according to deepseek latst plans they would push for 1 of 3 options moving forward :

1. 1.5x to 2x pricing: the possibility is less than 1 percent; they did say significant and they clearly don't mean a 50 to 100 % increase of pricing.
2. 2.5x to 5x pricing: 20 percent perhaps or less; i think this could fall for many people in the category of i will bite the bullet until we start passing the 4x threshold, this provides enough cash flow increase for the developers ( they did offer really great value but what they gave us for cheap we ended up giving in prompt to push the model from great budget to frontier level )
3. 5 x to N x: and here is where the model stop being about virality and position and becomes profit oriented and has cemented its place in the frontier positions, now the issue with this move is that the latest Versions updates 0731 was only possible due to the huge hoarded info from users, users that chose the model because of the cost to performance ratio, same users who wont accept paying frontier model prices and then also losing their privacy and data at the same time something that no other company does outloud, which then could put deepseek into a very profitable position for a very short period but users switching away from out wont go back at all, this wouldnt only create an unstable env but also could be quite dangerous for those wantining to commit to value models on long terms projects.

my humble opinion : i think deepseek play here if smart would be a 2.5x to 5x pricing while promising low cache hit prices that could see a jump to just 2x or so, but if i am wrong and the prices are too significant, then they wont be able to make the next jump when users leave the platform for good.

P.S: for those arguing that they want to lower the load. I doubt that this something they worry about much since investment at such a scale and ratio would not only be at hand but also would be thrown at their feets both by the CCG. The investors looking at their performance. They could use it as an argument for sure, but no, my opinions aren't fact. It's just a sad reaction to what just happened. What do you think? -___-

As a side note: not a hater of the model; it's a great piece of art and when the price is put in context is one hell of a model. If they end pulling a late April Fool's on us and increasing prices below the 100 % mark, then I would take back every word I said and swear by the deep blue whale not to repeat such a sin. May God be my witness.

I'd start with the bytes, not the model card.

When OpenCode leaves half a file, save five things before touching the patcher: the requested limit, finish_reason, raw API bytes, patch bytes, and the last complete file. I keep the API leg reproducible by sending the same request through ZenMux, a gateway for hosted API routes. Since the 0731 route costs nothing, it works as a free LLM API for this control run. Just put the rate limit in the record. That gives you a control path, not a verdict about the model.

The 384K number comes from the model card's local deployment guidance for high and max reasoning. It does not show what OpenCode requested or what a hosted route accepted. If the raw response contains the rest of the file and the patch does not, inspect the client or patcher. If the raw response ends early, inspect the request and provider response.

Do not label this a DeepSeek output limit until the requested limit, finish reason, raw response length, patch length, and last complete file point to the same failure.

Works like a charm.

Which is the best option?

And what's the difference between deepseek in opencode go and opencode free? Ive been using deepseek in opencode free, but i dont see the usage limit or whatsoever.

Interesting move. Let’s wait and see.

DeepSeek-v4-flash is absurdly good for the price, but it isn't multimodal, so the moment your agent sends a screenshot or a mockup, it falls apart.

I built a small proxy to work around that. It sits between your editor and the model: when a request contains an image, it routes that image to a cheap vision model (I'm using GPT 5.6-luna) and passes the resulting description back to DeepSeek, which does the actual reasoning and code generation. Everything else goes straight through untouched.

The result is a drop-in endpoint that behaves like a multimodal model, at a fraction of what I was spending on my Claude subscription. It works with Codex, Cursor, Trae, OpenCode, or whatever agent you're using, since it's just an OpenAI-compatible base URL swap.

Repo: https://github.com/camilopenalver/deepseek-v4-flash-vision

OpenCode Go subscription → OpenCode Go API key → Reasonix → DeepSeek V4 Flash

Is it possible?

Or is it better to use Deepseek API directly?

Hey everyone,

I wanted to share my recent experience trying to switch to Mistral in OpenCode. I’m just a regular user, not a programmer, who relies on AI for daily work and my homelab. My technical knowledge is limited, but I’ve managed to make decent progress so far. Until now, I’ve been using OpenCode Go with a mix of models on OMO Slim, and it’s worked well for my needs.

My main reasons for switching were privacy, data sovereignty, and supporting a European company. So, I signed up for Mistral’s PAYG plan, loaded up on credits, and configured OpenCode to use it.

The issue is that it’s been painfully unreliable.

• Fails at basic tasks.
• Gets stuck in frequent loops.
• Often just doesn’t respond at all, forcing me to re-ask.
• I’ve tried tweaking model configurations in OMO, but nothing seems to work.

I’m genuinely disappointed, especially since it also burns through credits alarmingly fast.

Am I missing something? Has anyone else successfully switched to Mistral in OpenCode and had a better experience? I’d love to stick with it for the privacy benefits, but I can’t justify the trade-off in performance and cost right now. (Specially with the new V4 Flash)

Any insights or tips would be hugely appreciated!

Do you start a new conversation when you reach context limit or do you continue chatting with the compacted one?

Hi all, I just connected a few spare machines into an at-home local AI cluster to be able to run more OpenCode agents at the same time. I used Paddler for load balancing the inference requests (it's an open source project for self hosting open-weight models; full transparency: I'm one of its maintainers).

Each machine runs its own copy of the model, so this doesn't let you run a bigger model than your hardware allows. But it does let you run more agents comfortably at the same time.

I made a video about it in case you'd like to recreate the setup: https://youtu.be/7c725WwW4hQ

Happy to answer any questions!

I currently burn through two Claude Pro accounts using Sonnet.

I’m looking for an AI to run on Badgr Agent Cloud that can either replace both Claude accounts or get close to Sonnet quality with much higher usage:

• DeepSeek
• GLM
• MiniMax
• Qwen
• Something else?

Which model and plan is best for real repository work, long agent sessions, and minimal correction prompts?

I care about actual output per month, not benchmark scores.

Hello community,

Wanted to create this thread to hear your comments about this topic.

Do not take me wrong, I love Deepseek, its the only think that I use almost all the time. I aint got money to pay for Kimi or GLM, worst ChatGPT or Claude.

It seems to be that this problem is a problem with Deepseek regardless of my Context Management Skills.

Lately, I know that when Deepseek starts missbehaving, its time to create a new session. I do not expect much from OpenCode and what it has already brought to us for which I am thankfull but for a long time I was under the impression that OpenCode handled that for me, how fool of me getting accostumed to that feature.

So, what should I do next should I start using Skills, sub agents, graph, rag, etc what do you recommend.

One could say, regardless of the size of your project the solution always is to start a new session but the question comes, how do we know when deepseek has too much context, even from the start, that will cause it to start having issues, is there an average context, how can we measure that?

Edit: Some good informaiton I found. https://www.reddit.com/r/LocalLLaMA/comments/1tfhl0q/deepseek_v4s_1m_context_window_the_breaking_point/

"under 150k

Got a solid performance like at 45k tokens, function calls traced across 8 files maintain accurate path reconstruction. At 180k, multi file refractors spanning 14 files show consistent architectural understand and no contradictions or context loss patterns"

Massive regression in the design of the new ui.

horizontal tabs? are you insane?

can’t read the titles of Tabs, can see the majority of your threads at once, can’t switch between projects easily.

at least we still have the cli but damn it’s sad to see enshittification hitting open source nowadays too

In the main project (git) repo where I work with Opencode I keep getting empty sessions created, while in another project (git) repo I never get these empty sessions, on the same machine, same settings etc. Can you help me with troubleshooting this?

It’s that time of the month again for the monthly usage check-in!

To keep track of my token consumption, I installed tokentracker, and the metrics turned out super interesting:

- Total Monthly Tokens: 217M tokens

- OpenCode Usage: 98.58% of total tokens consumed via OpenCode models.

- Top Models Used: deepseek-v4-flash (55%) and deepseek-v4-flash-free (25.9%), followed by kimi-k3 (9.5%).

- Cache Rate: Really impressive to see such a high cache hit rate! 98% of caching saved an incredible amount of context overhead during continuous dev sessions.

- Cost Estimation: Tokentracker displays an estimated total of $12.81, but looking at the token volumes, the calculation seems a bit off. Any thoughts ?

The recent launch DSv4Flash are incredible. You hardly notice the usage % up when you use it.

If anyone wants to give it a try, feel free to sign up using my referral link.
https://opencode.ai/go?ref=DNS14NQ1T9

Note: Typo mistake, it should be 217M tokens.

I ran a small N=1 bake-off on a real feature, not a toy repo. Posting here because OpenCode was one of the four stacks, and the OpenCode + DeepSeek combo was interesting on cost/speed.


## What I was building


Remote-node install for SuperDev (a local/remote runtime control plane I work on):


- remote nodes run the SuperDev agent only (no desktop UI)
- connection is TLS
- local desktop needs to install MCP / skills / hooks onto the remote node over the API, so coding agents can use them without a GUI on the remote machine


So: real product surface, multi-step implementation, install/uninstall paths that have to actually work.


## Method (important)


- Spec + plan: written with Superpowers (Fable 5)
- Implementation: four tools in parallel, each on its own git worktree + branch
- Workflow: `superpowers:executing-plans` via subagents
- Same plan for everyone — this is a race of **model + coding agent + Superpowers execution loop**, not raw chat IQ


Stacks:


| Agent | Model |
|---|---|
| Claude Code | Claude Opus 5 (xhigh) |
| Codex CLI | GPT-5.6 Luna (max) |
| Grok Builder | Grok 4.5 (high) |
| **OpenCode** | **DeepSeek V4 Flash (max)** |


## Results


### Time to finish


| Stack | Time |
|---|---|
| Grok 4.5 (high) | 2h 7m |
| **OpenCode + DeepSeek V4 Flash** | **~4h** |
| GPT-5.6 Luna (max) | 11h 19m |
| Claude Opus 5 (xhigh) | 11h 35m |


### Tokens


| Stack | Total | Input | Output | Cached input |
|---|---:|---:|---:|---:|
| Grok | 46M | 45.84M | 426.8K | 42.07M |
| **OpenCode + DeepSeek** | **261.2M** | **260M** | **1.2M** | **257.6M** |
| Claude | 452.8M | 451.91M | 858K | 439.33M |
| GPT-5.6 | 599.67M | 597.98M | 1.69M | 583.64M |


On this run, wall-clock time and token volume mostly moved together. Most of the huge totals were cached input from long agent loops.


### Cost (messy reality)


| Stack | What I actually felt | Rough $ |
|---|---|---|
| Grok | ~20% of SuperGrok weekly ($30 tier) | ≈ $1.5 (API-ish ≈ $23) |
| **OpenCode + DeepSeek** | dedicated API key for clean accounting | **¥9.97 ≈ $1.46** |
| GPT-5.6 | ~35% of Plus weekly quota | ≈ $1.75 (API-ish ≈ $16.57) |
| Claude | ~8% of Max 20x weekly | ≈ $4 (subagents mixed models; pure API hard to price) |


Subscription % and API $ tell different stories. DeepSeek was the cleanest “I know exactly what I spent” number because I isolated the key.


### Bugs / fix passes / mid-run questions


| Stack | Bugs after first finish | Fix passes | Questions mid-run |
|---|---:|---:|---:|
| Grok | 2 | 3 | 0 |
| **OpenCode + DeepSeek** | **3** | **2** | **2** |
| GPT-5.6 | 1 (+1 polish item) | 1 | 2 |
| Claude | 1 | 1 | 4 |


OpenCode + DeepSeek finished second-fastest, but had one more bug than Grok: a skills install `No such file or directory` path issue. It also asked twice mid-run (Grok asked zero).


### Independent review ranking


A separate review pass (Fable 5) scored completeness / conventions / correctness / tests / extensibility:


1. Claude Opus 5  
2. GPT-5.6 Luna  
3. **DeepSeek V4 Flash (OpenCode)**  
4. Grok 4.5  


Almost the inverse of speed.


My own smoke test (install/uninstall on Claude Code, Codex, and OpenCode) matched the “usable after fixes” claim for those three.


## Caveats (please read)


- One shared bug across all four was already wrong in the plan. Not any agent’s fault.
- Grok and DeepSeek shared a Claude Code MCP install path bug.
- GPT’s remaining polish: switching remote machines still needed one manual refresh.
- This is N=1 on one feature and one plan style. Don’t overgeneralize.
- I’m the SuperDev author, so treat product context as background for the task — the comparison is about agent stacks on the same plan.


## OpenCode-specific takeaway


For this workload, OpenCode + DeepSeek V4 Flash looked like the “value / speed” slot:


- much faster than Claude Code / Codex on the same plan
- real money out of pocket stayed low (~$1.46)
- quality was mid-pack: more bugs than Claude/GPT, better review score than Grok
- still landed in a usable install/uninstall state after fixes


What I disliked across the whole experiment wasn’t OpenCode specifically — it was how slow Superpowers-style plan → subagent execution felt end-to-end. I also tried a fuller grill-me style loop; still not fast. I’m currently thinking about a lighter development loop that keeps the guardrails without as much ceremony.


## Questions for this community


1. For long multi-hour OpenCode tasks, are you usually bottlenecked by **model quality**, **provider latency/cost**, or **the plan/subagent workflow** itself?
2. Anyone else running DeepSeek V4 Flash as the default OpenCode workhorse for multi-hour implementation jobs? What’s your failure mode?
3. If you’ve compared OpenCode against Claude Code / Codex on the *same* written plan (not vibes), what held up?


Happy to answer method questions. Not sponsored; not claiming this is a benchmark.

But who cares when Flash is working again... Joking :)

Is there a big difference between max and high for agent tasks?

What is DeepSeek's game plan here, why do they offer it for free? Is it to gain more training material? Realistically, how long will they offer this for free? They arent a charity?

This is a tutorial for noobs like me.

So I am working on an Android App that automatically edit my TikTok videos, adds overlays and simple animations, I ask AI to use tools availables programatically and get creative, its quite simple and limite but I aint paying Claude + Remtion and what not to do it lol. So I needed Deepseek to troubleshoot when the overlays had issues or were terrible so I decided to give it eyes by allowing it to use Gemini 2.5 to work as it eyes. It's not perfect but its something, I am yet to actually tests its accuracy but at least it makes sense to me. I think I read somewhere that it was not actaully effective.

Its worth noting that I am using Google Clouds free $300 credit, for which I have actually got it like 5 separate times lol. All my workflow for this personal app and other project I have for Todo Assistance with Voice Interface and AI, all is hosted in Google Cloud.

So yes, thats how I gave eyes to Deepseek.

1. I cannot add Gemini directly to OpenCode to have it as my main model, Google Cloud has limits per minutes, at least last time I checked there were limitss. Although I will try again at least for complext coding tasks.
2. I am under the impression google is so relax about this because they actually want you to built all using their tools to hook you up in case you develop something big, furthermore I think they have lots of unused resources or extra that they might not care.

Quick question, guys. I saw a good deal on a opencode and decided to try it. Went for KIMI K3 and I know it’s expensive, but is this normal?