AI Briefing3.6-27B: Dense vs MoE Comparison
Key point
In a comparison of the new 3.6-27B models, the MoE model is rapidly catching up to the Dense model, particularly in coding performance.
Details
A performance comparison between the 27B Dense model and the 35B-A3B MoE model shows that the MoE model is rapidly catching up to the Dense model's performance in certain areas.
The key comparison results are as follows:
- Overall performance: The Dense model still maintains an edge on most benchmarks.
- Narrowing gap: The MoE model is closing the gap with the Dense model on 7 out of 10 benchmarks.
- Dramatic improvement in coding performance: The MoE model showed strong performance gains in coding. On the SWE-bench Multilingual benchmark, the Dense model's lead shrank significantly from +9.0 to +4.1.
- Notable exception: On Terminal-Bench 2.0, the Dense model widened its lead from +1.1 to +7.8, showing unrivaled performance.
In conclusion, the Dense model still holds a technical edge, but the MoE model is catching up very quickly. In particular, for use cases requiring large context windows within a 24GB VRAM environment, the MoE model has become an even more valuable choice.
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