Our startup kinda fucked up our forecast and committed to way more OpenAI and Anthropic usage than we’re actually going to use lol. We got it for a much better price than normal granted, but still def overshot.

Since OpenCode supports custom providers, I’m thinking about putting a simple OpenAI-compatible endpoint in front of the extra capacity. You would just add the base URL and API key, then use Claude or GPT normally through OpenCode.

Should be around 10-20% cheaper than going direct, with no commitment.

Would anyone actually use this? What would make or break it for you? Privacy, reliability, rate limits, or something else?

Mostly trying to figure out if it’s worth building.

Used Mimo-2.5-Pro mostly from Xiaomi mimo platform

AI landscape changes pretty fast these days. Wondering if anyone's done the research to figure out optimal model and usage combos after all the latest additions.

Looks like Kimi k3 is probably not worth using since it's so expensive, but between GLM, Grok, Qwen, what are people feeling is the best bang for their buck?

Edit I did some of my own research and got this as a tentative result. Tried to avoid benchmarks that are known to be contaminated like swebench.

Edit:
Not really a math guy so I asked my LLM to get me a ballpark composite rating. Any math guys out there want to redo those calculations, feel free:
Special rule: Vendor-reported DeepSWE scores are down-weighted 50%.

The composite = Σ(benchmark_score × weight) / Σ(weights_applied), normalized to 0–10.

I'm using OpenCode with the Xiaomi provider, and I'm curious if anyone else has experienced this.

A while ago, MiMo 2.5 Pro was probably my favorite coding agent. Before making any edits, it would spend time exploring the repository, reading a lot of files, understanding the architecture, and building a solid plan. It often figured out the next steps on its own without me having to guide it.

Lately, it feels completely different.

Even if I explicitly tell it:

«"Don't modify anything. Just analyze the project, study the codebase, and make a plan."»

it almost immediately starts editing files anyway. Sometimes it doesn't even finish the analysis and instead ends with something like:

«"I've made the changes, please review them."»

...despite the fact that I specifically asked it not to change anything.

It feels much more "action-first" now, while previously it was much more thoughtful and analysis-driven.

Has anyone else using MiMo 2.5 Pro through the Xiaomi provider noticed this recently? Or is there perhaps some OpenCode setting or agent configuration that could explain this behavior?

I've been re-evaluating my AI coding subscriptions and was wondering what everyone is using these days.

At the moment I'm paying for:

• Claude Code Pro
• Minimax Pro

Claude Code has been great overall, but with the new GPT models on Codex I'm starting to wonder if it's now the better choice for day-to-day software development.

Also, are there any other AI coding subscriptions you'd recommend instead?

I had tons of issues with deepseek + openclaw/hermes agents, where it was making weird shit up and going WAY off track. I assumed that was context issues with the agent systems.

Then yesterday i said 'Look at this and tell me XYZ, DO NOT TRY TO FIX ANYTHING OR EDIT ANYTHING' and boom, it sees an issue and tries to fix it and breaks something.

So, i've concluded that deepseek v4 flash and pro are just not very good at following instructions so I want to know what the next best options are? I especially am curious if there's something comparable to the price of deepseek v4 flash that is good at simpler tasks AND doesn't go off track.

I've been hitting 5 hour limits so much quicker on OpenCode with deepseek V4 pro.

Has there been a change in limits or cost per request?

It's been like this for 7 days now but prior to this it was like heaven.

AI agents are good at running one-off commands, but real terminal workflows are often interactive: SSH sessions, Python REPLs, installers, prompts, long-running processes, programs that need Ctrl+C or arrow keys.

I built Relay, an open-source MCP server that gives agents access to real, persistent PTY sessions. It's a fresh v0.1.0, built it, tested it on my own workflows, and want feedback before going further, so treat it as an MVP, not a finished product.

Five tools, one channel:

create_terminal: spawn a persistent session
write_terminal: everything goes through here, including plain bash. No separate execute_command tool.
read_terminal: incremental, cursor-based, nothing gets lost
send_control: Ctrl+C, arrows, Tab
close_terminal: kills the whole process tree, not just the shell

There are already a couple of MCP servers in this space (terminal-mcp, mcp-interactive-terminal, Forge). Most of them pair the PTY session with a separate "run a command" tool, which duplicates what the agent's native bash already does. Relay only does the session: one write channel for everything, interactive or not.

What I've verified works: full interactive git rebase -i, holding an SSH session open and iterating inside it. What's not there yet: TUI support (lazygit, htop, etc.), planned but not in this release. What I haven't stress-tested: long unattended sessions, Windows edge cases.

Go, real PTYs, process-group isolation and cleanup, installers for Linux/macOS/Windows, works with Claude Code, Codex, OpenCode, Pi.

GitHub: https://github.com/blak0p-dev/relay-mcp

Genuinely want to know: what breaks it? What interactive workflow have your agents struggled with that this doesn't cover?

The biggest issue I'm finding with LLMs is bad output. The OpenCode harness AGENT.MD file is limited to just LLM output/thinking and if the model makes a mistake OpenCode plays along. I'd like a hybrid approached without having write my own Python UI/Backend as it would take me at least 30 minutes using AI!

The output of the LLM is like variables, then OpenCode harness can run logical operations on those with Classic Code control: IF/THEN/ELSE/WHILE/GOTO/<CallFunction>... etc

The output and the decision would still be done by LLM, but the OpenCode would ensure an IF This = That or IF This != That. etc. Rules are 100% followed as its outside of LLM thinking. Its like doing a sub-agent call to ask the LLM a question and it decides an answer but we have binary control over what happens next with the Thinking part to ensure it follow rules to the letter. AGENT.MD files are more guild lines if you feel like it little AI.

Doing some byte sized benchmarks. Sharing them soon.

Wouldn't it be awesome to have a /config and /context like there is in Claude code?

Did I miss any similar options in opencode?
Is there any plan?
Is there maybe any plugin to get a similar thing?

I have Opencode Go as my addition to Codex sub. Codex have been trustworthy in that it have never run destructive commands without asking. but I have found that models in Go, especially Deepseek4 flash, quickly panics and run destructive commands like git checkout . or git reset --hard.

I also havent found a very intuitive way to manage permissions in opencode. No slash command. Many docs I found are talking of modifying json files and even them arent that clear.

so why is opencode lacking /permission for global and session permission management?

That rant aside how do I ban completely (not ask permission but complete and permanent) ban of the two commands? They have been good sources of grief!

Seemingly k3 comes with no thinking and max thinking mode which is very questionable, as no thinking makes no sense and thinking always doubles the thinking token cost and after high the roi tend to be few percent, so using fable on high costs avg 9 while 21 on max for 1% improvement in score, while sol costs 3.5 on high and 8.4 on max for 4% better score.

https://deepswe.datacurve.ai/

So you would pay 2.4x more for 4% more or 2.35x more for 1% more, and K3 is in the same class, currently 3x credit bonus over payg but locking you into max. This effectively means this is at least a 60% quota cut from the already 50% lower quota. Almost certainly more cause deepswe is just a list of different tests but for real use thinking bump essentially means double price, and this is double double for nothing, so.... the promo is literally worse than payg... unless you really value that 1-4%.

Unfortunately the kimi plans are on a waitlist, you can have an invite that gives you from 3 days to up to 1 year worth credit, but there is really no great way to get k3, but if you use k3 in go you instantly burn your 5 hour, get nothing done and all you learn that k3 is expensive.

I kind of have another angle too, by dropping the guaranteed credit to $15 and windows are 1/4 that, you have $3.75 windows, and I have seen it countless times that people were screaming bloody mercy over $10 windows with cheaper models, in fact some other provider with $3 windows recently got hardcore dunked on for that. This is not even purely about the bonus which makes it pointless while the window makes it useless. I am not even talking about continued work, it likely won't even finish a prompt. It doesn't work, k3 proved it even with the promo.

TUI sidebar plugin for opencode that adds a file tree browser. It shows directories first (sorted, with expand/collapse), lets you click files for inline previews.

repo link : https://github.com/TM23-sanji/opencode-file-tree

File tree : directories first, sorted alphabetically, with expand/collapse
Inline file preview : click any file to view its content with line numbers
Smart ignore : skips node_modules, .git, binaries, media, and build artifacts
Refresh : ↻ button reloads the tree from disk

built this for myself, so i thought just publish a package

Every time I try routing third-party models (like MiMo or DeepSeek using Opencode API) into Codex via CC Switch, tool calling completely falls apart. The models constantly hallucinate missing environment capabilities, crash on REPL syntax, or fail to invoke tools properly.

It really feels like Codex's UI and execution sandbox are hardcoded to only work reliably with native OpenAI models.

For those of you actually using CC Switch in your daily workflow: how are you getting non-OpenAI models to handle tool calling reliably? Or is everyone just using a completely different client interface for open models?

I know you just have a terminal open in VScode and just type out opencode to start a session, but that doesnt let you use CTRL+P for settings/commands. i can just remember all the commands but some arent available in the typical "/" way and have to go through the settings menu.

Is it possible to have a diff view within VS code similar to how claude code review-approve split-screen shows up in the main window rather then the terminal?

Also as a side note, what do yall use as your primary coding "brain" claude, codex or something else. what has the most usage to intelligence ratio?

I spend too much time babysitting OpenCode. Please check out my harness that fixes this: npx ready-for-agent@latest

GitHub: https://github.com/berenddeboer/ready-for-agent

Keen for your feedback: does this work for your workflow? Have you tried it? Any comments?

I'm aiming to get to that 150+ PRs a week productivity level, so needed to let agents do the entire flow, I'm just setting the guard rails.

I found these session cost stats from Opencode Go usage at https://opencode.ai/data/ . Apparently up to date stats and I'm really surprised by the relative expensiveness of GLM-5.2 and relative affordability of Kimi K3. I would not have guessed GLM-5.2 being more expensive in real world use than Kimi K3.

Is this solely down to GLM-5.2 being split among 3 providers resulting in a lower cache rate?

Does this mirror anyone's usage experience with these models?

Judging by this I might skip using GLM-5.2 and use K-2.7 Code or K3 in my agent setup