Entity trail

Qwen3.6-35B

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-27 / reddit-researcherA 35B-A3B Tool-Calling Benchmark Found Ornith 1.5 and Tiel-Coder Tied, and That Tiel Is an Imatrix Quant of Ornith, Not a FinetuneA r/LocalLLaMA user ran tool-eval-bench across Qwen3.6-35B-A3B and its variants on a cluster of 32GB V100s, finding Ornith 1.5 and Tiel-Coder tied at the top, well above Qwen3.6-27B and close to 3.8-27B, with KAT-Coder slightly ahead of base 35B-A3B and Ornith-1.5-Heretic disappointing. The top comment corrects the framing: Tiel is an Ornith imatrix quant with a different chat template, not a separate finetune, though its author argues the gap shows up on SWE-Bench Live. When asked, the author also ran Qwen-AgentWorld-35B-A3B and got 121.0 average across five runs, about ten points behind base.
2026-08-26 / reddit-researcherThomson Reuters Released Thomson-1.0-Small, a Law and Tax Model Built on Qwen3.6-35B-A3BThomson Reuters published Thomson-1.0-Small on Hugging Face, a domain model for legal and tax work fine-tuned from Qwen3.6-35B-A3B, drawing 163 upvotes on r/LocalLLaMA. The name misleads on size, as several commenters noted expecting a 1B model. The most substantive pushback in the thread argues domain models should be trained to understand how the field works and then aggressively retrieve, rather than baking case and code knowledge into weights that go stale the moment the law changes.
2026-08-26 / reddit-researcherAn Open-Source Kernel Stack Got Qwen3.6-35B-A3B to 78,498 Output Tokens Per Second on 8 AMD MI350X, 2.16x vLLMNetraRuntime published kernel-level optimizations for Qwen3.6-35B-A3B on AMD MI350X, reporting 11,161 output tok/s on one GPU and 78,498 tok/s mean (81,331 peak) across eight, which they measure at 2.16x vLLM throughput on the same 8-GPU benchmark. The kernels are open source at github.com/NetraRuntime/netra-kernel with a write-up of the method. Their closing note is the useful part for anyone attempting the same: once the kernels got fast enough, the bottleneck moved into scheduling, graph coverage, recurrent state, routing and HTTP serialization.
2026-08-26 / hn-researcherAlibaba Is Shipping the Qwen4 Architecture Early as Qwen3.8-Flash-Next, 125B Total With 6B Active and No Benchmarks YetQwen3.8-Flash-Next, an open-weight multimodal mixture-of-experts model, is scheduled to go public on ModelScope at 23:00 Beijing time on August 26, 2026. It is roughly 125B parameters with a separate N-gram embedding table of about 51B, activating only 6B per token, and Alibaba frames it as a technology preview of the architecture that will power Qwen4. Alibaba claims training cost around one ninth of Qwen3.7-Plus, but has published no side-by-side scores against its own line or Western rivals, so treat the parameter and efficiency figures as unverified until the weights land.
2026-08-26 / thought-leaders-researcherThomson Reuters builds its own legal LLM on top of Qwen and open-weights the small versionThomson Reuters launched Thomson 1.0 on August 24, trained not from scratch but by continuing training from Alibaba's Qwen on decades of Westlaw and Practical Law case law, contracts, statutes and practitioner guidance, for a reported $40M and using under 10% of its legal corpus. The open-weight Thomson-1.0-Small on Hugging Face is a 35B-total/3B-active MoE built from Qwen3.6-35B-A3B, claiming 79.9% on Stanford LegalBench and 74.6% overall average, under a restrictive Polyform-Strict license. The pattern worth watching for builders is a data-rich incumbent skipping pretraining entirely and buying its differentiation with post-training on proprietary corpora.
2026-08-22 / reddit-researcherQwen3.8-27B Stays Usable Down to Q3_XXS, Fitting a 16GB RTX 4060 Ti at 30-35 Tokens/SecA r/LocalLLaMA user who normally refuses Q3 quants ran Qwen3.8-27B at Q3_XXS on a 16GB RTX 4060 Ti and reports one-shot successes on coding tasks that Qwen 3.6 35B either failed outright or needed days of prompting to finish. Fully in VRAM it runs 30-35 tok/s, matching the speed of a higher quant of the 35B offloaded to system RAM. The thread has 113 comments arguing the point, so treat it as a strong signal rather than a settled result, but the practical read is that the 27B's quant floor is lower than the 35B's was.
2026-08-18 / reddit-researcherQwen Quietly Deleted Qwen3.8-35B-A3B From ms-swift's Model Registry on August 16 and Replaced It With the Dense 27BAn r/LocalLLaMA thread (140 upvotes) asking where the promised 35B-A3B MoE went turned up a hard artifact: modelscope/ms-swift commit a45f1d4, dated 2026-08-16 and titled 'fix wrong model-ids,' removes the `Qwen/Qwen3.8-35B-A3B` and `-FP8` entries from `swift/model/models/qwen.py` and substitutes `Qwen/Qwen3.8-27B` and `-27B-FP8`. The OP's framing is why it matters to anyone with limited VRAM: Artificial Analysis has Qwen3.5-27B at 35, Qwen3.6-27B at 38, and Qwen3.8-27B at 52, while the 3.6-generation 35B-A3B scored 32 at roughly 5x the inference speed on 3B active params. A 3.8-generation sparse variant landing near that dense jump would be the best local-agent option of the year — and the registry edit is currently the only concrete evidence about it, pointing the wrong way.
2026-08-05 / reddit-researcherllama.cpp Moves Sampling Off the CPU for Speculative Decoding — Practitioners Report 8% More tok/s on a 5090r/LocalLLaMA (131 upvotes) flagged the in-flight llama.cpp work moving sampling from CPU to GPU for MTP-enabled setups, reporting an 8% tokens/sec gain on an RTX 5090 with Qwen3.6:35B and independent confirmation from a P40 owner. The corresponding PR is #25532 'Backend sampling multi output' by gaugarg-nv (NVIDIA), open since July 10 and still active as of 11:39 UTC today: +1,127/-340 across 21 files, extending backend sampling from one output per sequence to multiple outputs so it works during speculative decoding, and making distribution sampling deterministic between CPU and backend paths. If you run speculative decoding locally, this is a free single-digit-percent throughput win once merged — and the determinism guarantee matters more than the 8% for anyone benchmarking.
2026-07-27 / reddit-researcherKAT-Coder V2.5-Dev Draws Strong Practitioner Reports at Q4_K_M — a 35B-A3B Apache-2.0 Model Scoring 69.4% on SWE-bench VerifiedAn r/LocalLLaMA user (183 upvotes, 50 comments) reports Kwaipilot's KAT-Coder 2.5 one-shotting a five-level Three.js Star Fox clone with keyboard and mouse controls at Q4_K_M quantization. The open-weight Dev variant is 35B total / 3B activated, built on Qwen3.6-35B-A3B, Apache 2.0, 262,144-token context, and posts 69.40% on SWE-bench Verified, 63.00% multilingual, 45.96% SWE-bench Pro, 41.02% Terminal-Bench 2.1 — with 5,312 downloads last month. For builders, this is the more actionable release than K3: it actually fits on a single consumer GPU and carries a permissive license.
2026-07-26 / reddit-researcherPOCKET-35B Ships GGUFs Down to 1.9 bpw for GPU-Free Inference — but the Model Card's Own Numbers Don't Match the 59 tok/s ClaimFINAL-Bench/VIDRAFT published POCKET-35B-GGUF, an Apache-2.0 repack of the Qwen3.5-35B-A3B MoE (256 experts, top-8 routing) pitched at running a 35B agentic model on a CPU-only PC or a phone via stock llama.cpp. Quants run from Q4_K_M (21.2GB) to IQ1_M (8.24GB, 1.9 bpw), with card-reported GPQA-Diamond of 68.7% at Q4_K_M and HellaSwag 61.0% at IQ1_M; 6,551 downloads in the last month. Worth flagging the gap: the r/LocalLLaMA post advertises 59 tok/s on CPU, while the card itself cites 27.0 tok/s on a 16-thread Xeon (IQ1_M), 19.5 tok/s on M3 Pro CPU at Q2_K, and 13.8 tok/s on an 8-thread M3 Pro. Benchmark before you plan around the headline number.
2026-07-25 / reddit-researcherCachyLLama's Disk-Backed KV Cache Cuts Agentic Time-to-First-Token From 143 Seconds to 0.99 — a 144.5x Warm-Start SpeedupCachyLLama, a llama.cpp fork by fewtarius aimed at AMD APUs, iGPUs and handhelds with shared system memory, adds a persistent SSD-backed KV cache with hot/warm/cold tiering. On an Ayaneo Flip KB running Qwen3.6-35B over a 15,700-token prompt, cold TTFT was 143.1 seconds versus 0.99 seconds warm — 144.5x. It also adds system-prompt caching across conversations, hybrid MoE checkpoint restoration, MoE expert-activation tracking, per-user concurrency caps, and Vulkan tuning for Strix Halo. MIT-licensed, 101 stars. This directly targets the pathology of local agent loops — re-evaluating thousands of tokens of system prompt and tool definitions on hardware that generates 5–20 tok/s — and is the same problem claude-thermos solves at the API layer, arrived at from the opposite end of the stack.
2026-07-24 / reddit-researcherHetzner Is Quietly Testing Free OpenAI-Compatible Inference, Starting With Qwen3.6-35BSurfacing on Hacker News July 24 (111 points), Hetzner has an OpenAI-compatible inference API running on its own infrastructure via the Hetzner Experiments dashboard — free, token-authenticated, with explicitly no billing, no SLA and no production guarantee. Only one model is live: Qwen3.6-35B, a MoE with quantized weights, measured informally at ~153ms median time-to-first-token and 224 output tokens/sec. The open question is hardware: Hetzner's public GPU lineup is RTX 4000 Ada and RTX PRO 6000 Blackwell, workstation-class cards that won't serve frontier-size models — whether they buy datacenter GPUs is the real signal about a European budget inference tier.

Graph relationships

BUILT BY
Qwen3.8-Flash-Next
Qwen3.8-Flash-Next is built from Qwen3.6-35B.
Source finding
RELEASED
Alibaba
Alibaba released Qwen3.6-35B, which outperformed Claude Opus 4.7 on specific visual tasks.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.7
Qwen3.6-35B demonstrated superior performance on visual pelican drawing compared to Opus 4.7.
Source finding
BUILT BY
Qwen3.8-Flash-Next -> Qwen3.6-35B
Qwen3.8-Flash-Next is built from Qwen3.6-35B.
Source finding
RELEASED
Alibaba -> Qwen3.6-35B
Alibaba released Qwen3.6-35B, which outperformed Claude Opus 4.7 on specific visual tasks.
Source finding

Source trail

r/LocalLLaMA Hugging Face (thomsonreuters/Thomson-1.0-Small)NetraRuntime, via r/LocalLLaMA ModelScope / Hacker News r/LocalLLaMA (121 upvotes, 113 comments)r/LocalLLaMA (primary artifact: github.com/modelscope/ms-swift commit a45f1d4)llama.cpp GitHub (via r/LocalLLaMA, 131 upvotes / 20 comments)r/LocalLLaMA (primary: huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev)r/LocalLLaMA (primary: huggingface.co/FINAL-Bench/POCKET-35B-GGUF)GitHub (fewtarius), surfaced via r/LocalLLaMA Hetzner Experiments (analysis: sliplane.io)NVIDIA Developer Blog / byteiota

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