"the post-training performance gains in Qwen3.5 primarily stem from our extensive scaling of virtually all RL tasks and environments we could conceive."

I don't think anyone is surprised by this, but I think it's interesting that you still see people who claim the training objective of LLMs is next token prediction.

The "Average Ranking vs Environment Scaling" graph below that is pretty confusing though! Took me a while to realize the Qwen points near the Y-axis were for Qwen 3, not Qwen 3.5.

You'll be pleased to know that it chooses "drive the car to the wash" on today's latest embarrassing LLM question.

For those interested, made some MXFP4 GGUFs at https://huggingface.co/unsloth/Qwen3.5-397B-A17B-GGUF and a guide to run them: https://unsloth.ai/docs/models/qwen3.5

Pelican is OK, not a good bicycle: https://gist.github.com/simonw/67c754bbc0bc609a6caedee16fef8...

Would love to see a Qwen 3.5 release in the range of 80-110B which would be perfect for 128GB devices. While Qwen3-Next is 80b, it unfortunately doesn't have a vision encoder.

Sad to not see smaller distills of this model being released alongside the flaggship. That has historically been why i liked qwen releases. (Lots of diffrent sizes to pick from from day one)

Last Chinese new year we would not have predicted a Sonnet 4.5 level model that runs local and fast on a 2026 M5 Max MacBook Pro, but it's now a real possibility.

Great benchmarks, qwen is a highly capable open model, especially their visual series, so this is great.

Interesting rabbit hole for me - its AI report mentions Fennec (Sonnet 5) releasing Feb 4 -- I was like "No, I don't think so", then I did a lot of googling and learned that this is a common misperception amongst AI-driven news tools. Looks like there was a leak, rumors, a planned(?) launch date, and .. it all adds up to a confident launch summary.

What's interesting about this is I'd missed all the rumors, so we had a sort of useful hallucination. Notable.

Does anyone know what kind of RL environments they are talking about? They mention they used 15k environments. I can think of a couple hundred maybe that make sense to me, but what is filling that large number?

Does anyone else have trouble loading from the qwen blogs? I always get their placeholders for loading and nothing ever comes in. I don’t know if this is ad blocker related or what… (I’ve even disabled it but it still won’t load)

Already on open router, prices seem quite nice.

https://openrouter.ai/qwen/qwen3.5-plus-02-15

The "native multimodal agents" framing is interesting. Everyone's focused on benchmark numbers but the real question is whether these models can actually hold context across multi-step tool use without losing the plot. That's where most open models still fall apart imo.

From the HuggingFace model card [1] they state:

> "In particular, Qwen3.5-Plus is the hosted version corresponding to Qwen3.5-397B-A17B with more production features, e.g., 1M context length by default, official built-in tools, and adaptive tool use."

Anyone knows more about this? The OSS version seems to have has 262144 context len, I guess for the 1M they'll ask u to use yarn?

[1] https://huggingface.co/Qwen/Qwen3.5-397B-A17B

Wow, the Qwen team is pushing out content (models + research + blogpost) at an incredible rate! Looks like omni-modals is their focus? The benchmark look intriguing but I can’t stop thinking of the hn comments about Qwen being known for benchmaxing.

Going by the pace, I am more bullish that the capabilities of opus 4.6 or latest gpt will be available under 24GB Mac

Is it just me or are the 'open source' models increasingly impractical to run on anything other than massive cloud infra at which point you may as well go with the frontier models from Google, Anthropic, OpenAI etc.?

Do they mention the hardware used for training? Last I heard there was a push to use Chinese silicon. No idea how ready it is for use

Was using Ollama but qwen3.5 unavailable earlier today

I just started creating my own benchmarks (very simple questions for humans but tricky for AI, like how many r's in strawberry kind of questions, still WIP).

Qwen3.5 is doing ok on my limited tests: https://aibenchy.com

Anyone else getting an automatically downloaded PDF 'ai report' when clicking on this link? It's damn annoying!

at this point it seems every new model scores within a few points of each other on SWE-bench. the actual differentiator is how well it handles multi-step tool use without losing the plot halfway through and how well it works with an existing stack

Let's see what Grok 4.20 looks like, not open-weight, but so far one of the high-end models at real good rates.

Is it just me or is the page barely readable? Lots of text is light grey on white background. I might have "dark" mode on on Chrome + MacOS.

Does anyone know the SWE bench scores?

Who can tell me how creating a sound generate from text localy

Yes, but does it answer questions about Tiananmen Square?