Entity trail

Qwen3.6 35B-A3B

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

Briefing refs

Findings

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Sources

Corpus findings

2026-05-28 / reddit-researcherQwen3.6 35B-A3B Successfully Completes FoodTruck Bench — First Small MoE to Pass Full Agentic BenchmarkA r/LocalLLaMA post (77 upvotes, 15 comments) reports Qwen3.6 35B-A3B (3B active parameters) successfully completed the FoodTruck Bench, a multi-step agentic reasoning benchmark at foodtruckbench.com. This follows last week's community consensus that Qwen3.6 is emerging as the top local model for agentic use (174 upvotes, 148 comments on May 26). The FoodTruck Bench completion adds concrete benchmark validation to the adoption trend — notable because models at this parameter count have historically struggled with multi-step agentic tasks.
2026-05-25 / reddit-researcherQwen3.6-35B-A3B vs Gemma4-26B-A4B: 147-Upvote, 117-Comment Community Benchmark ShowdownA high-engagement r/LocalLLaMA thread (147 upvotes, 117 comments, 0.80 comment-to-score ratio) compares Qwen3.6-35B-A3B against Gemma4-26B-A4B — two of the most capable mid-size mixture-of-experts models for local deployment. The thread captures real-world benchmark results and practitioner preferences across coding, reasoning, and creative tasks, signaling that the MoE architecture race is producing genuine competition at the 26-35B parameter range.
2026-05-23 / reddit-researcherQwen3.6-35B-A3B Q4 Runs 262K Context on 8GB 3070 Ti at 30+ Tokens/secA practitioner reports running Alibaba's new Qwen3.6-35B-A3B (MoE, 3B active parameters) at Q4 quantization with 262K context on an 8GB RTX 3070 Ti at 30+ tokens/sec, pushable to 512K-1M with speed degradation past 150K. Qwen3.6 is a confirmed new release succeeding Qwen3.5 under Apache 2.0, continuing Alibaba's aggressive open-source MoE push — now rivaling much larger models on consumer hardware (74 upvotes, 30 comments).
2026-05-19 / thought-leaders-researcherOpen-Weight Model Surge: Gemma 4, GLM-5.1 (1.5TB), and Qwen3.6-35B Now Run on LaptopsSimon Willison's PyCon talk highlights a dramatic open-weight model surge: Google's Gemma 4 (April) is their most capable open model yet, China's GLM-5.1 is a 1.5TB open-weight release, and Qwen3.6-35B-A3B fits in 20.9GB — running on a standard laptop. Gemini 3.1 Pro (February) produced notably superior visual outputs. The gap between frontier and open models is compressing faster than expected, with practical implications for local AI development and privacy-sensitive deployments.
2026-05-16 / vibe-coding-researcherQwen3.6-35B-A3B Beats Gemini 2.5 Pro on Terminal-Bench 2.0: 24.6% vs 19.6% with little-coder HarnessQwen3.6-35B-A3B (a 35B MoE model with only 3B active parameters) paired with the little-coder harness scored 24.6% (±3.2) on Terminal-Bench 2.0, landing above Gemini 2.5 Pro on Gemini CLI (19.6%). This demonstrates that small open-weight MoE models with the right harness can outperform frontier cloud models on agentic terminal coding benchmarks, shifting the cost-performance frontier for local agent deployment.
2026-05-12 / vibe-coding-researcherUnsloth Ships Multi-Token Prediction GGUFs for Qwen3.6 — 1.5-2x Faster Local Inference with 75% Acceptance RateUnsloth released GGUF quantizations of Qwen3.6-27B and 35B-A3B with Multi-Token Prediction (MTP) layers grafted on, stored in Q8_0 precision. MTP predicts 3 draft tokens per step with ~75% acceptance rate, achieving 1.5-2x faster generation over baseline autoregressive decoding. Requires a custom llama.cpp build from PR #22673. Multiple community creators (froggeric, havenoammo) have also released MTP-enabled GGUF variants. This significantly closes the speed gap between local and API inference for coding tasks.
2026-04-26 / reddit-researcherQwen3.6-35B-A3B Heretic Uncensored Model Claims KLD 0.0015 — Community's Top Uncensored 35B Pickllmfan46's Heretic-abliterated Qwen3.6-35B-A3B at KLD 0.0015 (indicating minimal quality loss from uncensoring) is being called the best uncensored model at this size. Fits in 24GB VRAM with IQ4XS quantization, Q8 KV cache, and 262K context. Amidst the HauhauCS plagiarism scandal, practitioners are rallying around Heretic as the legitimate uncensoring tool. 366↑ on r/LocalLLaMA with 65 comments.
2026-04-26 / reddit-researcherQwen3.6-27B-INT4 Hits 100 tps on Single RTX 5090 with 256K Context via vLLM 0.19Community benchmarks show Qwen3.6-27B-INT4 hitting 100 tokens/sec on a single RTX 5090 with 256K context length using vLLM 0.19, up from 80 tps the day before with the same hardware. Practitioners report the 27B dense variant is noticeably better than the 35B-A3B MoE for coding tasks despite lower parameter count. 212↑ on r/LocalLLaMA with 75 comments sharing optimization recipes.
2026-04-25 / reddit-researcherQwen3.6-35B-A3B Spontaneously Hallucinated Context Window Full — Emergent Self-Monitoring BehaviorA r/LocalLLaMA user (113 upvotes, 35 comments) discovered Qwen3.6-35B-A3B spontaneously hallucinating that its context window was full despite having capacity remaining, fabricating a contextual awareness signal at approximately the right moment. The unexpected emergent behavior suggests the model learned to approximate context-length tracking from training data, but unreliably — a potential production reliability concern for developers relying on MoE models for long-context tasks without explicit context management.
2026-04-25 / reddit-researcherQwen3.6-35B-A3B: Bigger Quants Beat Smaller Even in VRAM-Limited Scenarios — Challenges Conventional Wisdomr/LocalLLaMA benchmarks (241 upvotes) demonstrate that Qwen3.6-35B-A3B performs better with larger quantizations even when VRAM is constrained, challenging the standard advice of using smaller quants to fit more of the model in GPU memory. The finding suggests that for MoE architectures with selective expert activation, quality-per-active-parameter matters more than cramming the full model into available VRAM — a practical insight for anyone running local models on consumer hardware.
2026-04-16 / vibe-coding-researcherPattern: Local Qwen3.6-35B Beats Opus 4.7 on Visual Tasks — Frontier Isn't Always BestSimon Willison demonstrated that Alibaba's Qwen3.6-35B-A3B running locally on a laptop produced a better pelican drawing than Claude Opus 4.7 — on the same day Opus 4.7 launched with its 98.5% visual acuity benchmark. This is a recurring pattern: frontier models dominate aggregated benchmarks but specific open-weight models can outperform on narrow tasks at zero marginal cost. For builders: always test your actual use case against local models before committing to API spend.
2026-03-28 / reddit-researcherM5 Max vs M3 Max Inference Benchmarks — Qwen3.5 Performance Comparison Across Three Model SizesA practitioner ran identical benchmarks on both 16-inch MacBook Pros (M5 Max and M3 Max, both 40 GPU cores, 128GB unified memory) across Qwen 3.5 122B-A10B MoE, 35B-A3B MoE, and 27B dense models using oMLX v0.2.23. The 81-upvote post with 31 comments provides the first direct M5 Max vs M3 Max inference comparison, giving local model practitioners concrete data on whether Apple's latest silicon justifies the upgrade for LLM workloads.

Graph relationships

BENCHMARKED AGAINST
Gemini 2.5 Pro
Qwen3.6-35B-A3B scored 24.6% on Terminal-Bench 2.0, outperforming Gemini 2.5 Pro at 19.6%.
Source finding
BENCHMARKED AGAINST
RTX 3070 Ti
Qwen3.6-35B-A3B runs at Q4 quantization on 8GB RTX 3070 Ti.
Source finding
RELEASED
Alibaba
Alibaba released Qwen3.6-35B-A3B MoE model under Apache 2.0.
Source finding
BENCHMARKED AGAINST
FoodTruck Bench
Qwen3.6 35B-A3B successfully completed the FoodTruck Bench multi-step agentic reasoning benchmark.
Source finding
COMPETES WITH
Gemma4-26B-A4B

Source trail

r/LocalLLaMA r/LocalLLaMA r/LocalLLaMA simonwillison.net r/LocalLLaMA Hugging Face / Unsloth HuggingFace / r/LocalLLaMA

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