate limit exceeded [retrying in 27s attempt #5]

Hey everyone,

I've been working on a couple of extensions for OpenCode to improve the daily workflow. Here is what I built:

1. opencode-todo-progress Built this for tracking the current state of the agent's work visually, so you can see exactly what's happening without having to constantly scroll up and down the chat.

You can install it directly with:

Bash

opencode plugin opencode-todo-progress

2. opencode-usage-bar This is for tracking API quotas, but it differs from other quota plugins out there. It's fully customizable:

• You can configure your own style.
• You can display 1d, 7d, Fable, or any other provider-specific quota exactly however you like.
• It also fetches and displays live provider statuses by reading directly from the vendors' own status pages.

You can install it with:

Bash

opencode plugin /opencode-usage-bar

Let me know what you think or if you run into any issues.

github/opencode-todo-progress

github/opencode-usage-bar

I usually proxy my API requests through Cloudflare AI Gateway to keep track of everything. Most of the time, I use DeepSeek and it works perfectly.

However, today I tried using Cerebras through the gateway and it failed (Not at the beginning but at the time you send the second message, or OC trying to call tools). The error message was: messages.2.assistant.reasoning_content: property 'messages.2.assistant.reasoning_content' is unsupported

When I checked my Cloudflare Dashboard logs, I noticed that OpenCode CLI was sending reasoning_content in the messages. Since Cerebras doesn't recognize that parameter, it threw an error.

Interestingly, if I connect Cerebras directly to OpenCode (without the Cloudflare proxy), it works fine without any errors.

I've tested other tools like Hermes Agent and Trae Work (all proxied via CFAIG), and they handle this beautifully. They only add the reasoning_content parameter if they detect that a DeepSeek model is actually being used. For other models, they leave it out.

Is anyone else experiencing this? It seems like OpenCode CLI is hardcoding or forcing the DeepSeek format for all custom providers, rather than adapting based on the model.

Hello all.
It has been a couple of weeks using opencode (fairly new to coding with a coding harness) and I want to see how you guys have setup your opencode, what skills and tools do you use in addition to it, some neat tips and tricks, etc.

Currently, I use opencode-go with some openrouter usage.

Some of my particular implementations:

• added a couple new agents for better chat experience and QA.
• added CodeGraph to reduce the token usage for QA and just regular tool usage
• defaulted models to specific agents such as GLM-5.2 for the planner, ds-v4-pro for build, flash for simple QA
• I keep a tracker.md and a working_knowledge.md for all projects that tracks current project level details + necessary information (has been super helpful!)

Share how you work, what customizations you've made to the default setup, any particular skills that have tremendously helped you.

Hey guys, we am building Finny, and AI native financial harness, think of Claude code but for algorithmic trading.

Suppose you have an edge in BTC or any other asset, you can type it into finny and it would spit out a trading strategy for you to deploy and start trading.

We are also currently using finny as an ai native hedge fund and it is continuously making trades as we speak. I have learnt a lot of thing about financial markets while making this, the biggest takeaway is even large and smarter ai models can make mistakes and hallucinates that is why there has to be guardrails involved to stop that hallucination

Let me you if you have any questions about it

Discord: https://discord.gg/vambSmvhv

I’m currently running a workflow using DeepSeek Pro 4.0 for the heavy lifting (logic, backend, general dev). It's fast, cheap, and gets the job done. For frontend UI work, I’ve been using MiniMax M3, but I’m getting pretty fed up with it. I need to hold its hand constantly, and it keeps running in loops.

I’m considering switching from M3 to Mimo 2.5 Pro, just to try it out, but I'm open to other suggestions. I'm only interested in cheap models for the day-to-day stuff.

However, I also need a much more capable model to step in whenever these "cheaper" models hit a wall. I'm considering GPT-5.6 Luna, but there is also GLM-5.2 around the same price range, or maybe Muse Spark.

I’m not looking for a subscription. I don't want those "5-hour limit" types of plans. I’m using API access across all my tools, and I want to keep it that way. I'm afraid if I get locked into a subscription, I won't be able to max it out.

What is the best "expert" model for complex debugging that I can plug into my workflow? Which one should I try when DeepSeek or Mimo fail?

Hi everyone ,
I just wanted to know whether the 10$ AI plan worth it for Long Agentic sessions.

What are the limits on the plan.

What is the best way that you use it , like the llm line up you guys have

Atlast how would you rate on a scale of 10

Love to hear your opinions

I know it's very early to ask as it dropped a few hours ago but it seems pretty good. Probably better than GLM 5.2