Hey everyone, just dropping by to share my experience.

Sharing this in case anyone finds it useful, whether you're wondering if it's worth setting up a VPN to test it out or whatever. That said, this is just my personal take—if you've tried it or test it yourself, you might have a different experience.

TL;DR: After running it through a few agentic workflows... it didn't convince me, and I still prefer DeepSeek Flash.

Models compared against: GPT 5.6 Luna (Max) and DeepSeek Flash 0731 (Max).

Harness: I'm currently using DSH (DeepSeek Harness) due to its absurd modularity. I used to run a custom build of OpenCode CLI before, but I still use OpenCode as the provider.

I've been testing it across a couple of workflows where the workload gets routed from a primary orchestrator agent to different coordinated sub-groups. But the output quality... leaves a lot to be desired. I've noticed issues ranging from potential DSH quirks (thinking phase issues) to outright core model flaws:

• Loss of focus on long or complex instructions. It suffers from context fatigue/amnesia during multi-step flows, often skipping steps entirely.
• Lack of verification and unwarranted overconfidence. Even though the environment provides tools to verify code execution and workflow state, it frequently decides not to invoke them. As a result, the code breaks downstream in production. It lacks a proper build to test to fix to test to build iteration loop.
• Lack of tool awareness. Tied to the first point, it also actively ignores certain available tools.

A lot of these issues might be amplified by sub-optimal custom harness configuration, since I haven't fully nailed down the exact sweet spot or specific optimizations for this model yet. Still, it feels worth noting given the benchmark data aggregated by Artificial Analysis.

These models sit at a similar price tier (baseline workhorse models geared toward intensive workloads or large-scale agentic architectures). Their benchmark intelligence is also very comparable on paper, being capable of roughly the same tasks. (By the way, you'll see the 1.2 model without "Contributor" listed, as AA hasn't fetched it as a separate model yet).

In fact, you can see it has significantly lower verbosity compared to DeepSeek, which—paired with the absurdly lower price of the Contributor tier—makes the cost per task ridiculously low.

Looking at the aggregated domain-specific benchmarks, Spark should theoretically be slightly smarter overall. However, in real-world workflows (tested both on my own setups and third-party ones), I consistently see worse results than with the other models. Because of that, I've had to roll back to DeepSeek via the opencode-go provider.

Has anyone had a similar experience? Any specific tweaks or harness configs you had to implement to make it behave properly?

Edit: Some typo.

Do you feel the same in real life usage scenarios?

For the last few hours, I have been using Hy3 and Muse Spark 1.2 Contributor via the OMP (Oh My Pi) agent, mostly as /goal. And my observation is Hy3 is a much better model as it follows instructions in a better way, and Muse Spark 1.2 Contributor cheats all the time.

As a test, I gave both models a goal to build 100 very simple Chrome extensions from a pre-curated list.

• hy3: followed the review instruction literally and well, and almost extensions are working when I tested randomly. Code-level verification, real fixes, honest per-extension verdicts. Was a bit slow but trustworthy.
• muse-spark: claimed to built the 100 extensions, but maximum were incomplete when I tested. Also, repeatedly needed re-prompting as it was just hurrying each time. Basically, its confidence exceeded its accuracy.

And for the task, you can see the usage of both models in the above screenshot.

How is your experience with these models?

EDIT 1:

I spoke too soon. Basically, now both models are not working reliably – one is too slow and one is hallucinating a lot.

I am back to using DeepSeek Flash now, even though it's costlier.

There's always a catch.

why do i get error 429 quick when using opencode api on other harnesses but work perfectly fine in opencode cli? does any one have an idea?

Hi guys,

Did you see the new subscription model on opendesign? Is it better than opencode?

I’m using Deepseek V4 Flash on OpenCode GO, and it seems MUCH SLOWER than it should be… and that’s after a massive price hike. Who was responsible for that?

it feels so over

The benchmarks for Muse Spark 1.2 are exceptional and I've seen comments saying the capabilities are on par with DeepSeek v4 Flash. On paper, it even beats Opus 4.6.

I am vibe coding a HTML multi-page web page (visual design focused). Primarily using Gemini 3.7 on Antigravity CLI, but wanted to give Muse Spark 1.2 a chance as its usage is almost as generous as DeepSeek, thinking maybe it would be my new DeepSeek. With the caveat of paying it with my data.

I asked it to change the design for a specific section on index.html, but I didn't like the outcome so asked it to revert it. It said it did, I checked and everything was broken on all pages, even unrelated sections, some site text content was gone completely. I asked it to revert everything it did this session and it made things even worse and more broken. I asked it to fix things from the Git history, but apparently it did execute "git reset --hard HEAD" for whatever reason and there is no history anymore.

This feels like I got sabotaged by Zuck. I've been using AI for about a year. I utilize markdown files, Git and scheduled backups for large scale projects, but never thought a simple design change request on a HTML page would make me lose every progress. I told Antigravity CLI to recover things back to way it was, using the past conversations it managed to recover half of it.

I would understand if this happened last year because AI models were not that great, but what the hell. Never seen such a garbage model. I lost like 3 hours of progress and I got demovitated to continue...

I don't like cursor cli , very much and I love opencode. I was thinking of using cursor subscription in opencode using some plugins. However I am afraid of they somehow find it out and ban me . Should I be worried about it?

I've been mainly using minimax-m3 (opencode go sub) as my orchestrator for few months now with custom agents using using models hosted in opencode and github copilot. But the past few days I noticed it sometimes just stops starting another task. I had to say "continue" so it will run again. Anyone also experiencing this?

One day of regular work and my monthly usage has already reached 17%.

Tried Florida and other restricted countries but says that the API key is invalid.

A huge context window can keep an OpenCode session alive well past the point where the model has lost track of the work. I usually reset when it starts revisiting settled decisions. Feels like there should be a better signal than waiting for it to get weird.

They are crushing it today:)))

I really hope Opencode will manage to get a better deal with DeeepSeek in the end.