7 results on this page
A Reddit user reports that Qwen3.8-27B autonomously retrieved a university class schedule through 80 tool calls using only credentials and a university name. In another test, it downloaded and analyzed a social-media video, installed Whisper for transcription, and enhanced video frames without human intervention.
A Wall Street evaluation of eight leading global agents ranked Qwen first overall for office use. The comparison also highlights cost as an increasingly important factor in Agent commercialization.
A community developer created Qwen3.8-23B-Mini-Me by strategically removing layers from Qwen3.8-27B, reducing the model to approximately 22.7B parameters without severe reasoning degradation. The model is reported to work well for coding, agentic tasks, and multi-turn chats, but it has not yet been benchmarked and struggles more with edge cases and underspecified prompts.
A TraceML benchmark using Qwen3-1.7B with LoRA found that equivalent effective batch sizes can have substantially different training times depending on physical batch size and accumulation steps. Across 100 optimizer updates, 4×1 was 17% faster than 1×4 on a T4 and 41% faster on an L4.
The post argues that Qwen3.8 intermediate tokens should not be interpreted as human-like reasoning. Citing research, it highlights that trace validity often does not correlate with answer correctness, and that models trained on corrupted or irrelevant traces can perform as well as or better than models trained on valid traces.
QwenMix-3.7 is an experimental merge of the Qwen3.8-27B and Qwen3.6-27B models. The creator reports that it passed a basic smoke test, but no meaningful evaluation has been performed yet.
Qwen Code v0.21.14 successfully completed one SWE-bench Verified task and one Terminal-Bench 2.0 task. Both benchmarks recorded 100% scores with no unresolved cases, execution errors, or infrastructure failures.