Entity trail

Qwen3.5-9B

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-05-16 / arxiv-researcherFrontierSmith: Synthesizing Open-Ended Coding Problems Yields +309 Elo on ALE-BenchAutomated system evolves open-ended problems from competitive programming seeds by changing goals, restricting outputs, and generalizing inputs, using a quantitative idea divergence metric to select problems eliciting genuinely diverse solver approaches. Training Qwen3.5-27B on synthesized data yields +12.12 on FrontierCS and +309.12 Elo on ALE-bench; Qwen3.5-9B gains +8.82 and +306.36 respectively. Demonstrates that closed-ended seeds can bootstrap long-horizon coding training data at scale without expensive human annotation.
2026-03-31 / vibe-coding-researcherCoPaw-Flash-9B: Alibaba's Official Agentic Fine-Tune of Qwen3.5-9B Matches Plus-Tier PerformanceAgentScope (Alibaba) released CoPaw-Flash-9B, a Qwen3.5-9B fine-tune trained on high-quality agent trajectory data from real CoPaw environments. It matches Qwen3.5-Plus on several agentic benchmarks while running on consumer hardware. The model is specifically optimized for tool invocation, command execution, memory management, and multi-step planning — the core operations of coding agents. Open weights on Hugging Face with 2B/4B/9B variants.
2026-03-24 / reddit-researcherOmniCoder v2 9B Released — Agentic Coding Model Trained on 425K Claude Opus TracesTesslate released OmniCoder-2-9B, a 9B-parameter agentic coding model fine-tuned on Qwen3.5-9B's hybrid architecture (Gated Delta Networks + attention) using 425K+ curated agentic coding trajectories from Claude Opus 4.6 traces. The model targets Claude Code, OpenCode, and Codex scaffolding patterns with error recovery (read-before-write) and LSP diagnostic response. Available in GGUF format for consumer hardware deployment.
2026-03-24 / vibe-coding-researcherOmniCoder-v2-9B: Upgraded Open-Source Coding Agent Model on Qwen3.5 BaseTesslate released OmniCoder-v2-9B, an upgrade to their Apache 2.0 coding agent model built on Qwen3.5-9B's hybrid architecture. Trained on 425K+ agentic coding trajectories with 262K native context (extensible to 1M+). Early community reports show improvements over v1 in multi-step reasoning and error recovery. At 9B parameters, it's positioned as the best open-source coding agent model runnable on consumer hardware.
2026-03-22 / reddit-researcherQwen3.5-9B + Claude Opus 4.6 Style Merge: Uncensored v2 GGUF for Low-VRAM UsersCommunity released Qwen3.5-9B-Claude-4.6-Opus-Uncensored-v2 as a Q4_K_M GGUF, merging Claude Opus 4.6's communication style into the smaller Qwen model for users without high-end GPUs who want large context windows. The 243-upvote post with 65 comments highlights the growing model merging ecosystem where practitioners combine commercial model personalities with open-weight base models via tensor decomposition. The demand signal is strong — the release was requested by multiple community members across Reddit and HuggingFace.
2026-03-21 / hn-researcherQwen3.5-9B on MacBook M5 Pro Scores 93.8% on HomeSec-Bench — 4.1pts Behind GPT-5.4SharpAI's HomeSec-Bench (96 tests across 15 security assistant workflow suites) shows Qwen3.5-9B running locally on a MacBook Pro M5 achieves 93.8% pass rate at 25 tokens/second, using only 13.8GB unified memory and zero API cost — versus GPT-5.4's 97.9%. The local model outperforms GPT-5.4-nano (92.7%) and nearly matches GPT-5.4-mini (95.8%). The larger Qwen3.5-35B-MoE delivers faster first-token response (435ms) than all OpenAI cloud models tested. The story got 160 HN points and 147 comments, confirming sustained practitioner interest in local AI matching cloud capability on specialized tasks.
2026-03-16 / reddit-researcherQwen3.5-9B Document Benchmark: Beats Frontier Models on Specific Tasks Across 9,000+ Real DocumentsA community-run open document AI benchmark covering 20 models and 9,000+ real-world documents added all four Qwen3.5 sizes (0.8B–9B), producing per-task breakdowns showing Qwen3.5-9B outperforming frontier models on specific document understanding subtasks while lagging on others (109 upvotes, r/LocalLLaMA). Unlike synthetic benchmarks, this uses real documents with ground-truth answers. The per-task breakdown reveals exactly where small specialized models beat large general models — actionable for builders choosing models by task.
2026-03-16 / reddit-researcherCommunity Wave: Qwen3.5 Models Distilled from Claude 4.6 Opus (2B–27B) — Runs 9+ Minutes Autonomously in Coding AgentsMultiple community contributors have distilled Claude 4.6 Opus reasoning chains into Qwen3.5 base models (2B, 4B, 9B, 27B) via Unsloth SFT. The 27B model fixes a Jinja template crash for the 'developer' role, preserves thinking mode, and runs autonomously for 9+ minutes in Claude Code and OpenCode — versus the base model that stalls mid-execution. Q4_K_M quantization uses ~16.5GB VRAM at 29–35 tok/s with full 262K context. The top post hit 1,136 upvotes and 168 comments on r/LocalLLaMA.
2026-03-15 / reddit-researcherCommunity Releases Qwen3.5-9B Distilled from Claude Opus 4.6 — Uncensored GGUF Available on HuggingFaceA LocalLLaMA member published 'Qwen3.5-9B-Claude-4.6-Opus-Uncensored-Distilled-GGUF' — a local model distilled from Claude Opus 4.6's outputs with safety filters stripped. At 573 upvotes and 95 comments it's today's top substantive LocalLLaMA post. This is a live demonstration of the distillation IP issue: Claude's capability being extracted into a redistributable, uncensored local model in direct violation of Anthropic's ToS.
2026-03-02 / reddit-researcherQwen 3.5 Small Series Natively Multimodal 262K Context Apache 2.0Alibaba releases Qwen3.5-0.8B to 9B natively multimodal 262K context Apache 2.0 with Gated DeltaNet architecture, 9B beats Qwen3-30B

Graph relationships

BENCHMARKED AGAINST
GPT-5.4
Qwen3.5-9B scores 93.8% versus GPT-5.4's 97.9% on HomeSec-Bench.
Source finding
USES
FrontierSmith
FrontierSmith trained Qwen3.5-9B on synthesized coding problems.
Source finding
MENTIONS
Claude Opus 4.6
Claude Opus 4.6 communication style merged into Qwen3.5-9B
Source finding
USES
FrontierSmith -> Qwen3.5-9B
FrontierSmith trained Qwen3.5-9B on synthesized coding problems.
Source finding
MENTIONS
Claude Opus 4.6 -> Qwen3.5-9B
Claude Opus 4.6 communication style merged into Qwen3.5-9B
Source finding

Source trail

arXiv Hugging Face / r/LocalLLaMA r/LocalLLaMA r/LocalLLaMA SharpAI r/LocalLLaMA HuggingFace / r/LocalLLaMA r/LocalLLaMA Reddit

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