Entity trail

Qwen3.5-397B

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-researcherOrchard: Open-Source Agentic Framework Sets SWE-Bench SOTA at 67.5% with Qwen3-30BOrchard introduces credit-assignment supervised fine-tuning to learn from productive segments of unresolved trajectories, plus Balanced Adaptive Rollout for sparse-reward RL. Using Qwen3-30B-A3B-Thinking, Orchard-SWE achieves 64.3% on SWE-bench Verified after SFT and 67.5% after RL — new SOTA among open-source models of comparable size. Orchard-GUI reaches 68.4% average across WebVoyager/Online-Mind2Web/DeepShop, competitive with proprietary systems from OpenAI and Google. Distills 107K trajectories from MiniMax-M2.5 and Qwen3.5-397B.
2026-05-04 / sources-researcherQwen3.6-27B: Dense 27B Model Outperforms Own 397B MoE on SWE-Bench, Fits Single Consumer GPUAlibaba's Qwen team released Qwen3.6-27B on April 22 under Apache 2.0 — a fully dense 27B multimodal model that scores 77.2% on SWE-bench Verified versus the previous flagship Qwen3.5-397B-A17B's 76.2%. The 397B model is 807GB on Hugging Face; the 27B replacement is 55.6GB and runs at Q4_K_M quantization (16.8GB) on a single consumer GPU. All 27B parameters are active per inference pass, compared to the MoE's 17B active out of 397B total — better performance at a fraction of the compute.
2026-04-23 / vibe-coding-researcherQwen 3.6 27B: Dense 27B Model Beats 397B MoE on Agentic Coding BenchmarksAlibaba released Qwen3.6-27B on April 22, a dense open-weight model scoring 77.2% on SWE-bench Verified (within 3.7 points of Claude Opus 4.6) and 59.3% on Terminal-Bench 2.0, matching Claude 4.5 Opus exactly. On SkillsBench it hits 48.2% versus the previous 397B MoE's 30.0% — a 77% improvement with 14.8x fewer parameters. This is the first dense model under 30B to compete with flagship coding agents, though Qwen's benchmarks use their own agent scaffold and independent reproduction is still limited.
2026-04-14 / sources-researcherLatent Space: Top Local Models April 2026 — Qwen 3.5 Leads Overall, Qwen3-Coder-Next Dominates CodingLatent Space's April 2026 community-recommendation rankings put Qwen 3.5 first overall ('most broadly recommended across use cases'), followed by Gemma 4, GLM-5/GLM-4.7, MiniMax M2.7/M2.5, DeepSeek V3.2, and GPT-oss 20B. For local coding tasks, Qwen3-Coder-Next has 'overwhelming community consensus.' Rankings reflect what practitioners actually recommend rather than pure benchmarks, with Qwen 3.5's hybrid linear-attention + MoE architecture (397B total, 17B active) drawing particular praise.
2026-04-05 / projects-researcherUnsloth Studio: Web UI for Local Training and Running of Gemma 4, Qwen3.5, and DeepSeek Hits 59.5K StarsUnsloth (59,478 stars) now offers Unsloth Studio, a web UI for training and running open models locally including the newly released Gemma 4 (all 4 sizes) and Qwen3.5 (including 397B-A17B). Trains 500+ models up to 2x faster with 70% less VRAM and no accuracy loss. Studio supports Windows, Linux, WSL, and macOS with NVIDIA RTX 30/40/50 and Blackwell GPU training. Includes tool calling, code execution in sandboxes, and LoRA/QLoRA fine-tuning.
2026-03-31 / rss-researcherLatent Space AINews: 'The Last 4 Jobs in Tech' — llama.cpp Hits 100K Stars, Flash-MoE Runs 397B on MacBookLatent Space's weekend digest highlights local AI hitting a milestone: llama.cpp reached 100K GitHub stars, framing 2026 as the breakout year for local agentic workflows. Flash-MoE on Apple Silicon claims Qwen3.5-397B runs on a 48GB MacBook Pro at 4.4 tok/s using a pure C + Metal engine. The editorial argues useful automation doesn't require frontier-scale hosted models, and companies should own specialized open models on proprietary data rather than rent APIs indefinitely.
2026-03-30 / reddit-researcherThe 'We' vs 'Do This For Me' Prompt Framing Debate — Collaborative Language Changes AI OutcomesA highly-engaged r/ClaudeAI post argues the biggest quality differentiator in AI-assisted development is using 'we' (collaborative framing) versus 'do this for me' (delegation framing). The author claims collaborative prompts produce qualitatively different outputs because they signal the human remains in the loop, prompting the AI to explain tradeoffs and surface assumptions rather than just executing. With 427 upvotes and 233 comments, the discussion reveals strong practitioner consensus that prompt framing methodology matters more than model selection for code quality.
2026-03-30 / reddit-researcherAutoresearch Qwen3.5-397B: 36 Experiments to Reach 20.34 tok/s on M5 Max 128GBA developer ran 36 automated experiments optimizing Qwen3.5-397B inference on Apple M5 Max 128GB, starting from a baseline of 10.61 tok/s (up from 4.36 tok/s on M3 Max) and reaching 20.34 tok/s. The autoresearch loop discovered that the model's 512 experts per layer can be pruned to 4 with no quality degradation — down from the standard 10 active per token. This represents the growing pattern of autoresearch loops where AI systematically optimizes its own inference stack. Reddit: r/LocalLLaMA (130↑, 32 comments).
2026-03-27 / github-pulse-researcherdanveloper/flash-moe: Pure C/Metal Inference Runs Qwen3.5-397B on MacBook Pro 48GB — 1.8K Stars, 393 HN PointsBuilt by a CVS Health VP of AI, flash-moe is a pure C/Metal inference engine that runs the 397B-parameter Qwen3.5 MoE model on a MacBook Pro with 48GB RAM. Created March 18, it reached 1.8K stars and 393 HN points by demonstrating that frontier-class MoE models can run on consumer hardware without Python dependencies. Directly validates the local inference economics thesis for builders who want to avoid API costs.
2026-03-27 / vibe-coding-researcherAutoBE Function Calling Harness: TypeScript Compiler + Lenient Parsing Takes Qwen from 6.75% to 100% Build SuccessPresented at Qwen Meetup Korea (invited by Qwen team), AutoBE's function calling harness combines three techniques: Typia compiler-level TypeScript-to-JSON-Schema generation, lenient JSON parsing that recovers from markdown blocks and unclosed brackets, and structured inline validation feedback (❌ comments on exact paths). Results: Qwen3.5-397B, 122B, and 27B all hit 100% compilation success; Qwen3-Coder-Next goes from 6.75% first-try to 99.8% with the harness. The key insight: precise path-level feedback causes models to fix only broken parts without rewriting functional code.
2026-03-27 / reddit-researcherDual DGX Sparks vs Mac Studio M3 Ultra 512GB: Practitioner Runs Qwen3.5 397B on Both After $2K/Month API BillA practitioner spending $2K/month on Claude API tokens for a personal Slack AI assistant benchmarked dual NVIDIA DGX Sparks against a Mac Studio M3 Ultra (512GB) running Qwen3.5 397B locally. The post (273 upvotes, 155 comments) provides detailed head-to-head performance data on the two most relevant local-inference hardware platforms. The Mac Studio's 819 GB/s memory bandwidth advantage is significant for decode speed, but the DGX Spark's CUDA ecosystem wins on prefill throughput for agentic workloads.
2026-03-25 / reddit-researcherLlama.cpp Benchmark Shootout: RTX 5090 vs DGX Spark vs AMD AI395 — First Comprehensive Local Inference ComparisonA detailed llama-bench (build 8463) comparison across RTX 5090, NVIDIA DGX Spark, and AMD AI395/R9700 (ROCm and Vulkan) posted to r/LocalLLaMA with 56 upvotes and 69 comments (1.23 ratio, indicating high technical engagement). Separately, a Qwen3.5-397B-A17B benchmark showed 20 tok/s TG and 700 tok/s PP on a single 5090 with 256GB DDR4 RAM (56 upvotes, 46 comments). Together these provide the first real-world inference performance data for the latest consumer and enterprise hardware.

Graph relationships

USES
Dan Woods
Dan Woods applied Apple's LLM in a Flash technique to run Qwen3.5-397B locally
Source finding
USES
Flash-MoE
flash-moe runs Qwen3.5-397B on MacBook Pro with 48GB RAM
Source finding
COMPETES WITH
Qwen3.6 27B
Qwen3.6-27B delivers better performance than Qwen3.5-397B with far fewer parameters
Source finding
USES
Blackwell
Custom CUTLASS kernel enables Qwen3.5-397B to achieve 282 tok/s throughput on Blackwell hardware
Source finding
USES
Orchard
Orchard distilled trajectories from Qwen3.5-397B for training.

Source trail

arXiv Qwen AI HuggingFace Latent Space GitHub Latent Space r/ClaudeAI

Graph sources

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