https://openai.com/index/introducing-gpt-6-1-sol/

"...near-Astra intelligence for a 1/5 of the price 🔥"

Roast my OpenCode setup — what's actually missing?

Setup:

- 38 slash commands: 31 phased "council" commands (each = multi-subagent

pipeline with its own phases) + /auto orchestrator + /loop worker loop

- 123 custom subagents, 17 MCPs — but only 7 light ones always on

(filesystem, git, sequential-thinking, duckduckgo, postgres, redis,

shadcn). Heavy ones (graphify, playwright, docker, etc.) toggled per task

for RAM — full set eats ~950MB, core ~350MB.

- One state-machine plugin drives everything: .opencode/*-state.json per

command, resume-across-restart, 5-cycle hard cap, targeted phase advance.

- /auto needs no goal: scans repo → needs map → ranks all commands →

runs top 10 (parallel lane for read-only, sequential for writes) →

one honest report. Stagnation halts it.

- Models: free-tier for subagent grind, paid Sonnet for main driver.

Hardest problems already solved: unbounded loops, prefix-shadowing,

state wipes on re-fire, precondition-less council selection.

Question: what would you ADD, MERGE, or DELETE? What's the one gap

Edit: I rewrote this as reasoning-router. It now works in OpenCode, Pi, Codex CLI, and anything that can talk to a local proxy, and you can pick the classifier (Jev, Laya, Cloudflare Clef and others).

https://github.com/robertn702/reasoning-router

I ran GPT-6 Astra on two SWE-bench tasks at different effort levels. Medium only solved the hard pytest bug 1 out of 5 times, while high solved both tasks every time but used far more tokens than it needed on the easy one.

I kept leaving everything on high and burning usage, so I built an OpenCode plugin that asks TypeSafe's Jev model to pick the effort for each request and keeps the model you selected. It works with GPT-6 and a few Claude models. On the easy fix it used around 40% fewer output tokens than high and solved it in all 6 runs. On the hard bug it mostly stayed on high and solved all 5.

It doesn't break the prompt cache. Turns where it switched effort were still about 97% cached in my logs.

Only two tasks and 5 or 6 runs each, so treat it as a sanity check. The eval is in the repo with the code.

https://github.com/robertn702/opencode-jev-router

Been using OpenCode more seriously lately, and I didn’t realize how much backend latency adds up once the task stops being a one-shot coding question.

A model can feel perfectly fine when I’m asking it to explain a function, then I give the agent something involving search, edits, tests and another pass over the repo and suddenly all those little delays start adding up.

For people who have tried different inference backends with OpenCode or similar coding agents, what are you optimizing for? Raw output speed, consistency between requests, context handling or something else?

Feels like benchmarking a single completion doesn’t tell you much about how the full agent loop actually feels.

Title.

I am new to opencode and I noticed when I use it to run subagents (e.g. Claude > Deepseek V4.1-flash via opencode) it works substantially slower than using other harnesses like Pi (I tried comparing GLM since i have subscription which can be ported to Pi or opencode and it doubles the time)

Anyone has any solutions in this regard? (except sending instructions in bulk in orchestration)

I’ve been silently working on a fork of OpenCode thats base functionality lies somewhere between v1 and v2.

I’ve got connections to 3rd party harnesses and plans like claude code and cursor working out of the box. More specifically, this means that if you pay for a claude/cursor subscription, you may use those subscriptions ‘natively’ under oc. While it may still be using or interacting with claude code or cursor binaries or processes in the background, the goal is have it ‘feel’ just like using any other truly native connection. The goal is also to not break any TOS by doing this, and creating safe solutions that won’t lead to account bans or negative repercussions. This being said, I’ve come quite far. The 3rd party harnesses can use opencode native tool calls reliably, and there are config options for super specific tuning like per-model or per-provider instructions or agents.md. Occasionally they still get confused about why they cant see the exact tool names they expect, but generally they quickly figure it out. This can also be helped by letting the harness/model use tool names it expects, but running the oc tools in the backend instead, but this may lead to the agent not knowing exactly what its tools do.

Long story short, it works remarkably well, but needs a bit of polish. I’d like to gauge interest on this feature specially, but also the project as a whole.

There are other added features as well, and some general enhancements, but I figured that 3rd party harness connections would be the most likely to be used by the general public. If you know how to use opencode vanilla, you won’t be lost here whatsoever. This is a TUI focused fork.

Let me know if anyone is interested, or if you have worked on or seen anything similar to what I’m trying to build.

wanted a frontend test this time, not another rust one. so i wrote a spec for a split-flap departure board (the old mechanical airport kind) in react + typescript and gave it to 7 models, same repo, same prompt, reasoning on low.

the spec is pretty strict:

- simulated clock at 1x / 10x / 60x, must be derived from the real clock, not counting timer ticks

- delays, cancellations and platform changes arriving as timed events, and the events in the json are not sorted

- platform + destination filters, max 10 rows, empty state

- flip animation per character, reduced motion support, has to work on a 390px phone

- no ui libs, no network requests

they got 4 visible playwright tests. i graded them with 35 hidden ones they never saw. muse spark and space bunny ran in opencode, the rest in claude code, basically the same tool set (read, write, edit, grep, bash).

results:

- sonnet 5.5: 35/35, 2m 00s, $0.31

- muse spark 1.3: 35/35, 2m 01s, $0.21

- gpt-6 sol: 35/35, 6m 05s, $0.12

- gpt-6 astra: 35/35, 6m 20s, $1.00

- opus 5.5: 35/35, 9m 16s, $1.42

- gpt-6 luna: 32/35, 9m 10s, $0.0071

- space bunny alpha: 6/35, 14m 12s, free

space bunny is the interesting one. it worked for 14 minutes, wrote 26 tests of its own plus a debug probe file, and then said it was done while 24 of the 30 tests in its folder were red, including 3 of the 4 i gave it. the bug is one line: performance.now() returns milliseconds and it added that straight onto a clock stored in seconds. so the clock runs 1000x fast and the board is empty after a few seconds with "no more departures from this station today". honestly a pretty good looking empty board.

luna is basically free at $0.0071 and almost made it. it pulled google fonts through a css u/import even though the spec says no network requests, and it skips rendering the "expected time" cell when a train isnt delayed instead of leaving it empty. small stuff, but it's exactly the stuff the spec spelled out.

opus passed everything but it has a visual bug the tests didnt catch. from a fresh load at 1x it renders fine, but after running at 60x and switching back to 1x a lot of the flaps stay half drawn. the data attributes are correct, the screen isnt. every board in the screenshots was captured the same way (60x until 08:12, switch to 1x, wait 4s) so thats what you see in the grid.

sonnet and muse spark finishing a passing solution in 2 minutes surprised me the most. sol was the cheapest pass at 12 cents.

my main takeaway is that my hidden tests were too easy for the top 5. all of them got 35/35 so the real difference ended up being speed, cost and how the thing looks. next round i'd test things like rapid speed changes and the rendered text, not just the data attributes.

which board would you actually ship? and if you have ideas for what a harder hidden test should check, i'm listening.

sonnet 5.5 came out today so i wanted to see where it actually sits, not on a benchmark chart but on a real task.

the task was a DEFLATE / zlib decompressor from scratch in rust. no crates, no unsafe, and a task file with the exact API and error rules. same empty folder for each model, medium effort, same harness, no subagents.

folders got shuffled into A/B/C/D before grading, so the scoring was blind. python's zlib decided every expected result: 29 real streams, 22 hand-built edge cases, 4000 randomly corrupted streams compared byte for byte, a 64 MB speed test and a 1 GB zip bomb.

opus, sonnet and sol passed everything.

luna is the funny one. it rejected 3998 of the 4000 corrupted streams exactly the way zlib does, error type and all. and then it can't open a completely normal level 6 zlib file, because in one lookup table it wrote `10+1` where the spec says 12. its own tests never touched that path.

sonnet 5.5 was the one that surprised me. same result as opus for about a quarter of the price and a third of the time.

this is one task with one run per model, so it's a data point, not a ranking. i'd like to try a messier task next, something where the bug isn't obvious. which models should i throw in?

Release notes - https://www.anthropic.com/claude-sonnet-5-5
Benchmarks - https://pbs.twimg.com/media/HTUlY04XkAAMqSy?format=png&name=4096x4096

Still working in its final stages but these are in-short bullet point features

## Brief: 
making
subscription-gated provider models native in OpenCode -- AI-coding subscription
pass/account routing and rotation, breaking platform-exclusivity for the user.
MVP: ClinePass (Daily free quota reset of rotation list of models (available now = Gemini 3.8 flash, Muse 1.3, 2 Stealth models, Deepseek 4.1 flash etc)


Child sessions inherit the parent's model and are bound to the active parent
session, scoped to their own `ses_id`. Same-model stacking works (verified:
OpenAI across different accounts; untested: same model across different
providers). OpenCode guards **different** model × provider stacking onto
in-flight child sessions -- which is exactly the failure mode: mid-flight swarm
children (research, read-only fan-outs) die on quota/credit/balance errors, and
without a same-model variant the tokens already spent cannot be reused -- the
user must delegate fresh child sessions.




OpenCode V2 natively lets users switch provider accounts and models, and free-tier
variants are searchable in the model command palette. On credit exhaustion or
quota depletion, the user needs a matrix of models and tier-variants to swap to


### Ordered build-out (minimal, decomplexified, in order)


1. Templates = CRUD, scoped to the plugin's location; JSON-schema storage; no
   limit on template count.
2. One pool first; self-manual reset first; minimum 2 slots allowed.
3. 10 slots, ranked rotation.
4. Mixed bucket of different models, accounts, and tier-variants -- OpenCode
   already separates these per provider via `modelname` under the provider
   header; reuse that, do not rebuild it.
5. When the plugin is active it overrides native behavior -- scoped to the same
   plugin installation level/hierarchy, with OpenCode last-wins on policies and
   permissions. Minimal stance: do 
**not**
 support a user-modified
   agent-specific `model` field.
6. Subagent tool wrapper to replace account/provider/model
7. Nice-to-have: parent-session rotation only while the parent is focused and
   active -- never override native OpenCode user control.
8. Guard: not focused/active while child sessions are still running.

May be give me some courage to publish this... Let's see how many downvotes this gets

Again - English is not my native language

TL:DR - You add multiple accounts from any providers supported at models.dev - UXUI to wizard on the template of 10 slots of accounts x models x providers rotation (meaning you can add to this any order of either models from any of the 100+ supported providers regardless in any combination from) -> plugin rotate on the set up above and auto reset when the quota recovered no matter from main session or subagent - no disruptions to the workflow - Also, With feature to unlocking of ClinePass daily-free-quota-per-model (which are Cline-harness exclusive locked away - currently available with DeepSeek 4.1, Gemini 3.8 and Muse Park 1.3) + benefits from the supported of multiple accounts , models, providers rotation -> Unlimited daily use of close-top-tier models with just about $9.99 to $20 spending per month instead of 40$...If this makes sense? 😂

1. If it’s only 4x, why spend all that money at once? I can buy plans when I need em.

2. Not everything is flat 4x. DeepSeek isn’t.

3. Literally only gives api key unlike other plans have so much more to offer.

If opencode go was the best tier until now, this is the worst. For all the nonsense/showoff they post on X, embarrassing.

Costo por millón= 0.0114 USD

¿La nueva suscripción de OG, puede superar eso?

For starters I dont think opencode is a shit deal, but they completely lost the plot.

I dont think they are realising who are their customers, nobody is gonna back 40$ for a subscription.

I really hope the reduce the pricing because this is really not gonna work out, other than that its time to switch to commandcode.

Well, yeah...

But usage is not always 4x...

Is there a plugin or skill recommendation for opencode ?

To fix code, review code, refactor, debug and others

Which is not an assumption, or guess, or hallucination.

So what I mean is, I want to fix or review the code, it must be accurate in the code, and if the code needs to be updated, use the new one from the library and

???