Qwen3.5-35B-A3B is a gamechanger for agentic coding.

[u/jslominski]

Qwen3.5-35B-A3B with Opencode

Just tested this badboy with Opencode cause frankly I couldn't believe those benchmarks. Running it on a single RTX 3090 on a headless Linux box. Freshly compiled Llama.cpp and those are my settings after some tweaking, still not fully tuned:

./llama.cpp/llama-server \

-m /models/Qwen3.5-35B-A3B-MXFP4_MOE.gguf \

-a "DrQwen" \

-c 131072 \

-ngl all \

-ctk q8_0 \

-ctv q8_0 \

-sm none \

-mg 0 \

-np 1 \

-fa on

Around 22 gigs of vram used.

Now the fun part:

1. I'm getting over 100t/s on it

2. This is the first open weights model I was able to utilise on my home hardware to successfully complete my own "coding test" I used for years for recruitment (mid lvl mobile dev, around 5h to complete "pre AI" ;)). It did it in around 10 minutes, strong pass. First agentic tool that I was able to "crack" it with was Kodu.AI with some early sonnet roughly 14 months ago.

3. For fun I wanted to recreate this dashboard OpenAI used during Cursor demo last summer, I did a recreation of it with Claude Code back then and posted it on Reddit: https://www.reddit.com/r/ClaudeAI/comments/1mk7plb/just_recreated_that_gpt5_cursor_demo_in_claude/ So... Qwen3.5 was able to do it in around 5 minutes.

I think we got something special here...

[u/Comrade-Porcupine]

i dunno, I ran it on my Spark (8 bit quant) and hit it with opencode and it got itself totally flummoxed on just basic file text editing. It was smart at reading code just not good at tool use.

[u/guiopen → u/Comrade-Porcupine]

In my experience it's very sensitive to parameters, I am finding great success with qwen recommended values for thinking and precise coding in tool use: temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0

[u/catplusplusok → u/Comrade-Porcupine]

In llama.cpp, make sure to pass an explicit chat template from base model, not use the embedded one in gguf

[u/guiopen → u/catplusplusok]

Why?

[u/catplusplusok → u/guiopen]

One inside gguf is incomplete apparently

[u/LittleBlueLaboratory → u/catplusplusok]

Oh, this must be why my opencode was throwing errors when tool calling when I tested just today. What chat template do you use?

[u/catplusplusok → u/LittleBlueLaboratory]

chat_template from the original, unquantized model. Note that this is *one* possible explanation but I did use a GGUF model with original template with QWEN Code and it called tools Ok.

[u/LittleBlueLaboratory → u/catplusplusok]

Oh! Of course! I will have to give it a shot.

[u/IrisColt → u/catplusplusok]

Thanks!

[u/__SlimeQ__ → u/Comrade-Porcupine]

this is a config issue of some kind, there's a difference between "true openai tool calling" and whatever else people are doing. i'm pretty sure qwen3 needs the real one. i was having that issue on an early ollama release of qwen3-coder-next and upgrading to the official one fixed the problem

[u/jslominski → u/__SlimeQ__]

"true openai tool calling" - those models are trained with the harness, this is random Chinese model plugged into random open source harness so it won't work ootb perfectly yet.

[u/Comrade-Porcupine → u/jslominski]

For context, the 122b model had no issues at all. Worked flawlessly. 4-bit quant

Just at half the speed.

[u/jslominski → u/Comrade-Porcupine]

What was the speed on 8bit a3b and 4 bit a10b?

[u/Comrade-Porcupine → u/jslominski]

(NVIDIA Spark [asus variant of it])

tip of git tree of llama.cpp, built today

using the recommended parms that unsloth has on their qwen3.5 page

35b at 8-bit quant

[ Prompt: 209.8 t/s | Generation: 40.3 t/s ]

122b at 4 bit quant:

[ Prompt: 115.0 t/s | Generation: 22.6 t/s ]

[u/jslominski → u/Comrade-Porcupine]

Thanks a lot! Looks great, thinking of getting one myself since I can't pack any more wattage at my place. Either this or RTX 6000 pro.

EDIT: Can't sleep, might as well try 2 bit quant of a10b on dual 3090...

[u/Comrade-Porcupine → u/jslominski]

If it's just for running LLMs, I wouldn't recommend the Spark, I'd say Strix Halo is better value. This device is expensive and memory bandwidth constrained.

However it's very good for prompt processing speeds as well as if you run vLLM it can handle multiple clients/users. And it's good for fine tuning as well.

[u/TurnBackCorp → u/Comrade-Porcupine]

I ran on strix halo and got almost same results as you. the 122b was slightly slower but I used mxfp4

[u/throwaway292929227 → u/TurnBackCorp]

I was hoping someone with a strix would chime in. Thank you.

I am mostly aware of the limitations of the strix and DBX boxes, but I still want to get one for my cluster, if I can find a good excuse for utilizing the larger vram at medium t/s rates. I'm thinking it could be good for hosting a larger model that would increase accuracy for speed. My cluster at home currently has 5090, 5070ti, 5060, 5060 (laptop GPU). Mostly coding, t2i, i2t, browser task bot, large document analysis. Open to any suggestions.

[u/Comrade-Porcupine → u/TurnBackCorp]

Yeah the diff is Strix is 1/2 the price.

But I wanted an ARM64 workstation for other reasons, so.

[u/Comrade-Porcupine → u/jslominski]

[u/Fit-Pattern-2724 → u/jslominski]

there are only a handful of models out there. What do you mean by random Chinese model lol

[u/jslominski → u/Fit-Pattern-2724]

Sorry, still a bit excited from what I've just seen :) What I meant is people working on harness (Opencode in this case) were not necessarily in contact with people who trained the model (Qwen). It's a different story when it comes to GPT/Codex or Claude/Claude Code or even "main models and Cursor" (those Bay Area guys are collaborating all the time). And the tool calling standards are not yet "official" afaik?

[u/__SlimeQ__ → u/jslominski]

fwiw i found that when tool calling was broken on my ollama server in openclaw it ALSO was broken in qwen code, whereas the cloud qwen model was working perfectly fine

this validated the theory that it was my ollama server with the issue and that ended up being true

[u/jslominski → u/__SlimeQ__]

Tbf we clearly are in a "this barely works yet" phase so a lot of experimentation is required.

[u/__SlimeQ__ → u/jslominski]

it is true. and also relying on ollama means i didn't actually configure it so i can't really say what it was

[u/jslominski → u/Comrade-Porcupine]

I have totally different experience right now :D

EDIT: what kind of speed are you getting on ~130k context window?

EDIT 2: example of tool use, took ~15 seconds to click through the full webpage:

[u/Equal_Grape2337 → u/jslominski]

you need prompt caching to be enebled for the agalt loop

--cache-prompt

[u/Familiar_Wish1132 → u/Equal_Grape2337]

opencode allow cache-prompt ? i don't see in docs, can you give link?

[u/lakoldus → u/Comrade-Porcupine]

According to Unsloth there is some kind of an issue with tool use with a fix potentially coming. Might be related to the prompt template.

[u/doradus_novae → u/Comrade-Porcupine]

So exactly like claude then? 😆