YOU ARE VIEWING ONE ITEM FROM THE AICRIER FEED

Qwen3.5-35B-A3B powers flawless 27-step local agent chain

AICrier tracks AI developer news across Product Hunt, GitHub, Hacker News, YouTube, X, arXiv, and more. This page keeps the article you opened front and center while giving you a path into the live feed.

// WHAT AICRIER DOES

7+

TRACKED FEEDS

24/7

SCRAPED FEED

Short summaries, external links, screenshots, relevance scoring, tags, and featured picks for AI builders.

Qwen3.5-35B-A3B powers flawless 27-step local agent chain
OPEN LINK ↗
// 65d agoBENCHMARK RESULT

Qwen3.5-35B-A3B powers flawless 27-step local agent chain

A Reddit user says Qwen3.5-35B-A3B completed a 27-call local video workflow end to end, from Whisper transcription to subtitle burning, without a single error or manual intervention. The whole run stayed on a Lenovo P53 with llama.cpp and whisper.cpp, no cloud APIs, making it a strong real-world demo for a sparse MoE model on mid-range hardware.

// ANALYSIS

MoE is starting to look like a real advantage, not just an architecture footnote. The interesting part here is less that Qwen answered well and more that it held state across a long, messy tool chain and finished the job locally.

  • 27 sequential tool calls with verification is a better agent test than a single prompt-response benchmark.
  • The official model card says 35B total parameters and 3B activated, which is exactly the kind of sparsity that makes local deployment plausible.
  • Fully local execution with llama.cpp and whisper.cpp removes cloud latency, cost, and privacy friction.
  • Video-to-subtitles is a good stress test because it mixes planning, file I/O, transcription, and post-processing.
  • Ten minutes end to end is slow, but if it stays reliable, that's a tradeoff many local workflows will happily take.
// TAGS
qwen3.5-35b-a3bllmagentself-hostedopen-weightsautomationinferencebenchmark

DISCOVERED

65d ago

2026-03-25

PUBLISHED

65d ago

2026-03-25

RELEVANCE

9/ 10

AUTHOR

cride20