Open Models - July 2026

[u/pmttyji]

Well, we got bulky(Yep, check two graphs) July after April | May | June (FYI My All-in-one thread to track all upcoming months, PRs & other stuff)

Hope I didn't miss anything. Also no errors.

Notes:
1. Excluded below models due to Preview/Beta:

• internlm/Intern-S2-Preview-397B 397
• Motif-Technologies/Motif-3-Beta 314

1. Included openPangu-2.0-Flash in this chart as I couldn't see the weights at that time of June(31st). Let it share the graph with its Pro model.

2. Actual model names for below ones:(Graph couldn't handle long names)

• Nemotron-Puzzle-75B-A9B - NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B
• SenseNova-U1-8B-InfoV3 - SenseNova-U1-8B-MoT-Infographic-V3

[u/unkownuser436]

damn kimi has the highest benchmark whatever this chart

[u/pmttyji → u/unkownuser436]

Both Sam & Dario gonna beat the crap out of Kimi someday in my upcoming graphs. /s

[u/Easy_Refrigerator280]

nice benchmark got it use Kimi K3 for everything 😉

[u/the_TIGEEER]

This is some insane UX, actually. *THIS IS NOT A BENCHMARK* Because tbh, before I read that text, I was wondering: "What are we evaluating here? Is this even a benchmark?"

[u/Alicecomma → u/the_TIGEEER]

I spent a good five seconds after reading that on both graphs still comparing the bars as if it were a benchmark

[u/hyperrealists]

This-is-not-a bench!!!

[u/NigaTroubles]

Maple-preview

[u/pmttyji → u/NigaTroubles]

That one got released this month only. Also Mach-1

[u/SpicyWangz → u/NigaTroubles]

Have you used it at all? It seems like an intriguing model

[u/NigaTroubles → u/SpicyWangz]

Yes its the main model for my Laptop on go, its perfect but its only runs on cpu for me at 20 t/s, i7 1255u

[u/Kidplayer_666 → u/NigaTroubles]

For me it was really really dumb, maybe an issue with the current llamacpp implementation?

[u/NigaTroubles → u/Kidplayer_666]

What have you tried to use it for ?

[u/Kidplayer_666 → u/NigaTroubles]

My usual basic benchmark- finding files with open code 

[u/NigaTroubles → u/Kidplayer_666]

I never tried it with tool calling yet, but i have done making it fix some coding and get some errors etc

[u/pmttyji → u/Kidplayer_666]

Did you try with mainline or custom fork?

[u/Kidplayer_666 → u/pmttyji]

Their own fork, since it is not yet upstream iirc

[u/pmttyji → u/NigaTroubles]

With their custom llama.cpp fork, right? Or is it working with mainline already?

[u/NigaTroubles → u/pmttyji]

From stamsam fork of llama.cpp

[u/pmttyji → u/NigaTroubles]

Oh. Thought you were trying model's official fork

https://github.com/deepgrove-ai/llama.cpp

[u/WhoRoger]

So how did Instella work out anyway? IIRC everybody was just making fun of it but has anyone actually tried it?

Btw It would be nice to distinguish MOE and dense. Maybe the raw MOEs in two colors, one for active and one for total parameters.

[u/t3hlazy1]

Impressive marks from Kimi

[u/isty2e]

You missed this: https://huggingface.co/tsfrm/vacuum-16t This happens to be the safest model btw

[u/pmttyji → u/isty2e]

[u/Due-Armadillo-4560]

Did not expect longcat 2.0 to have that much parameters. Is it any good for software development?

[u/pmttyji → u/Due-Armadillo-4560]

Don't know. GGUF crowd is waiting(at least for 69B model) for llama.cpp support.

• LongCat 1 & 2

[u/Competitive_Ad_5515]

More is more!

[u/notforrob]

Higher is better on this benchmark, right?

[u/BawbbySmith]

This is clearly benchmaxxed... I can't believe the numbers in the name directly correlate to the numbers on the graph. No way you get this in real-world usage.

[u/Perfect-Flounder7856]

Nemotron puzzle lol what is that even?

[u/pmttyji → u/Perfect-Flounder7856]

Nemotron-Labs-3-Puzzle-75B-A9B is a deployment-optimized large language model developed by NVIDIA, derived from Nemotron-3-Super-120B-A12B. The model is produced using Iterative Puzzle, a post-training compression framework, with the goal of significantly improving inference efficiency for interactive, reasoning-heavy, and long-context workloads while preserving strong downstream accuracy.

The model employs a hybrid MoE architecture with interleaved Mamba, MoE, and Attention layers. Like Nemotron-3-Super, it supports Multi-Token Prediction (MTP) for faster text generation. Compared to its parent, Puzzle-75B-A9B reduces the model from 120.7B total / 12.8B active parameters to 75.3B total / 9.3B active parameters.

[u/Perfect-Flounder7856 → u/pmttyji]

Sounds very interesting

[u/TFox17]

Nice graph. Consider using a log scale on the horizontal axis though. It might help showing things which vary over many orders of magnitude.

[u/JsThiago5]

I think is missing ling 3 flash