Qwen3.7-Max: The Agent Frontier

Well, yes, garbage in garbage out. That's a given and not what's meant by "hallucination" in this context.

Here are some examples of the questions in the benchmark. If these are representative, they seem pretty cut and dry. https://artificialanalysis.ai/evaluations/omniscience#exampl...

This counts only incorrect answers though. A model can get 0% hallucination rate just by refusing to answer all questions.

There are two flavors of Qwen 3.6:

- A 27B "dense" model

- A 35B "Mixture of Experts" model, which activates only 3B parameters for each token.

For your hardware, I strongly recommend `unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_M`. I have an M1 Max with 32GB VRAM from 2021 that can read at ~300-500 tokens/sec and write at ~30 tokens/sec with llama-cpp's default settings, which is plenty fast. The 27B model can read ~70tok/sec and write ~5tok/sec.

The 35B MoE model technically takes slightly more memory but is much faster because it's doing 1/9th the work. It's not quite as "smart", but it's comparable.

I recommend sticking with the dense models for both Qwen and Gemma.

On testing I've done on same-quant apples to apples, with F16/F16 (ie, unquantized) kv cache, 35B-A3B underperforms against 27B on anything even remotely complex. But yes, 35B-A3B can be like 3-4x faster on my hardware.

By Qwen's own admission, on any meaningful benchmark (ie, ones that involve logic, math, or tool calling), 27B performs like 122B-10B and 397B-A17B, but 35B-A3B is somewhere between 27B dense and 9B dense.

Also, MTP recently got merged in, so I'd suggest downloading Qwen 3.6 MTP (I assume you get it from unsloth) and updating your copy of llama.cpp, and adding `--spec-type draft-mtp --spec-draft-n-max 2` to your arguments.

https://huggingface.co/unsloth/Qwen3.6-27B-MTP-GGUF/ https://huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF/

Also, I recommend not quantizing kv cache, and if you do, only quantize v. Lowering model quant while also lowering context size to fit F16/F16 or F16/Q8_0 massively improves model performance for thinking models. Also, quantizing cache, either k or v, decreases speed by a lot on some hardware.

I have a 24gb 7900xtx, so I can fit >32k F16/F16 context with Qwen3.6-27B, but use unsloth's Q3_K_XL. This performs better than Q(4,5,6)_K_XL with v quantized.

    I tried the qwen3.6-27b Q6_k GUFF in llama.cpp 
    and LM Studio on my M2 MacBook Pro 32GB machine 
    last week, and I barely get a token a second with either.

The fact that it was this slow makes me suspect it's a matter of insufficient free RAM. The entire model needs to fit into RAM (and stay there the entire time) for acceptable performance.

(not sure of exact diagnosis/fix, but definitely look in that direction if you're still having this issue when you give it another shot)

Also, there are two stages - prompt processing, and token generation. Prompt processing is notoriously slow on Apple Silicon unfortunately. If you have large context (which includes system prompts, lots of tools loaded by a harness like Claude Code, OpenCode, etc) it can take minutes for prompt processing before you see the first output token. On the bright side, the tokens are cached between turns, so subsequent turns won't be so bad.

Check out Unsloth Studio it provides MTP support now which 2x the token generation speed with no loss of accuracy: https://unsloth.ai/docs/models/qwen3.6#mtp-guide

The 27B model is dense, so is relatively slow. The 35B-A3B model is marginally weaker but being MoE is much faster - like ~4-8x faster in basic benchmarks on my M1 Max.

For comparison, I just ran a couple of quick benchmarks (default settings) with llama-bench:

Qwen3.6-35B-A3B at Q6_K_XL gave 858 t/s pp512 (prompt processing) and 43 t/s tg128 (token generation).

Qwen3.6-27B at Q4_K_XL gave 103 t/s pp512 and 8 t/s tg128.

My token throughput is much better using vLLM-mlx on my M2 ultra than llama.cpp. It might be worth a shot to give it a try.

27B is the dense one. Try the Qwen3.6-35B-A3B variants for the MoE release. That's what I'm running on a Framework Desktop and I get ~50 tok/s plus or minus a few. The dense one is similarly slow for me -- not sure what to expect on your hardware from the MoE but it should probably be much faster.

> qwen3.6-27b Q6_k

That's the dense model, you probably want a mixture-of-experts (MoE) one.

Here's what you probably want instead: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF

I'm running Qwen3.6:27b Q4 KM on a 4090 and similarly fast CPU and I think 32GB of RAM. Make sure the context window is set to be big enough otherwise the conversation will keep compacting. No special MCP tools set up yet. Qwen is able to do web search out-of-the-box although I think it is getting blocked by anti-bot firewalls--I still need to figure out if I can fix that.

here's a simple setup to get you started on an Apple M1 Max from 2021 with 32GB VRAM. it will download 20GB of models to `~/.cache/huggingface/hub`, which you can delete when you're done.

  /Users/gcr/llama.cpp/build/bin/llama-server
      -hf unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_M
      --no-mmproj-offload
      --fit on
      -c 65536 # edit to taste
      --reasoning on --chat-template-kwargs '{"preserve_thinking": true}'
      --sleep-idle-seconds 90 # very aggressive: purge model from vram after this long
      -ctk q8_0 -ctv q8_0 # Optional. Lower memory use, but lower speed. Omit if you can.

I don't recommend ollama or lm-studio. Ollama's in the process of switching from their llama-cpp backend anyway, but their new go framework frequently OOMs and crashes on my hardware. I also don't recommend MLX-based inference backends on this hardware; I've found them to consistently reduce performance, contrary to what I've read online. I've tried all the llama-cpp metal forks, but right now, MTP, TurboQuant, MLX, etc etc etc are too new and just slow things down. It's all dust in the wind still.

For agent harnesses, opencode is okay, as is pi or even Zed's built in agent panel. Claude code "works" with ANTHROPIC_BASE_URL=http://localhost:8080/v1, but is very chatty (the default system prompt burns 20k tokens). Crush (from the charm-bracelet folks) is particularly nice when starting out. I've personally converged on pi-agent under an otherwise-mostly-default setup. You can ask qwen to customize pi or write you an extension which helps a little.

You'll need to add `http://localhost:8080/v1` as an OpenAI-compatible model provider in your coding harness with any API key (doesn't matter) and any model identifier (doesn't matter with llama-cpp).

Note that pi doesn't have permissions. Everything is permitted. The hundred hungry ghosts you've trapped in a jar WILL find a way to delete your home folder someday. That's what Man gets for summoning demons without casting a circle of protection first. Flying too close to the sun etc etc etc

Take backups and then go have fun. Hope this helps.

I would say if Sonnet is a senior engineer, then Qwen3.6 (the 27b model) is probably closer to a junior engineer. Still capable of getting stuff done, just needs more guidance and makes mistakes more often.

Maybe that's underselling it. It is quite a good model and might end up replacing a lot of the work I was sending to Sonnet 4.6.

Also, Sonnet 4.6 is almost certain a much bigger model so the performance differences aren't unexpected.

Where and how do you run that? I tried it but somehow I always ran out of context or generation was incredibly slow (mbp m4 pro 48gb).

Look for an open release from the Qwen team in the coming weeks. They like to showcase their proprietary models first, which score higher on benchmarks anyway due to model size.

I haven't tried Aider yet but perhaps I will. Another one that seems to be getting traction is Pi Coding Agent.

I do hope The U.S. hyperscalers do the same as well.

In an ideal world U.S. residents would use Chinese AI models and Chinese residents would use U.S. AI models.

Governments in both countries are collecting data for nefarious reasons. But the Chinese government has far less influence on a U.S. resident and vice versa.

We are all better off if our data is collected by a government halfway across the world instead of our own governments which hold incredible amounts of power over us.

Thank you for pointing that out! If 3.7-Max makes its way to Fireworks that'd be a joy.

All this data is accessible to national security agencies; this is true in every country in the world.

China has more integration between intelligence and industry than many western countries, and it does present a higher risk of unwanted “tech transfer” to industry than running on oracle or Google or ms or Amazon does in the US.

DHS has long staffed full time agents in California to deal with foreign IP exfiltration - using qwen is like fast/easy mode for IP exfiltration: why make anyone get a job in your palo alto office when you can just send it to them in Hanzhou?

Upshot - If you have something proprietary you’re working on I would generally advise not to just direct send it to Alibaba.

> Unless you're thinking Alibaba is going to ship chat logs to some government ministry

This made me think of a Seinfeld episode: "I didn't know it was possible not to know that."

… building and selling a product to US companies that sends company-internal data to Chinese AI providers is not a particularly good way to get people to buy it.

Even if they weren’t individually worried about their proprietary data being shared with Chinese domestic competitors or with government… their audit / security programs likely wouldn’t allow it for a _huge_ range of types of data.

>Unless you're thinking Alibaba is going to ship chat logs to some government ministry that will then dole out proprietary information to new competitors (which doesn't seem logistically feasible)

That's exactly the fear, and why would it not be logistically feasible? The threat is definitely a bit overhyped, but China has a longstanding track record of aggressive corporate espionage.

Because my CEO thinks China scary big hacker guys over there

The plus and max models have never been open as far as I know.

I have a 128G mac studio and even 397B was a happy surprise to me due to its high quantization resilience.

I've created a 2.54BPW quant that fit on my hardware with 128k context, 20 tps tg and 200tps pp, while maintaining high scores on many benchmarks: https://huggingface.co/tarruda/Qwen3.5-397B-A17B-GGUF/discus...

There definitely have been some options in the past, cool to see them.

Oddly enough, though, Qwen 3.6 35B A3B and Gemma got some really good reviews, despite being way smaller than any of these ones.

Qwen 3.5, 122B A10B: https://huggingface.co/unsloth/Qwen3.5-122B-A10B-GGUF

Qwen Coder Next, 80B A3B: https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF

It's kinda weird that DeepSeek V4 Flash is supposed to be 284B A13B, but shows up as 158B in HuggingFace, probably some weird bug: https://huggingface.co/unsloth/DeepSeek-V4-Flash and that's not even just Unsloth but like the official source too https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash (so also doesn't fit the category unless you get a heavily quantized version to run, but cool regardless)

Mistral Medium 3.5 is interesting because it's 128B but dense, so probably too slow for most folks: https://huggingface.co/unsloth/Mistral-Medium-3.5-128B-GGUF

GPT-OSS, 120B A5B: https://huggingface.co/unsloth/gpt-oss-120b-GGUF

What’s the price point for getting into that sweet spot?

I’m on an M1 Max with 32GB VRAM, so I’m looking forward to the 27B or 35B-A3B models. Is dropping $5k for an RTX 6000 or a DGX Spark really the best option?

> Realistically I assume they hope readers don’t notice the fine details.

The pool of people reading such articles while ignoring such details can't be big.

4.6 was an awful experience the month I used it right after launch where it didn't ask anything just made assumptions and went on its merry way. 4.5 and 4.7 don't do that for me but 4.7 eats my quota for breakfast so I've been avoiding using it because I like to have it for more than an hour a day.

Frontier: Need it done quick and I'm willing to pay.

Open-weight: Good enough for the majority of tasks, and I'm willing to spend a bit more time and effort steering towards my desired result.

Same here. Can't stand 4.7.

Opus 4.6 was never nerfed, that's FUD. There were harness-level problems that were fixed.

4.7 is much better. But perception is a funny thing, once you think something is bad you start looking for it everywhere.

Qwen recommends to preserve_thinking: true for agentic/coding workloads.

How do you use DeepSeek 4 Flash? Via a cli?

hey Qwen, how many civilians were killed on Tiananmen Square in 1989?

> Oops! There was an issue connecting to Qwen3.6-Plus.

> Content Security Warning: The input text data may contain inappropriate content.

hey ChatGPT, how many civilians were killed in Gaza in the war since 2023?

> [one page of estimates from local and international sources with links]

On some level, it's the lesser of two evils. Both do suck as options, I agree.

The Shanghai government surveillance drones are mobile, whereas the Flock government surveillance cameras are stationary! USA FTW, liberty and justice for all

Look around for EU LLM routers. There are some, but none are as big as OpenRouter. Still, Cortecs (Austria) is quite good and offers a couple of recent models through its EU-based providers. Zero data retention, GDPR compliant, etc. Really nice.

https://cortecs.ai/serverlessModels

This is the current European modus operandi: virtue signal and cry about tech that other countries produce, pass local laws that limit its use in their countries even though they have no viable local alternatives, brag amongst themselves about decoupling from US and Chinese tech, and then look on wistfully as the rest of the world moves on without a single fuck given.

Europe's sense of superiority and actual global importance/relevance is assbackwards.

My computer lacks the ram.

There is no reason why they couldn't license the model to Friendli/Fireworks/etc and have it hosted in the US to alleviate this concern.