The Qwen-AgentWorld-35B-A3B model shows strong performance in coding tasks, with a 65.63% score on Software Writing Evaluation and 65.92% overall benchmark. It outperforms Qwen3.5-35B-A3B and rivals larger models in agent-based tasks, with a first impression noting superior accuracy in long-term agent workflows.
media
r/LocalLLaMA
·
56d ago
·
open_models
Qwen-AgentWorld-35B-A3B for Coding?
from English
Related articles
media
r/LocalLLaMA
·
47d ago
·
8 views
Local benchmarks with a RTX 3090 - Qwen3.6 27b vs Ornith
A user compared Qwen3.6 27b, Gemma4 26B A4B QAT, and Ornith1.0 35B MoE using the inspect-ai framework on an RTX 3090 to evaluate local model performance. The testing revealed mixed results across general knowledge, grounding, and coding benchmarks, with Qwen3.6 generally leading in scores while Ornith showed strengths in specific areas like DROP.
media
r/LocalLLaMA
·
51d ago
·
3 views
GLM 5.2 Q1_S vs Qwen 27B Q8: A Local LLM Comparison
An amateur comparison on consumer hardware demonstrates that the heavily quantized GLM-5.2 (Q1_S) outperforms the higher-bit Qwen 3.6 27B (Q8) in a complex coding task, despite significantly slower inference speeds.
media
r/LocalLLaMA
·
52d ago
·
1 view
Qwen3.6 27B local vs Opus 4.8, voxel engine in raw C with zero frameworks
A comparison experiment pitted Claude Code on Opus 4.8 against a locally running Qwen3.6 27B model to build a voxel world engine in plain C without any external frameworks or libraries.
arxiv
arXiv cs.AI
·
56d ago
·
5 views
LLMs Benchmarked for Web Vulnerability Detection
A study evaluates six LLMs on detecting real-world web vulnerabilities in WordPress plugins, finding detection rates vary by model and prompt design. Claude Opus 4.6 achieved the highest detection rate at 63%, while Qwen 3.5 only reached 35%, and no model consistently identified all baseline vulnerabilities across iterations.
arxiv
arXiv cs.AI
·
56d ago
·
4 views
CORTIS: Text-Only Adaptation of Spoken Language Models
CORTIS enables task-oriented voice agents to generate structured speech outputs by fine-tuning spoken language models using only text-form task supervision. It outperforms ASR-LLM cascades under acoustic degradation, especially in preserving high-level task semantics, without requiring paired speech-target annotations during training.