Entity trail

Qwen 3.5 397B

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-06-27 / reddit-researcherAlibaba Open-Sources Qwen-AgentWorld — a 'Language World Model' That Simulates 7 Agent EnvironmentsQwen released Qwen-AgentWorld-35B-A3B (35B total / 3B active MoE, 256K context, Apache 2.0) plus AgentWorldBench on June 24 — a language world model trained to simulate the environment an agent acts in, predicting next observations across MCP, Search, Terminal, SWE, Android, Web, and OS from a single model. A flagship 397B-A17B variant scores 58.71 on AgentWorldBench, edging out GPT-5.4 (58.25). For builders, this is a 'flight simulator for agents' — train and stress-test agent policies offline without standing up real environments, with vLLM/SGLang/Transformers deployment examples shipped.
2026-06-14 / hn-researcherRio de Janeiro's City Government Releases Rio 3.5 Open 397B Under MIT LicenseIplanRIO, the municipal IT company of Rio de Janeiro, released Rio 3.5 Open 397B on Hugging Face under an MIT license — a 397B-parameter sparse MoE (~17B active) post-trained from Qwen 3.5 397B, with a ~1.01M-token context window and multimodal input. It introduces SwiReasoning, a training-free inference framework that dynamically switches between explicit chain-of-thought and latent-space reasoning via entropy-based confidence signals for better token efficiency. A city government shipping a frontier-class open model is notable, though the benchmark claims still await independent audit.
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 / reddit-researcherr/LocalLLaMA Demands Qwen 3.6-397B-A17B Open Weights: 'Substantial Improvement Over 3.5 for Real World Tasks'A 210-upvote post calls for Alibaba to open-source the full Qwen 3.6-397B-A17B model, with the poster reporting it outperforms GLM-5.1 and Kimi-k2.5 on real-world tasks despite benchmarks not showing it. Qwen 3.6-Plus launched April 2 as API-only with 1M context and agentic coding capabilities, but the community wants the weights. The free OpenRouter preview reportedly ended April 3, increasing urgency for self-hosting options.
2026-04-05 / reddit-researcherGemma 4 31B Takes 3rd Place on FoodTruck Bench, Beating GLM 5, Qwen 3.5 397B, and All Claude SonnetsGoogle's Gemma 4 31B dense model placed 3rd on the FoodTruck Bench, outperforming several frontier models including GLM 5, Qwen 3.5 397B (a 15x larger model), and every Claude Sonnet variant (560 upvotes, 96 comments). The result reinforces Gemma 4's position as the most capable open model at its parameter class, with practitioners noting that a 31B model beating a 397B MoE is a striking efficiency milestone.
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-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.

Graph relationships

BENCHMARKED AGAINST
Gemma 4 31B
Gemma 4 31B beat Qwen 3.5 397B on FoodTruck Bench despite being 15x smaller
Source finding
USES
Rio 3.5 Open 397B
Rio 3.5 Open 397B was post-trained from Qwen 3.5 397B with added SwiReasoning framework.
Source finding
BENCHMARKED AGAINST
gpt-oss-120b
Qwen 3.5 397B outperforms GPT-OSS 120B on coding tasks.
Source finding
BENCHMARKED AGAINST
Gemma 4 31B -> Qwen 3.5 397B
Gemma 4 31B beat Qwen 3.5 397B on FoodTruck Bench despite being 15x smaller
Source finding
USES
Rio 3.5 Open 397B -> Qwen 3.5 397B
Rio 3.5 Open 397B was post-trained from Qwen 3.5 397B with added SwiReasoning framework.
Source finding

Source trail

Qwen (GitHub)Hugging Face / Hacker News arXiv Qwen AI HuggingFace Latent Space r/LocalLLaMA

Graph sources

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