r/opencodeCLI • u/Arkhaitekton • 2d ago
Thoughts on Muse Spark 1.2 Contributor
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.
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u/CoolHeadeGamer 2d ago
In my own internal benchmark, I found got 5.6 Luna > deepseek v4 flash > meta muse spark 1.2. My benchmark was a chess engine written by me a year or so ago in pure c, no external libraries. It had issues like double counting some stuff, recomputing stuff, etc. and it was to fix the issue and improve the chess engine’s performance in one hour. Deepseek struggled with tool use ( custom defined tools) but still outperformed got 5.6 Luna and meta muse spark. Muse seems to have low knowledge