While the dual-thinking mode wasn't introduced by Qwen, they helped popularize it with a simple mode switch by including either /no_think or /think in the prompt. To the surprise of some, Qwen is abandoning the concept after talking to the community and released an update to the big (now non-reasoning) MoE model. The scores are impressive (beating the Kimi K2 model we recently hyped as a major release), including 41.8 on ARC-AGI.
It turns out training a hybrid reasoning model is more challenging technically than it is worth relative to the upside of downstream serving (where training two separate models, one thinker one not, is much easier).
The best part of this release is that it has come with multiple reports of strong vibe tests. Historically, Qwen has been known to be among the benchmark-maximizing labs - there are a few papers that have come out recently highlighting signs of data contamination in Qwen base models - but the Qwen models are improving in the robustness of normal testing. We've written multiple times on Interconnects about how labs will first shoot for strong benchmarks to get on the map, and then move to models that are more precisely those that people want to use. Quoting from our Qwen 3 post:
"We'll start to see if Qwen has taste/vibes. They have the benchmarks complete, and now we'll see how they compare to the likes of R1, o3, and Gemini 2.5 Pro for staying power at the frontier."
Qwen team members mentioned a new flagship thinking model is on the way and joked about coding models coming soon. The evaluation scores are below relative to other models without a <think> section. Again, as we mentioned in our Kimi K2 post, these models are trained extensively with reinforcement learning still, but the goals of the model are more constrained. These instruct, non-thinking, models are best for when the user wants a fast time-to-first token or other automation tasks.
The evaluation summary is here:
