Qwen3.8 27B and Muse Glimmer Benchmarks: Accuracy, Token Efficiency and Memory Use

Muse Glimmer was released only a few days ago as I write this, yet it has already been largely overshadowed by Qwen3.8 27B.

The difference in accuracy between the two models is striking, especially given their similar parameter counts and dense architectures. Muse Glimmer struggles in particular with long-horizon agentic coding, while Qwen3.8 27B performs remarkably well, even across different agents and with thinking effort set to medium.

Still, as we saw in a previous article, Muse Glimmer may retain a few important advantages:

• It has a relatively short maximum context length. That is a limitation in itself, but it also suggests that the model was not trained to produce the extremely long reasoning traces Qwen3.8 can generate.

• Its KV-cache memory consumption is very low: it uses roughly 4 times less memory than Qwen3.8.

Taken together, these characteristics make Muse Glimmer considerably more memory-efficient than Qwen3.8 27B, despite being slightly larger.

In this article, I first present my own accuracy results for Muse Glimmer and Qwen3.8 27B across a broad range of tasks, with both models evaluated at xhigh thinking effort. The benchmarks cover coding, world knowledge, difficult mathematics, instruction following, and more.

I then look beyond raw accuracy to examine token efficiency: how many tokens each model typically needs to solve a problem, and how much memory each consumes once the length of its reasoning traces is taken into account. This gives a more complete picture of the trade-off between the two models than parameter count or benchmark accuracy alone.

Acknowledgments

Verda provided the RTX Pro 6000s to run the experiments described in this article.

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Note: I don’t receive any form of compensation from Verda, or any usage information, related to this coupon.

Note: For this article, I evaluated Qwen3.8 only in xhigh thinking mode. A follow-up will compare its low, medium, xhigh, and disabled thinking modes. I also plan to publish a separate article focused entirely on agentic coding with Qwen3.8, as there is a great deal to explore there. Results for quantized versions of both Muse Glimmer and Qwen3.8 will come later, as will a comparison of their inference speeds.

My results confirm that Qwen3.8 27B at xhigh thinking effort is a substantial improvement over Qwen3.6 27B: