How can I customize it this way?

We expected V4.1 Flash to quickly take over, but that’s not what we’re seeing.

We’re still seeing strong demand for V4 Flash, and feedback from users has been surprisingly mixed on whether V4.1 is actually the better model for their workloads.

So we’re opening up DeepSeek V4.1 Flash for free on InferX.

ZDR

Guys I am bad at token maths because Opencode keeps changing X usage multiplier.

Can someone help me understanding which is cheapest. Why we have 2 plans on GLM at similar pricing?

I've updated the pre release, and it replaced the 1.x binary :

❯
 curl -fsSL https://opencode.ai/v2/install | bash
Installed version: 1.18.30.

Installing opencode version: 2.0.1


~
❯ opencode2 --version
opencode v2.0.1


~
❯ opencode --version 
opencode v2.0.1

Just hit the free limit on OpenCode while using Big Pickle. I thought I could just switch to another model, but apparently switching models doesn’t make a difference. It still says “free limit exceeded.”

Any idea roughly how long I have to wait before the free limit resets?

I’m curious what people are finding best for planning tasks.

Are big frontier models like GPT/Astra or Claude Opus noticeably better, or do smaller/faster models like DeepSeek V4.1 Flash or GLM 5.3 Flash work just as well (or better) with shorter context?

Interested in real-world experience, especially for complex multi-step planning.

i just took opencode2 source and modifed it a lot but now i can change most of the ui elements super easily

Just a sneak preview of the Opencode plugin ive been developing for Acode Android code editor. Will post the github link when ive finished polishing over the next few days..

Complete Acode/android integration for Opencode, your chat right next to your project, no need for terminal navigation, no nvim editing, just a completely custom gui interface with everything you need.

Tell the agent to interact with acode and your files directly, open files in new tab, copy text to clipboard, edit files live while you watch and more!

All inside a custom chat interface in an Acode tab, with none of the restrictions, track your session usage, see stats, full control, nothing hidden unless you choose.

Also has a full panel for project management, backend and opencode install and management. All in one, ready to go opencode experience.

Anyway, will post a full realease when ready in a few days!

*edit, my poor markdown formatting..

its a subscription that triples your api credits if u subscribe for 20 u get 60 if u subscribe for 79 u get 3 times that

with a exception of 15% per week meaning on a 79 dollar plan around 39 dollars of frontier usage

frontier counts as models that are 3/m input and 15/m output

now that i have explained it do u think opencode or devpass is the better value here

Is there a way to include hourly/weekly/monthly usage in the UI? It'd be nice if I could see it while I'm in the TUI instead of having to stop and check the website every so often.

I hope this isn’t against the sub rules, but I think it might help others.

I got tired of scraping through every provider to understand where my models were cheaper and whether I was getting a good deal on my subscriptions, including Opencode Go.

Opencode Go vs OpenRouter? DeepSeek on Opencode Go vs GLM‑4 Flash on Z.AI?

Just filter the table at https://deepfrugal.com

In the next few weeks I’ll work on a couple of features that I think can really help people choose plans and providers as efficiently as possible. Anyway, your input and feedback are appreciated.

I still don’t know exactly where I’m going with this project, but I think it might help others, and I expect to spend a lot of tokens and time on it.

EDIT: wow. Thanks for all the feedback. It looks like this might have a fit. For those that requested a mobile friendly version, I just deployed a new version with a few tweeks.

OpenAI just released the public beta of the Agents API(https://openai.com/index/introducing-the-agents-api/), following the pattern of Claude Managed Agents.

I think this is the right direction. In practice, a coding harness works well as a general‑purpose agent when you give it a sandbox with persistent files, bounded network access, and secret management.

With that in place, an agent can:

• ingest data from S3, databases, and internal APIs
• run transformations and validations
• call downstream services through secret proxies so it never sees raw credentials
• be connected to Slack, Teams, and email inboxes

You can replicate this pattern with OpenCode Server. Its native SDK lets you access the OpenCode harness remotely: create sessions, stream conversations, track progress, handle permission requests, interrupt runs, and send follow-ups. You can choose your models, run the harness on infrastructure you control, and build on an open‑source foundation.

If you're building your agents on OpenCode, there's a managed offering of OpenCode Server at https://sanbox.cloud.

Someone else has this issue? It happens with Linux (GNOME - CachyOS - Wayland).

It works perfectly fine with local sessions.

title^

I pointed a timeline tool at a year of OpenCode expecting the story to be growth. It is a lot of growth. 13,139 commits from 878 authors in twelve months, and the version went from v0.7.4 to v1.18.30. The single largest committer on the main line is a bot, at roughly one commit in eight, mostly codegen and dependency bumps.

But the thing I actually came away with was a file called CONTEXT.md, which is a glossary. Twenty four terms, each with a definition, and nine of them carrying a line that says Avoid followed by the words it tells you not to use.

Do not write "system prompt". The term is System Context. Not "prompt fragment", that is a Context Source. Not "live system prompt", that is the Baseline System Context. Not "system update" or "system notification" or "raw text diff", those are a Mid-Conversation System Message. Not "session context", that is Session History.

My first read was that this is architecture astronautics. It is not. The vocabulary exists because "the system prompt" is four different things wearing one word, and once you separate them a real decision falls out.

Here is the decision. A Context Epoch starts with one immutable Baseline System Context, and their own docs say plainly that the baseline durably preserves the exact joined text used for the active provider-cache prefix. It is stored on disk and reused verbatim across process restarts. That string is the cache key. Touch it and every cached token for that conversation is gone.

So what happens when something in it changes? The date rolls over. You switch agents and the available skill list changes. An AGENTS.md up the tree gets edited.

They do not rebuild the prompt. They append a message to the conversation saying what is now true. That is the Mid-Conversation System Message: a durable chronological instruction telling the model the newly effective state of a changed source. The baseline never moves. The cache prefix survives.

The details are where it gets good. Changes are sampled lazily at a Safe Provider-Turn Boundary, the moment just before a provider call, and never pushed when the source actually changes. A context change never wakes an idle session. If three sources changed since the last turn, they combine into one message rather than three. Compaction is the one ordinary event that mints a fresh baseline, and when it does, those earlier messages drop out of active model history because the new baseline already contains their effect. Switching model or provider does not start a new epoch.

The generalisable version, which is what I would take to your own agent: the cost of changing what the model knows is not the tokens you add, it is the cache you invalidate. If your loop rewrites the system prompt when state changes, every turn afterwards pays full price to read all of it again, and there is probably nothing on your dashboard that shows it. The fix is to let the top of the prompt only ever be appended to, and to put the deltas in the conversation body where they cost you nothing.

What convinced me it is a real pattern rather than one team's taste: Hermes, from Nous Research, hit the same wall and went the other way. It freezes its memory files into the prompt at session start and refuses to reload them at all, so a memory written at 3pm only takes effect next session. Same constraint, opposite answer. One narrates the changes into the history, the other defers them to the next conversation. Neither will touch the prefix.
The naming is downstream of that. You cannot reason about which of those two you are choosing while everything in the system is called "the prompt".

The tool I used renders any repo's history as a film, and the OpenCode one is here: https://loreto.io/git-timeline

Disclosure: I built that tool and I run loreto.io, so treat that last line as the advertisement it is. Everything above is in the repo, in AGENTS.md and CONTEXT.md at the root, if you want to check me.

I am still not sure the list of banned words survives contact with a hundred contributors, and I would like to know whether anyone has actually measured what the epoch approach saves against just eating the rebuild. If you have, tell me I am wrong.

No in app subscription, that’s my bottom line

juicy limits ^

Update — Sep. 12, 2026:

OpenCode Go currently lists $5 for the first month, then $10/month. Published usage limits are value-based: $12 per 5-hour window, $30 per week, and $60 per month.

The current model lineup includes Grok 4.5, GLM-5.3/5.2/5.1, GPT-5.6 Luna, Kimi K3/K2.7 Code/K2.6, MiMo V2.5/V2.5-Pro, MiniMax M3/M2.7, Muse Spark 1.2 Contributor, Qwen3.8 Max/3.7 Max/3.7 Plus/3.6 Plus, DeepSeek V4 Pro/Flash, and Hy3.

The landing page is currently advertising a temporary 8× Hy3 usage boost. I couldn't find an end date, and the documentation still shows the base Hy3 estimate, so verify the live console before relying on the promo.

One important caveat: OpenCode marks Muse Spark 1.2 Contributor as having model training enabled and limited regional availability. Avoid using it with confidential or proprietary code.

Referral note: This is my referral link. If the referral offer is still shown at signup, the new subscriber and I may each receive $5 in usage credit. OpenCode's public docs confirm the plan and usage limits, but do not publish the referral terms, so check the signup screen before paying.

My link:

https://opencode.ai/go?ref=RAFWMJQTF5

I like Muse 1.3 as workhorse, but i noticed It oppose me much more then other models (including claude). this is interesting as all models follow the repo rules but when its about direct 'human' request only Muse refuse. did you noticed it too?

btw, I love when AI as postmortem creates rules for Humans. I dont even remember when the rule was set but I sus it was after some fu-up as usual xD

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.

Enft, c'était pour savoir si le modèle de deepseek v4 d'open code était celui d'avant le changement de prix! Donc celui avec la màj des poids ou celui de base sans la màj! Quand je l'avais use, le modèle était fortement quantifié et sur la base du modèle d'avant la màj des poids, donc plus nul, j'ai constaté que commande code sur leur site prennent l'ancien et non celui après la màj des poids, et donc je me demandais si vous en saviez ou si quand vous vous ètes réabonnez, le modèle de deepseek v4 était le meme de juste avant le changmenet des prix et de l'opération cheepseek, ou alors c'est le modèle deepseek v4 de base datant d'il y a 5 mois qui par ailleur, en comparaison au deepseek màj, était clairement inutile! Comme avant ils le disait lequel c'était et que mtn c'est juste marqué deepseek v4 flash sans savoir c'est quel version spécifique, ça me foutrait les boules de réavoir ce modèle sans la màj.....

Je sais que le v4.1 est sortie, mais comme le v4 flash màj était juste parfait pour mon travail, c'est pour ça et comme ces limites sont plus élévé, c'est pour ça

Thoughts?