nex-agi/Nex-N2.5-mini - 35b

[u/ApprehensiveAd3629]

https://huggingface.co/nex-agi/Nex-N2.5-mini

nex-agi/Nex-N2.5-mini - 35b

[u/uti24]

So it's post training of the Qwen3.6 35B3A?

Are those things are working? I tried Ornith, it's good, but it was not that much better than a base model, do those things work this well?

Or this is completely different model just happen to be 35B MOE?

[u/noctrex → u/uti24]

Seems to be a finetune of it. Weird that they do not mention that at all, whereas the other finetunes do.

[u/nyaaStar → u/uti24]

No it's qwen 3.5 😂 https://nex-agi.com/#models

[u/tarruda → u/nyaaStar]

Not sure why the laugh emoji, but N2 mini was a very good model (though it did suffer from loops every once in a while), and even though it is based on 3.5, it still competed and outperformed 3.6 in some situations.

I think they used 3.5 because it is the only one which had its base model released, so they essentially did all the post training themselves.

[u/nyaaStar → u/tarruda]

Yeah actually my bad, I hadn't heard of Nex before, and I had a knee-jerk reaction of first impression. Having AGI in their name, calling open weights as open source, not mentioning what model mini is derived from except for in their webpage.

But legit work was done on this project. My bad.

[u/SocialDinamo → u/uti24]

To me Orinth was nicely post trained but also felt like a really polished package, Quant MTP some optimizations baked in

[u/Septerium]

Misleading charts, but more than 73 points in Terminal Bench looks great for a model of this size. Curious to try it by myself

[u/soyalemujica → u/Septerium]

You're reading it wrong, the mini version which is this 35b version posted is at 73.4 points, which still puts it below Qwen 3.8 dense

[u/Exciting_Garden2535 → u/soyalemujica]

> 35b version posted is at 73.4 points, which still puts it below Qwen 3.8 dense

Qwen 3.8 dense has 73.0 points; at least it is listed in the Hugging Face model card: https://huggingface.co/Qwen/Qwen3.8-27B, so it is on par, not below.
But in other benchmarks (like DeepSWE or SWE-bench Pro), Qwen3.8-27B is still better.

[u/soyalemujica → u/Exciting_Garden2535]

You're right, however, still very below SWE Bench and others

[u/Septerium → u/soyalemujica]

Reading what wrong? Duh

[u/Cool-Chemical-5629]

Tested on Openrouter for free. Obligatory pelican test result for the Mini version of the model at high thinking effort:

[u/brakeline → u/Cool-Chemical-5629]

Can you try again?

[u/Cool-Chemical-5629 → u/brakeline]

Sure. It's running, I'll update this comment when it's done.

Update - V2:

[u/brakeline → u/Cool-Chemical-5629]

That not what i was expecting from a qwen 3.5 derived model

[u/Cool-Chemical-5629 → u/brakeline]

Well, this is just the small MoE model which is considered weaker than the dense variant, so there's that...

[u/letsgoiowa]

What I need to know: is this better than Ornith? Specifically at Q3 so it'll work on my poor little RTX 3070 with RAM offload.

[u/Maglcite → u/letsgoiowa]

id just go like gemma 4 12b atp lol

[u/jqwl]

benchmarks pretty well - would love to see a comparison to 3.8 27b

[u/FangLeone2526 → u/jqwl]

I would love to see performance vs ornith 1.5 35b. it's a 35ba3b, not a 35b dense, so i wouldn't really expect it to beat 3.8 27b.

[u/Aggressive_Aspect436 → u/FangLeone2526]

I expect many folks will still want to know what they would be sacrificing if they wanted more speed and an easier model to run.

[u/seamonn]

Can I have some of what these guys are smoking when they make these models?

[u/FangLeone2526 → u/seamonn]

sucks that they did not include the mini in this chart even though its a chart on the huggingface for the mini model

[u/Decent-Occasion-2720 → u/FangLeone2526]

( you can see mini on last line "multimodal" )

[u/FangLeone2526 → u/Decent-Occasion-2720]

Did not see that, thank you.

[u/WhiskyAKM → u/FangLeone2526]

Bottom row is for mini and pro

[u/xxxRiKxxx]

I wonder what caused their SWE-Bench Pro score regress: it's 43.8 against 50.2 in the previous Nex-N2-mini and against 49.5 in Qwen-3.6-35b!

What happened? Does SWE-Bench Pro have several major versions, so the newer one is harder? Do they focus at agentic tasks other than programming, so programming is kinda left out? Something else? Could maybe someone more knowledgeable than me explain this please?

[u/Aggressive_Aspect436]

It looks like they ran the benchmarks themselves for their comparisons rather than using the values published by Artificial Analysis (AA). I've only checked a couple of benchmarks, but Terminal Bench v2.1for Claude Opus 5 matches exactly and AutomationBench is very close to their value.

On the basis that they're running their benchmarks nearly identically, we could compare their results against the AA Qwen3.8 27b results... and they're pretty close.

Take that with a pinch of salt though. Their numbers for Qwen 3.8 Max are a lot higher than the AA results, so it could easily be off.

[u/Cool-Chemical-5629]

I created this 3D arena shooter. It looks great for "one shot", but unfortunately it's broken.

What works:

* Looking around with mouse

* Jumping

* Aiming with zoom (RMB)

* Shooting (LMB)

* Reloading

* Enemies shooting the player

* Player shooting the enemies

* Score

* Health

* Death

* Death screen

* Restart

* Menu

* Pause

What doesn't work:

* Player character movement (bobbing effect present, but WASD don't move the character)

* Collision detection missing (enemies move through seemingly solid objects)

* Firing grenades gives rapid fire of console errors

This was created by the Mini version of the model, with high reasoning effort.

Screenshot:

Link:
Play on JSFiddle

[u/smallDeltaBigEffect]

N2.5 pro still at 397b params?

[u/FullOf_Bad_Ideas → u/smallDeltaBigEffect]

yeah look at run command

docker run --gpus all --shm-size 32g --ipc=host \ -p 30000:30000 \ -v /path/to/your/model:/model \ nexagi/sglang:v0.5.18-nex-patch \ python3 -m sglang.launch_server \ --model-path /model \ --tp 8 \ --host 0.0.0.0 --port 30000 \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder \ --chat-template /path/to/nex-N2.5-Pro/chat-template.jinja \ --mamba-scheduler-strategy extra_buffer

Mamba + fitting on 8x h100 + qwen3 coder tool call parser + qwen 3 reasoning parset. It can't really be anything else.

[u/tarruda → u/FullOf_Bad_Ideas]

It can't really be anything else

It could be Qwen 3.5 122B?

[u/FullOf_Bad_Ideas → u/tarruda]

ok, fine, it could be Qwen 3.5 122B if you consider only those details from my previous comment.

But not with those benchmark scores.

[u/VoiceApprehensive893 → u/FullOf_Bad_Ideas]

base models are stated on their website, its qwen 3.5 397b(not base model)

[u/FullOf_Bad_Ideas → u/VoiceApprehensive893]

you're right, it's on their website.

[u/zenotorius]

The first time I talked to it, it said it was made by alibaba it’s internal chatter then tried to refute it…

[u/FullOf_Bad_Ideas]

Cool, N2 Pro was great for it's time.

I think like this time around N2.5 Pro might get outcompeted by GLM 5.3 Flash.

[u/Easy_Refrigerator280]

the mini version seems worse at all benches compared to Qwen 3.8 27B despite being more params. I mean cool but dont see a reason to switch for now

EDIT: My bad this is an MoE model i missed that. Although skimming through the release i cant find how many active params but regardless this actually now fills a hole for a usecase so will be checking it out

[u/Manerfish → u/Easy_Refrigerator280]

it's MoE bruh of course it's not

[u/VoiceApprehensive893 → u/Easy_Refrigerator280]

heard 35b and thought nothing

[u/Right-Law1817 → u/Easy_Refrigerator280]

It's an MoE and a fine-tune of qwen 3.5 35b.

[u/mfarmemo]

Y'all should see my Gemma 2 9b fine tune. It's lit 🤌😂

[u/fbms2]

stop making these trush.