For coding, qwen 3.6 35b a3b solved 11/98 of the Power Ranking tasks (best-of-two), compared to 10/98 for the same size qwen 3.5. So it's at best very slightly improved and not at all in the class of qwen 3.5 27b dense (26 solved) let alone opus (95/98 solved, for 4.6).
You compare tiny modal for local inference vs propertiary, expensive frontier model. It would be more fair to compare against similar priced model or tiny frontier models like haiku, flash or gpt nano.
Going to have to disagree on the backup test. Opus flamingo is actually on the pedals and seat with functional spokes and beak. In terms of adherence to physical reality Qwen is completely off. To me it's a little puzzling that someone would prefer the Qwen output.
I'd say the example actually does (vaguely) suggest that Qwen might be overfitting to the Pelican.
Qwen's flamingo is artistically far more interesting. It's a one-eyed flamingo with sunglasses and a bow tie who smokes pot. Meanwhile Opus just made a boring, somewhat dorky flamingo. Even the ground and sky are more interesting in Qwen's version
But in terms of making something physically plausible, Opus certainly got a lot closer
I understand the 'fun factor' but at this point I really wonder what this pelican still proofs ? I mean, providers certainly could have adapted for it if they wanted, and if you want to test how well a model adapts to potential out of distribution contexts, it might be more worthwhile to mix different animals with different activity types (a whale on a skateboard) than always the same.
For a delightful moment this morning I thought I might have finally caught a model provider cheating by training for the pelican, but the flamingo convinced me that wasn't the case.
I've been using Qwen3.5-35B-A3B for a bit via open code and oMLX on M5 Max with 128Gb of RAM and I have to say it's impressively good for a model of that size. I've seen a huge jump in the quality of the tool calls and how well it handles the agentic workflow.
For coding, qwen 3.6 35b a3b solved 11/98 of the Power Ranking tasks (best-of-two), compared to 10/98 for the same size qwen 3.5. So it's at best very slightly improved and not at all in the class of qwen 3.5 27b dense (26 solved) let alone opus (95/98 solved, for 4.6).
You compare tiny modal for local inference vs propertiary, expensive frontier model. It would be more fair to compare against similar priced model or tiny frontier models like haiku, flash or gpt nano.
Eh it’s important perspective, lest someone start thinking they can drop $5k on a laptop and be free of Anthropic/OpenAI. Expensive lesson.
Going to have to disagree on the backup test. Opus flamingo is actually on the pedals and seat with functional spokes and beak. In terms of adherence to physical reality Qwen is completely off. To me it's a little puzzling that someone would prefer the Qwen output.
I'd say the example actually does (vaguely) suggest that Qwen might be overfitting to the Pelican.
Qwen's flamingo is artistically far more interesting. It's a one-eyed flamingo with sunglasses and a bow tie who smokes pot. Meanwhile Opus just made a boring, somewhat dorky flamingo. Even the ground and sky are more interesting in Qwen's version
But in terms of making something physically plausible, Opus certainly got a lot closer
Given adherence is a more significant practical barrier, it's probably the better signal. That is, if we decide too look for signal here.
That's not surprising; Opus & Sonnet have been regressing on many non-coding tasks since about the 4.1 release in our testing
I understand the 'fun factor' but at this point I really wonder what this pelican still proofs ? I mean, providers certainly could have adapted for it if they wanted, and if you want to test how well a model adapts to potential out of distribution contexts, it might be more worthwhile to mix different animals with different activity types (a whale on a skateboard) than always the same.
That's why I did the flamingo on a unicycle.
For a delightful moment this morning I thought I might have finally caught a model provider cheating by training for the pelican, but the flamingo convinced me that wasn't the case.
r/LocalLlama is now doing a horse in a racing car:
https://redd.it/1slz38i
It is completely wild to me that you prefer Qwen's flamingo. I think it's really bad and Opus' is pretty good.
The Opus one doesn't even have a bowtie.
To me the opus flamingo is waaaay better than the qwen one. qwen has the better pelican, though.
Is a flamingo on a unicycle not merely a special case of a pelican on a bicycle?
I'm really curious about what competes with Claude Code to drive a local LLM like Qwen 3.6?
OpenCode?
That Qwen flamingo on the unicycle is actually quite good. A work of art.
I've been using Qwen3.5-35B-A3B for a bit via open code and oMLX on M5 Max with 128Gb of RAM and I have to say it's impressively good for a model of that size. I've seen a huge jump in the quality of the tool calls and how well it handles the agentic workflow.
This is about the newly release Qwen3.6. Just wanted to make sure you got that correctly.
How about switching to MechaStalin on a tricycle? It gets kind of boring.
boring ... the ways all the models fail at a simple task never gets boring to me