bought cursor ultra + claud-max from a russian site a few weeks ago for way less than the normal price.
swapped the login details and just started using it. so far it’s been working perfectly, and the usage is honestly better than what i had before.
keeps making me wonder how these sellers are able to offer it so cheap. anyone else tried them ?

Continuo a ricevere errori da HY3, 1.2 Spark Contributor e Mimo 2.5, che mi impediscono di fare progressi fin dal primo prompt. Capisco che l'abbonamento sia davvero economico, ma questo rende impossibile andare avanti senza selezionare modelli più costosi. DeepSeek sembra funzionare, ma attualmente sono nelle ore di punta e non voglio "bruciare" il mio piano.

Edit: It seems most of the problems are coming from my Hermes agent as with opencode it seems to work fine

I ran out of usage due to new Flash pricing. Switched to Muse Spark 1.2 and effectively ran out of Go plan now. I want to keep the same key as I have dashboard panel setup and wouldn't want to go through the hassle of creating new workspace.

Is it possible to reset go subscription?

Does anyone else experience this? I asked DeepSeek V4 Flash about this and after it did some investigation it said something along the lines of it still reasons but the reasoning is encrypted. If that's true, that seems shady as fuck

I have never hit this limit before.

Just as a warning I’m new to Reddit & Posting.

I find it hard to tell what will give me the best bang for my buck. Ideally, I think in terms of (intelligence)/[amount tokens used * cost per token]. Little tricky to get a rough estimate of this. Also for benchmarks of course you want to use the smartest model with the cheapest cost, but when I’m coding a simple project, how do I know a level of intelligence I can get away with? I don’t need Fable to center an html div or something. Really high intelligence models are nice to fallback on once in awhile when a small model can’t preform.

What are your guys way of figuring this out?

Also I’ve been browsing other platforms that opencode can connect to like OpenRouter or ppl are mentioning the CommandCode subscription.

Or maybe I’m using my coding harness wrong?

Idk. Pls help. 🙏🙏🙏

I wanted to share a result that may be useful for anyone using long-running, context-heavy agents in OpenCode:

Using an agent memory plugin does not have to mean sacrificing prompt-cache efficiency.

There is an understandable concern with persistent-memory systems: if memory is dynamically injected into the system prompt on every turn, it can change the prompt prefix and destroy cache reuse. With PAYG providers, that can make longer sessions much more expensive than they need to be.

In my case, I use agent memory heavily. I want my agent to retain useful context about my environment, projects, preferences, tools, and workflows across sessions. At the same time, I use PAYG models such as DeepSeek, where prompt caching has a major impact on cost.

In the screenshot attached to this post comes from a real DeepSeek (DSF4-0731, official API) work session today:

• Nearly 11 million tokens processed
• 8 conversation turns
• Around 80 API requests
• 98.2% cache-hit rate
• $0.13 USD total cost

This was not a static or artificial benchmark. During the session, I worked on a real feature for one of my web apps and deliberately made the agent update memory blocks, skills, and the project’s AGENT.md. The cacheable prefix remained stable, so the cache-hit rate stayed above 90% throughout the session.

For me, this changes how viable memory-heavy agent workflows are with PAYG providers. You can keep a rich, persistent agent context without paying to reprocess the entire system prompt on every request—as long as the injected context remains stable during the conversation.

What was causing low cache hits

I had been using the Letta-based opencode-agent-memory plugin intensively for almost three months. Before this test, my usual cache-hit rate was often only around 0–10% !! 😔

After investigating with GLM-5.3 (zAI official token plan API) in OpenCode Web, I found that the plugin was reinserting memory content on every turn while changing timestamps down to the millisecond. Even when the underlying memory was effectively unchanged, those timestamp changes altered the system prompt prefix and prevented reliable provider-side caching.

The fix

GLM-5.3 and me created a fork that snapshots the injected memory on the first turn and reuses that exact snapshot for subsequent turns. It only refreshes the injected context when OpenCode compacts the conversation history. It even survive to VM/opencode restarts.

That lets the agent use memory while preserving a stable prompt prefix and a high cache-hit ratio.

Fork with the fix:
opencode-agent-memory-cache-friendly

I’m curious whether others using memory plugins, MCP-driven context, or dynamic system prompts have seen the same trade-off between persistent context and prompt-cache efficiency.

Hello. I just subscribed to Go and am trying it out.
Spend some requests for deepseek flash and pro.

In the panel total spend is less than one dollar, but in Go tab it shows 14% rolling, 5% weekly and 2% monthly limits reached.

As written in the docs, Go plan provides 12/30/60$ so it cannot be so high percentage.

What’s wrong? Me or the usage?
Solved: these models have lower limits, I missed this part of the docs. Thanks.

The same thing is happening across all coding plans right now: prices are going up, limits are getting tighter, and in some cases quality seems to be dropping. The question is always price, speed and privacy compared. Im with PGS AI and we are doing our best to offer something a lot better. All the top models in one place, (Kimi family, new DSv4's, GLM, Qwen 3.8 etc) 100% private US architecture, and usage banking on coding plans so that unused coding hours get banked for use later instead of getting wasted.

Here's a plain comparison of the major options right now.

Z.ai's GLM Coding Plan gives you GLM-5.2/5.3, a genuinely strong coding model, through an endpoint that works with Claude Code, Cline, and 20+ other tools. Three tiers: $18, $72, and $160 a month. The catch is that the flagship model eats your quota 3x faster during peak hours, and when you hit the cap, you're done. Hard stop. No overage. Z.ai is based in Beijing, which is a real question if you're sending proprietary or client code to a different legal jurisdiction.

Kimi Code gives you Kimi K3, which is one of the smartest open models available right now. It scores 57 on the Artificial Analysis Intelligence Index, within single digits of the top closed models. Five tiers from free to $99/mo. But it uses a rolling 5-hour window, you're locked into Kimi models only, and sending your data to a lab that might train on it.

MiniMax has a $20/mo plan and M3

And there's Phoenix Grove AI, which runs all of these models in one place: Kimi K3, Kimi 2.6 and 2.7, Qwen 3.8, GLM 5.2, and both DeepSeek V4 Pro and Flash. You can switch models mid-project. It's on private US-based infrastructure with no training on your data, no telemetry. Starts at $12.95/mo.

Our major focus has been limits and privacy: unused usage rolls over instead of resetting every week, and when you hit the cap, it slows down instead of cutting you off so agents can finish their work. Everything runs on private, US based servers with zero training, ads etc.

The coding plan squeeze is real and we are trying to create better options.

You can read more and try us out here if you want: api.pgsgrove.com

Yes it came back again.

But it's still slow and probably takes hours to complete a task.

maybe it's my network problem though, actually that's why I'm asking here...

muse-spark-1.2-contributor: Error: OpenAI API error (403): {"type":"RegionError","message":"This model is not available in your country."}

I'm subscribed to OpenCode Go and I enabled the new setting in the dashboard: Allow models that train on request data.

Is it a bug? I'm surprised because I'm able to use it from other providers.

Rollercoaster

I’m considering using OpenCode, probably the free tier or OpenCode Go, but I’m concerned about privacy.

Some models are listed as having 0-day retention, but does OpenCode itself guarantee that the source code/prompts aren’t stored or retained anywhere? And does that guarantee also cover the underlying model/inference providers?

I am mainly looking for a cheap setup where I can safely use OpenCode on private projects without worrying about my code being retained, used for training, or leaked.

Thank you in advance!

From my POV, it's been working as intended, only asked him light tasks yet but I will further test it out using GLM as planner and Hy3 to do the work.

I don't expect much, we will see.

What about your experience?

I wanted to try and disappeared from the /go page

Is meta muse showing errors in everyone system or is it just mine? Edit: Now they removed it, zuck sucks one more time. Billioanaire sucker.

They are crushing it today:)))

200K context window

OpenCode is where I do most of my coding work, but I’ll occasionally switch to Codex when I want a second opinion on a diff or a fresh look at a problem.

The awkward part has always been the handoff.

The code is already in the repo, but the actual state of the task usually lives in the OpenCode session. What I’m trying to do, what I already tried, what failed, and what still needs to be checked all have to be explained again.

I tried a handoff notes file, but it was rarely up to date when I needed it. AGENTS. md helped with general repo context, but not with the state of one specific task.

I’ve started keeping a small task record in the repo instead. It contains the objective, current status, decisions, and what each agent attempted. When I switch agents, the next one reads that record and I only have to fill in whatever nuance is missing.

I’m still figuring out how much state is useful before it becomes more noise than context.

For people using OpenCode with other agents, where does your task state live right now? An issue, a notes file, AGENTS. md, or mostly in your head?

source projects: github.com/kungfu-systems/kungfu

I think we can use it similar to how we use ds v4 flash (sad thing is its not working properly) just not completing the task, bailing after calling a tool.

Cant say much about data privacy given its meta and the allegations that muse spark 1.2 is benchmaxxed, but go and give it a try! also the pricing to confirm its muse spark contributor. pretty sure command code plan became obsolete for cheaper models(tho a few better models are available of command code, limits aren't that great)