Qwen 3.5 — October 2026
Community sentiment from 10 posts · 366 comments · collected November 10, 2026 · generated November 10, 2026
Qwen 3.5 — October 2026 scores 67.86/100 overall in the r/LocalLLaMA community benchmark, collected November 10, 2026 from 10 posts and 366 comments. Its strongest category is Coding quality at 74/100, and its weakest is Long context at 58/100.
Confidence: medium — 10 posts and 366 comments, but most are about fine-tunes, routers, or hardware projects built on Qwen 3.5 rather than the base model itself; 4 of 10 categories have no direct data, and several strongest claims (38x speed, 8-of-9 accuracy) come from project OPs and are challenged in-thread.
Category scores
Intelligence
68Mixed-to-positive. Users call the 4B results 'seriously impressive for 4B' and 'punching above its weight', and a 0.8B+adapter setup is reported to beat a standalone 27B on 8 of 9 tasks; offset by 'I have never gotten any rational sense out of 9B', the 4B base failing 3D scenes out of the box, and challenges to the router's accuracy evidence.
Coding quality
74Positive. A Qwen3.5-4B fine-tune one-shots Three.js 3D scenes that users compare to big-company internal 3D-asset generators ('Think you just replicated Roblox...'); 'This is far better than I expected'. Countervailing: a Q8 quant of that fine-tune 'doesn't even work with the example prompts' for one user, and the un-fine-tuned base mostly 'fails to render anything useful'.
Instruction following
no dataNo data in the sample — comments discuss fine-tunes (3D scene model, Bev, Jeff) and hardware projects built on Qwen 3.5; no direct statements about its prompt/instruction adherence.
Long context
58Single positive data point: the OP training a 4B fine-tune reports examples 'around 16K–64K tokens' that 'the little 4B is handling them really well so far'. No other context-length statements in the sample.
Price / value
70Positive. 'Qwen 4b is the father of all the most efficient 4b that we have today', 'size is very nice'; the 4B is trainable on a single GPU (~22GB VRAM per OP) and users run it on their own consumer hardware (one reports a 6GB card) with 'actually good results'.
Speed
68Positive for the small variants. The 0.8B router is 'small enough to be also fast on CPU' with a '38x faster decisions' headline; the FPGA post measures only ~6 tok/s prefill / ~3 tok/s decode at 75MHz, and one user calls the latency win believable while the accuracy headline is not.
Reliability & uptime
no dataNo data in the sample — no statements about stability or uptime of Qwen 3.5 itself; the only failure report concerns a Q8-quantized 3D fine-tune, and uptime is not discussed for local models.
Creativity & writing
no dataNo data in the sample — no user comments in these files evaluate Qwen 3.5's writing quality or creative output.
Multimodal & vision
no dataNo data in the sample — the image-classification praise in the Decision-model post targets that derivative decision model, not Qwen 3.5's own vision capabilities.
Agentic & tool use
66Positive. 'Nice to see another Qwen 4b finetuned for agentic tasks, I will definitely check it out'; a 0.8B Qwen router 'can speed up coding agents' with a planned tool-calling experiment; users plan to test Qwen-based 4B models 'as a sub agent in Pi.dev', and one OP runs 'qwen 27B' in their pi coding agent.
Summary
In the October 2026 r/localllama sample (top 10 posts by score mentioning qwen-3.5, 366 comments), Qwen 3.5 appears almost exclusively as the base of small-model projects: a 4B fine-tune generating Three.js 3D scenes, a 0.8B 'Jeff' router with nine LoRA adapters placed in front of a 27B model, an FPGA fabric implementation of the Qwen3.5 architecture, a browser/WebGPU neuron-steering playground for the 4B, a Microsoft 4B agentic fine-tune, and an intentionally-wrong high-confidence model. Users rarely evaluate Qwen 3.5 on its own; they evaluate what is built on top of it.
Where direct statements exist, they lean positive on coding and efficiency. Users call the 4B's 3D-scene results 'seriously impressive for 4B' and 'punching above its weight', comparing them to big-company internal asset generators, and one calls Qwen 4B 'the father of all the most efficient 4b that we have today'. The 0.8B variant is described as fast enough to run on CPU, and its adapter-based router is reported to beat a standalone 27B on 8 of 9 tasks with up to 38x faster decisions. The 4B is praised as trainable on a single GPU, and the project OP reports it handles 16K-64K-token training sequences well.
The negative side concerns raw capability and verification. The 4B base 'basically can't generate proper 3d scenes at all' without fine-tuning; a Q8 quant of the scene fine-tune 'doesn't even work with the example prompts' for one user; and one user says they have 'never gotten any rational sense out of 9B'. The 0.8B router's accuracy headline is challenged as under-evidenced (300 rows per task, no per-adapter confidence intervals, no router-vs-LoRA-vs-base ablation), and the absence of Qwen releases in the 14-24B range that several users call the sweet spot for 16GB GPUs draws complaints about the small-model landscape.
Coverage is uneven: instruction following, creative writing, multimodal/vision, and reliability/uptime have no direct user statements in these files — vision praise targets a separate decision model, and the only failure report concerns a quantized fine-tune. The picture that emerges is of Qwen 3.5 as a strong, efficient base for small-model builders, with its 4B in particular the workhorse; the recurring caveats are out-of-the-box capability on specialized tasks and independently verified accuracy claims.
Pros and cons
Pros
- 4B fine-tunes produce results users call 'seriously impressive for 4B' and 'punching above its weight' — one-shot Three.js 3D scenes compared to big-company internal 3D-asset models
- Efficiency is a recurring theme: 'Qwen 4b is the father of all the most efficient 4b that we have today', 'size is very nice', and a 0.8B variant that is 'small enough to be also fast on CPU'
- Trainable on modest hardware — a single GPU (~22GB VRAM) per the 3D-project OP — and runnable by users on their own consumer hardware, including a 6GB card, with 'actually good results'
- Handles long sequences: the 4B is reported to process 16K-64K-token training examples 'really well so far'
- Strong base for agentic work: a Qwen-4B agentic fine-tune is eagerly awaited, and a 0.8B router with LoRA adapters is reported to beat a standalone 27B on 8 of 9 tasks with up to 38x faster decisions
Cons
- The 4B base model is weak out of the box on specialized tasks: 'basically can't generate proper 3d scenes at all. most of the time it either fails to render anything useful or just produces a few basic shapes'
- Quantization fragility: one user reports the Q8 build 'doesn't even work with the example prompts' when asking for a rally car
- Doubts about the 9B size: 'I have never gotten any rational sense out of 9B… What do you use 9B for.'
- The 0.8B router's accuracy claims are questioned as under-evidenced: 300 rows per task, no per-adapter confidence intervals, no router-vs-LoRA-vs-base ablation
- Missing mid-size releases: multiple users ask where the good 14-24B models are, calling the 8-12B range the sweet spot for 16GB GPUs
Top quotes
“Think you just replicated Roblox, 50 billion dollar companies internal ai model for generating 3d assets lol”
Etroarl55 · 93 points positive view on Reddit ↗ “This is seriously impressive for 4B, especially with one person”
killerstreak976 · 46 points positive view on Reddit ↗ “A 4B one-shotting Three.js scenes is already impressive, but watching it improve checkpoint to checkpoint makes the full release even more exciting.”
CiobanuXashi · 10 points positive view on Reddit ↗ “It might look cruddy, but this is really punching above its weight. Compare to much bigger generalist models on Minebench! OP you're really pushing boundaries here”
Ylsid · 4 points positive view on Reddit ↗ “Nice to see another Qwen 4b finetuned for agentic tasks, I will definitely check it out. Qwen 4b is the father of all the most efficient 4b that we have today.”
synw_ · 6 points positive view on Reddit ↗ “Some training examples are pretty long, around 16K–64K tokens, and the little 4B is handling them really well so far.”
ConfidentDinner6648 · 9 points positive view on Reddit ↗ “I'm trying Q8 and most of the times it doesn't even work with the example prompts.”
Tasio_ · 6 points negative view on Reddit ↗ “I have never gotten any rational sense out of 9B… What do you use 9B for.”
No-Craft-7979 · 2 points negative view on Reddit ↗ “Latency win is believable if the tiny model is actually doing the work. The accuracy headline isn’t — not until you ablate router vs LoRA vs base, publish per-task CIs on those 300-row sets”
zykarys · 1 points negative view on Reddit ↗ “thx man , the base model basically can't generate proper 3d scenes at all. most of the time it either fails to render anything useful or just produces a few basic shapes, like in the example below”
ConfidentDinner6648 · 3 points negative view on Reddit ↗ “This is interesting. Do I get it right that there are two distinct ideas at play here, which don't rely on each other?”
crusaderky · 13 points neutral view on Reddit ↗ “Some of this was already on jeffhub.ai: https://jeffhub.ai/results has the ablation. 27B alone 86.6%; routing only, with no adapters, 87.1%; adapters only 95.2%; both 95.3%.”
Usual_Maximum7673 · 1 points neutral view on Reddit ↗
Top sample posts
Top 5 of 10 posts by score
- Update: My 4B coding model is getting better at generating 3D scenes ↗
ConfidentDinner6648 · 602 points · 114 comments
- Decision 3.0: open, multimodal decision models from 0.6B to 27B. Ranks #1 on both Jev Decision Index boards ↗
henzy123 · 548 points · 110 comments
- A 4B coding model that can generate 3D scenes ↗
ConfidentDinner6648 · 493 points · 96 comments
- Qwen3.5 arch implementation in FPGA fabric for 9B/27B INT4 models on relatively cheap eBay mining hardware ↗
I_am_purrfect · 363 points · 44 comments
- An LLM inside an LLM - Same guy who put a RISK-V CPU inside Qwen has now made Karpathy's stories260k run on an NPU compiled inside Qwen 3.5-4B's weights ↗
LatentSpacer · 333 points · 69 comments
FAQ
- What is Qwen 3.5 — October 2026's overall community score?
- 67.86 out of 100, with medium confidence: 10 posts and 366 comments, but most are about fine-tunes, routers, or hardware projects built on Qwen 3.5 rather than the base model itself; 4 of 10 categories have no direct data, and several strongest claims (38x speed, 8-of-9 accuracy) come from project OPs and are challenged in-thread.
- How was the Qwen 3.5 — October 2026 benchmark collected?
- From 10 posts and 366 comments on r/LocalLLaMA sampled over the past month window.
- Which categories does the Qwen 3.5 — October 2026 report score?
- Intelligence, Coding quality, Instruction following, Long context, Price / value, Speed, Reliability & uptime, Creativity & writing, Multimodal & vision, Agentic & tool use.
- What are the main community pros and cons for Qwen 3.5 — October 2026?
- Top pros: 4B fine-tunes produce results users call 'seriously impressive for 4B' and 'punching above its weight' — one-shot Three.js 3D scenes compared to big-company internal 3D-asset models; Efficiency is a recurring theme: 'Qwen 4b is the father of all the most efficient 4b that we have today', 'size is very nice', and a 0.8B variant that is 'small enough to be also fast on CPU'. Main cons: The 4B base model is weak out of the box on specialized tasks: 'basically can't generate proper 3d scenes at all. most of the time it either fails to render anything useful or just produces a few basic shapes'; Quantization fragility: one user reports the Q8 build 'doesn't even work with the example prompts' when asking for a rally car.
- When was the Qwen 3.5 — October 2026 data collected?
- Collected on November 10, 2026; the report was generated on November 10, 2026.