User logs Qwen3.5-4B failure modes across 10 agent harnesses
10/22/2026 — 10/23, 03:21·1 sources (all)·1 reports (all)
Key takeaways
- A user on the LocalLLaMA subreddit ran Qwen3.5-4B across 10 agent harnesses on five small tasks, one run each, using llama-server with a 64K…
Background & analysis
On October 22, 2026, a user post on Reddit's LocalLLaMA board described a test of Qwen3.5-4B across 10 agent harnesses. The setup paired the model with 5 small tasks, with each combination run once, for a total of 50 runs. The author said the scores mattered less than how the model failed, and that the goal was to see where a 4B model actually breaks down in local agent workflows.
The environment used Qwen3.5-4B Q4_K_M on a single llama-server with 64K context, 1 slot, f16 KV and the --jinja flag, running on an M5 MacBook Pro with 32GB of memory. Each agent ran headless with auto-approve enabled, received a fresh copy of the task files on every run, and had pass/fail decided by a script. The agents named in the post include Pi 1.3.0, goose 1.55.0, Crush 0.98.1 and Cline 3.0.73; the excerpt does not show the full list.
The public record currently stops at this single post. It lays out the test configuration and scale, but the summary does not disclose per-agent scores or the specific failure patterns observed, and no follow-up update or independent reproduction from another source has appeared.
AI-generated from 1 reports · updated 9 hours ago
Latest turnA Reddit user ran Qwen3.5-4B across 10 agent harnesses on five small tasks, one run each, using llama-server with 64K context on a 32GB M5 MacBook Pro. The user says the failure modes were more revealing than the scores, showing where a 4B model breaks in local agent workflows.
Related AI tools
- Heat index
- 42
- All sources
- 1
- All reports
- 1
- First seen
- 1 day ago
Reports on this story headlines open the original
A Reddit user ran Qwen3.5-4B across 10 agent harnesses on five small tasks, one run each, using llama-server with 64K context on a 32GB M5 MacBook Pro. The user says the failure modes were more revealing than the scores, showing where a 4B model breaks in local agent workflows.
Reddit · LocalLLaMAAI score 57
Other stories people are talking about
- Heat index 30Developer benchmarks Qwen3.8-Flash-Next vs Opus 5.5 on Rust coding1 sources
- Heat index 80Price list compares per-million-token costs across Qwen API models1 sources
- Heat index 152SurgeQwen launches N1 Pro AI glasses and clip-on AI earbuds2 sources
- Heat index 80Reddit User Charts Scores vs. Size for 11 Open-Weight Models1 sources
- Heat index 66capy-flash v0.7.1 runs a 125B MoE on 64GB of RAM1 sources
How is heat calculated?About the methodHide
Heat counts how many independent sources covered a story in the last 48 hours: one source counts once no matter how many posts it published, decaying with a 24-hour half-life. What ranks first is what many people are talking about.
This page aggregates public feeds. Headlines and summaries are machine-organized and remain the property of the original authors; verify important facts at the source. If you believe a headline or summary infringes your rights, email the contact address in the footer with the page URL and basis of your claim, and we will remove or replace it after review.
- Surge
- Discussion rising fast
- New
- First report within 6 hours
- Rising
- Still gathering discussion