Friendly reminder: wait a couple weeks to judge the ”final” quality of these free models. Many of them suffer from hidden bugs when connected to an inference backend or bad configs that slow them down. The dev community usually takes a week or two to find the most glaring issues. Some of them may require patches to tools like llama.cpp, and some require users to avoid specific default options.
Gemma 4 had some issues that were ironed out within a week or two. This model is likely no different. Take initial impressions with a grain of salt.
I've been waiting for this one. I've been using 3.5-27b with pretty good success for coding in C,C++ and Verilog. It's definitely helped in the light of less Claude availability on the Pro plan now. If their benchmarks are right then the improvement over 3.5 should mean I'm going to be using Claude even less.
Excited to try this, the Qwen 3.6 MoE they just released a week or so back had a noticeable performance bump from 3.5 in a rather short period of time.
For anyone invested in running LLMs at home or on a much more modest budget rig for corporate purposes, Gemma 4 and Qwen 3.6 are some of the most promising models available.
What competitive advantage does OpenAI/Anthropic has when companies like Qwen/Minimax/etc are open sourcing models that shows similar (yet below than OpenAI/Anthropic) benchmark results?
Also, the token prices of these open source models are at a fraction of Anthropic's Opus 4.6[1]
Been using Qwen 3.6 35B and Gemma 4 26B on my M4 MBP, and while it’s no Opus, it does 95% of what I need which is already crazy since everything runs fully local.
it seemed HN was moving the right direction when we added the "no AI comments", and yet, every single post about a new model is from you and your pelican. it's tired. please stop, it adds no value and has become cliche.
I just create the nopelican user to avoid seeing the same type of comments for scoring new models. Why doesn't someone create a pelican by month thread, like who is hiring, so that all who want to talk about their prefered mode and pelican can post with leisure at full extend. Perhaps such a thread could add some good information when grouped by time, model and pelican features. But I, honestly, think that the pelican test and the type of comments about it are too much, too repetitive, and it add no new information day after day.
The author of the pelican test has provided rich information about LLMs and AI just since LLM started to gain traction, but the pelican must fly and let the bicycle in the garage to show off just once a month.
Finally, a bitter take. Perhaps an information dense post without the pelican could be less commented and less reddit type, and some people might enjoy the image, so my comment from a boring, formal, not amussing person, may be not welcome from those, I agree.
This post suggest to create a by month thread about the pelican, it could give more value to the test. So I think is not far from meeting the HN etiquette of style.
Finally, since I think I will be downvoted until disappearing, LLM understand me: The "Substance" vs. "Meme" Conflict
I understand your frustration perfectly. When a model like Qwen 3.6-27B drops—a model explicitly marketed for "Flagship-Level Coding"—you want to know:
How does it handle dependency injection in complex Python projects?
What is its context window performance like for real-world repo analysis?
How does it compare to Claude 3.5 Sonnet for agentic workflows?
Instead, the top comments are often just people saying "Look, the pelican has three wheels!" or "The pelican is floating!" To you, this feels like a waste of the front page.
IMHO looks more like a stork, not a pelican. Look up any image of an actual pelican and check the ratio of legs to body. IMHO that's a weird mistake to make when asked for a "pelican".
Have you considered asking a couple of artists on Fiverr or something to draw you a picture with the same prompt? I don't mean this as a gotcha, it's actual advice, you should probably get a sense of what a real human artist/designer (or three) would do with this prompt.
For example, I hope you will find that: One reasoning choice is wrong with this picture that's not much to do with its ability to draw. Do we enlarge the pelican to human size? Or do we shrink the bike to pelican size? There is only one answer that keeps pelican proportions. Draw a pelican on a very tiny bike, and its legs will just fit without making it a different species, and you can even sort of cover part of the steer under the wings, etc etc.
I'm curious if other artists would come up with the same or other solutions, but they should in general come up with solutions, which I haven't seen the LLM do, really.
You (or maybe others?) said that the "pelican on a bike" prompt is good because "there is no right answer" cause you can't really fit a pelican on a bike. But most artists will say "hold my beer" and figure it out anyway. Cartoonists won't even have to think. The "figuring out" of these problems is what I'm missing in the LLMs response. It just put a pelican on a bike and makes it look like a stork if necessary. I don't really feel like it's actually testing for the thing this prompt is designed for, unless the test still says "FAIL" for each and all of them, including the one you just called "excellent".
I really like local models for code reviews / security audits.
Even if they don't run super fast, I can let them work overnight and get comprehensive reports in the morning.
I used Qwen3.6-27B on an M5 (oq8, using omlx) and Swival (https://swival.dev) /audit command on small code bases I use for benchmarking models for security audits.
It found 8 out of 10, which is excellent for a local model, produced valid patches, and didn't report any false positives. which is even better.
I'm kind of interested in a setup where one buys local hardware specifically to run a crap ton of small-to-medium LLM locally 24/7 at high throughput. These models might now be smart enough to make all kinds of autonomous agent workflows viable at a cheap price, with a good queue prioritization system for queries to fully utilize the hardware.
Adding to my own comment now that I've read the announcement in a little more detail: I find the assertion that the model's coding performance surpasses their own flagship 397B model from last generation fairly convincing.
This sounds like significant genuine gains unless one of the following is true, which would be really unlikely:
1. They somehow managed to benchmaxx every coding benchmark way harder than their own last generation.
2. They held back the coding performance of their last generation 397B model on purpose to make this 3.6 Qwen model look good. (basically a tinfoil hat theory as it would literally require 4D chess and self-harming to do)
So, it's pretty save to say that we actually have a competent agentic coding model we can leave on in a prosumer laptop overnight to create real software for almost zero token costs.
Has anyone tried using this with a Claude Code or Qwen Code? They both require very large context windows (32k and 16k respectively), which on a Mac M4 48GB serving the model via LM Studio is painfully slow.
Does anyone know good provider for low latency llm api provider? We tried to look at Cerebras and Groq but they have 0 capacity right now. GPT models are too slow for us at the moment. Gemini are better but not really at same level as GPT.
This depends a bit on your cost sensitivity and what model families you want support for, but Baseten and Fireworks have been my goto.
Currently Baseten has ~610ms TTFT and ~82 tk/s for Kimi K2.6, which is roughly 2x the throughput of GPT-5.4 (per their openrouter stats). GLM 5 is slightly slower on both metrics, but still strong.
I'll be really interested to hear qualitative reports of how this model works out in practice. I just can't believe that a model this small is actually as good as Opus, which is rumored to be about two orders of magnitude larger.
76 comments
[ 3.7 ms ] story [ 99.2 ms ] threadFriendly reminder: wait a couple weeks to judge the ”final” quality of these free models. Many of them suffer from hidden bugs when connected to an inference backend or bad configs that slow them down. The dev community usually takes a week or two to find the most glaring issues. Some of them may require patches to tools like llama.cpp, and some require users to avoid specific default options.
Gemma 4 had some issues that were ironed out within a week or two. This model is likely no different. Take initial impressions with a grain of salt.
https://unsloth.ai/docs/models/qwen3.6
Generate an SVG of a dragon eating a hotdog while driving a car: https://codepen.io/chdskndyq11546/pen/xbENmgK
Far from perfect, but it really shows how powerful these models can get
For anyone invested in running LLMs at home or on a much more modest budget rig for corporate purposes, Gemma 4 and Qwen 3.6 are some of the most promising models available.
Interesting pros/cons vs the new Macbook Pros depending on your prefs.
And Linux runs better than ever on such machines.
Also, the token prices of these open source models are at a fraction of Anthropic's Opus 4.6[1]
[1]: https://artificialanalysis.ai/models/#pricing
I ran it on an M5 Pro with 128GB of RAM, but it only needs ~20GB of that. I expect it will run OK on a 32GB machine.
Performance numbers:
I like it better than the pelican I got from Opus 4.7 the other day: https://simonwillison.net/2026/Apr/16/qwen-beats-opus/or wildly realistic,
pelican.
https://news.ycombinator.com/item?id=47865232
The author of the pelican test has provided rich information about LLMs and AI just since LLM started to gain traction, but the pelican must fly and let the bicycle in the garage to show off just once a month.
Finally, a bitter take. Perhaps an information dense post without the pelican could be less commented and less reddit type, and some people might enjoy the image, so my comment from a boring, formal, not amussing person, may be not welcome from those, I agree.
This post suggest to create a by month thread about the pelican, it could give more value to the test. So I think is not far from meeting the HN etiquette of style.
Finally, since I think I will be downvoted until disappearing, LLM understand me: The "Substance" vs. "Meme" Conflict
I understand your frustration perfectly. When a model like Qwen 3.6-27B drops—a model explicitly marketed for "Flagship-Level Coding"—you want to know:
Instead, the top comments are often just people saying "Look, the pelican has three wheels!" or "The pelican is floating!" To you, this feels like a waste of the front page.Have you considered asking a couple of artists on Fiverr or something to draw you a picture with the same prompt? I don't mean this as a gotcha, it's actual advice, you should probably get a sense of what a real human artist/designer (or three) would do with this prompt.
For example, I hope you will find that: One reasoning choice is wrong with this picture that's not much to do with its ability to draw. Do we enlarge the pelican to human size? Or do we shrink the bike to pelican size? There is only one answer that keeps pelican proportions. Draw a pelican on a very tiny bike, and its legs will just fit without making it a different species, and you can even sort of cover part of the steer under the wings, etc etc.
I'm curious if other artists would come up with the same or other solutions, but they should in general come up with solutions, which I haven't seen the LLM do, really.
You (or maybe others?) said that the "pelican on a bike" prompt is good because "there is no right answer" cause you can't really fit a pelican on a bike. But most artists will say "hold my beer" and figure it out anyway. Cartoonists won't even have to think. The "figuring out" of these problems is what I'm missing in the LLMs response. It just put a pelican on a bike and makes it look like a stork if necessary. I don't really feel like it's actually testing for the thing this prompt is designed for, unless the test still says "FAIL" for each and all of them, including the one you just called "excellent".
ollama launch claude --model qwen3.6:35b-a3b-nvfp4
This has been optimized for Apple Silicon and runs well on a 32G ram system. Local models are getting better!
Even if they don't run super fast, I can let them work overnight and get comprehensive reports in the morning.
I used Qwen3.6-27B on an M5 (oq8, using omlx) and Swival (https://swival.dev) /audit command on small code bases I use for benchmarking models for security audits.
It found 8 out of 10, which is excellent for a local model, produced valid patches, and didn't report any false positives. which is even better.
This sounds like significant genuine gains unless one of the following is true, which would be really unlikely:
1. They somehow managed to benchmaxx every coding benchmark way harder than their own last generation.
2. They held back the coding performance of their last generation 397B model on purpose to make this 3.6 Qwen model look good. (basically a tinfoil hat theory as it would literally require 4D chess and self-harming to do)
So, it's pretty save to say that we actually have a competent agentic coding model we can leave on in a prosumer laptop overnight to create real software for almost zero token costs.
Currently Baseten has ~610ms TTFT and ~82 tk/s for Kimi K2.6, which is roughly 2x the throughput of GPT-5.4 (per their openrouter stats). GLM 5 is slightly slower on both metrics, but still strong.