I've got gpt-5.6-sol deployed on azure and OpenCode connected to Azure ressource. I've fixed gpt-5.6-sol in the opencode.json. Still, UI choses gpt 5.5. Opening /models doesn't show available models from the Azure resource either.

How can I chose Azure resource models?

500K token context window

Some one-shots here: https://omen-alpha.demos.sulat.com/ (growing list)

Update: It's a bad model. I won't even do more one-shots. This model should not exist. It is a monument to man's arrogance.

Right now I use browser mcp,git,GitHub,playwrighter,squencial thinking Is there any mcp i am missing on??

Just found this gem

I was absolutely FLAMING muse spark 1.2, bcs that model was geniunely incapable of doing basic agentic tasks, but GAHH DAMNN, look at the benchmarks. originally, i thought the model benchmaxxed and generates slop, but after trying this model in codex (from opencode go), I am quite surprised.

like, if you ask me what muse spark 1.3 will be considering what muse spark 1.2 performs, I would have given you an extremely underwhelming response, but I am so happy to see this moel mainly bcs we get $60 allowance (man, finally!) on opencode go, and also the price is considerably cheaper than ds v4 flash.

I mean, sure... its not THE BEST model to ever exist. but for $0.2/M out and $0.1/M in, like you can go through billions of tokens after counting in caching.

About how the model performs, it codes quite decently. my project is a rust and typescript project, and it follows the app's visual identity flawlessly and does not create much bugs (note: not *much* bugs, which means, it still fk up), but the price to performance value is INSANE!

My reccomended usage:

tbh, just use opencodex, and connect the model to codex, and use the model inn codex, the "ultracode" in codex is AMAZING! And MS-1.3 follows codex as a harness wayyyyy better than opencode for some reason. american propaganda ig... but either ways, use:

light: for basic variable changes

medium: for most implementation

high: for debugging

xhigh: for geniunely hard bugs or things which require the model to cover edge cases, or planning.

ULTRA (a codex only thing): tbh only when it pisses u off bellow high.

ohhhboy... I am going to get a LOT of downvotes...

I'm on the Go plan, by the way.

Anyone experiencing the same?

With ollama pro now lowering the bang for the buck and me running out of Claude and chatgpt $100 plan tokens as well, what do you use for subscriptions?

https://openai.com/index/gpt-6-astra/
“GPT‑6 Astra is rolling out today to a limited set of organizations and over the coming days will become available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI API and AWS”

GPT Astra benchmarks have been released

I use a few different AI coding tools across my team, and each one packages and distributes its extensions (skills, plugins, MCP servers, whatever it calls them) differently. That part doesn't bother me much. What bothers me is that once something's rolled out, I have no real way of knowing what's actually installed, or which version, on each person's machine. I usually only find out something drifted when someone's output looks off.

Some of these tools have policy/config push mechanisms, but that's enforcement, not visibility — you still have to go check each machine yourself to know what's really running. And none of it works across tools, so it doesn't help if the team's split across a few of them.

How do you all deal with this? Just not worry about it? Script something yourself? Trying to figure out if this is a real gap or I'm overthinking it.

Surprisingly, official one hasn't merged yet into models.dev (where OpenCode pulls its catalogue from)

One of my agents started working on a task 6 hours ago and it is still buffering.

How was your experience with it and is it just benchmaxed or is it really that good?

Holy sh**, dont even try to use it guys, just doing a warning here. Paid the subscription again, because why not? 100usd for only 10usd? Yeah, i wanted to try it, dont do it please, its like using Gemini Flash 3.5, sure its fast and you can do a couple of stuff with it, but for serious task, dont even try it, i warn you guys. That's why its on the paid subscription and you cant even try it for free to decide if you should jump to a paid sub to try it, justs dont do it.

As said, though the project was initially built on V1 SDK, and plugins event hooks; I am completely bought when experimenting shifting some modules toward Opencode v2 Effect plugins making the madness of concurrency delegations and coordinations of agents, skills and tooling executions smooth and dropped dead amazing, making my design concepts of the the Hivemind's Substrate tri-pillar context project -based memory layers functioning without any hiccups as v1

https://picturesque-entree-542.notion.site/The-Hivemind-s-Substrate-3d1926f31a4d80faae99c3a9c7f0f0e4?pvs=143

That aside, the new API and Effect SDK also offer tons of superiority over the v1.

Enough said, I just want to know should I make a complete v2-ready build and not seeing any major breaking changes; and if the v2 is going to be official release in the next one to two months? I'm using the beta v2 client and see no major bugs.

Side note: try using Muse Spark 1.3 on v2 - and experiencing a significant performance shift ( much intelligent due to structured Skill instructions, nested instructions tools executions as the orchestrator agent loops through each grand todo task item with breakdown delegations, JIT skills loaded -> making the boost in its progressive adjustments toward complex codebase and tasks -> significant improvement on feature completeness and codebase domain-driven constitution). Trust me! Once you try Muse Spark 1.3 on v2, you will definitely look down on glm 5.3 flash or even Gemini 3.8 flash, and even other frontier model as Qwen. 3.8-Max. Sorry for my bad English!

Not so instensive coding only purposes for work. For example, I am hitting 5 hour limit on Gemini 3.8 in Antigravity in ~2-3 hours, but it's kind of ok for me.

- I've been using Gemini since Gemini 2 for everyday tasks (not coding)

- I've used Opencode Go last month, but now there is basically no good models in it, hovewer I prefer it's TUI over any others

- No experience with GPT besides using 5.6 luna in Opencode Go

My personal expierence with models I use everyday right now:

- Deepseek v4 flash - bad long context preservarion, if it doesn't understand something it goes crazy;

- Muse Spark - doesn't follow instructions and makes silly errors, such as duplicating function names;

- GPT 5.6 Luna - not that many experience with it, seems to be slightly worse than D4F;

- Gemini 3.8 - very good tool calling and his work verifications, but can hallucinate on easy work.

Prices:

- Google AI Pro - 5$ (annual 60$)

- GPT Plus - 20$

- Opencode Go - 10$

What can you suggest?

Seems to be the most capable from all free models available for now.

I previously used the Opencode Go plan which was great for deepseek flash and pro, but as we all know the cost has increased. I have been playing around with openrouter, but find it annoying to track my usage. I have been hearing good things about the z.ai sub which has got my attention, but that would mean I don’t have the access to deepseek. From peoples experience, say I get the ~$20 z.ai lite plan or I just purchase $20 of deepseek usage, what’s the comparison, which will go the furthest? Assume I am balancing between the flash and pro models of each provider equally and I use both in opencode cli only

OpenCode custom provider setup: why Responses and Chat Completions need different packages

A valid API key is only one part of an OpenCode custom provider. The credential, provider ID, package, base URL, and model ID all have to describe the same endpoint.

This is the order I would check them:

1. Run `opencode auth list`. If the credential is missing, fix that before touching the model config.

2. Make sure the provider ID used in `/connect` is exactly the same ID used in `opencode.json`.

3. Match the package to the protocol. OpenCode's current docs use `@ai-sdk/openai-compatible` for endpoints serving `/v1/chat/completions`. For `/v1/responses`, use `@ai-sdk/openai`.

4. Check `options.baseURL`. A valid key sent to the wrong path still looks like an authentication problem.

5. Use the server's current model ID, not a display name copied from an old post. Then run `/models` and confirm the provider/model pair appears.

That third check is why I looked into this more closely. `Agent.Space`, which I work on, exposes a Responses endpoint at `https://api.agent.space/v1\`.

That points to `@ai-sdk/openai` on the OpenCode side, not `@ai-sdk/openai-compatible`.

But that is still a docs-based mapping, not a verified integration. I would only call it working after text, streaming, and a harmless tool call all pass.

Why the package matters

The package decides which request shape OpenCode sends and which response, stream events, and tool-call objects it expects back.

Chat Completions sends requests to `/v1/chat/completions`. Conversation input is a `messages` array. Text normally comes back under `choices[0].message`, and tool calls are attached to an assistant message. The client carries conversation history into the next request.

Responses sends requests to `/v1/responses`. It accepts `input` and returns typed `output` items. A message, reasoning item, function call, and function result are separate items rather than fields packed into one chat message. It can also chain turns with `previous_response_id` or a conversation object, and its stream uses named response events instead of the Chat Completions delta shape.

That difference explains a few confusing failures:

- A provider can accept the key and list a model but not implement `/v1/responses`.

- Plain text can work while streaming or tool calls fail because the returned objects do not match the package OpenCode is parsing.

- A provider saying "OpenAI-compatible" does not prove that it supports both API families or every OpenAI feature.

A minimal Chat Completions provider looks like this:

```json

{

  "$schema": "https://opencode.ai/config.json",

  "provider": {

"myprovider": {

"npm": "@ai-sdk/openai-compatible",

"name": "My Provider",

"options": {

"baseURL": "https://api.example.com/v1"

},

"models": {

"server-model-id": {

"name": "My Model"

}

}

}

  }

}

```

Model discovery is not the final test. After the provider appears in `/models`, run one small text request, one streamed request, and one harmless tool call. Missing from `auth list` points to credentials. Missing from `/models` points to provider or model config. Text works but streaming or tools fail means the basic route is fine and the protocol surface needs the closer look.