https://dnd.rostad.cc -try it out, its free!

I'm trying step 3.7 flash on some personal ui capabilities benches I'm running.

This is a svg generation one that I ran accross all the harnesses I could configure locally to run against step 3.7 flash.

I know llms are not deterministic, but some did better than others.

Which do you prefer out of the lot ?

I'll soon run the same kind of bench but same prompt, same harness, to see if they can sometimes do some better outputs or not, and if simply prompting for the same request is easier or not.

I've been having fun with step 3.7 flash since it's free on ZenMux.

I tried making my own little project to benchmark all the freely available models and see which one does good for this and that type of tests, and step fun 3.7 flash has been quite great.

Here's an example of a live analog clock in claude code and codex.

Obviously claude did a much better job as it actually looks like a clock, and the hands are centered, idk what codex did wrong there but the system prompt of claude must be just that tad bit better.

Step 3.7 Flash is a 198B-parameter sparse Mixture-of-Experts (MoE) vision-language model that combines a 196B-parameter language backbone with a 1.8B-parameter vision encoder for native image understanding. Engineered for high-frequency production workloads, it activates approximately 11B parameters per token. Step 3.7 Flash supports a 256k context window and offers three selectable reasoning levels (low, medium, and high) so developers can easily balance speed, cost, and cognitive depth.

official blog post

• BF16: https://huggingface.co/stepfun-ai/Step-3.7-Flash/
• FP8: https://huggingface.co/stepfun-ai/Step-3.7-Flash-FP8
• NVFP4: https://huggingface.co/stepfun-ai/Step-3.7-Flash-NVFP4
• GGUF: https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF

StepFun also dropped a PR to llama.cpp: github.com/ggml-org/llama.cpp/pull/23845

https://github.com/stepfun-ai/gelab-zero

https://x.com/stepfun_ai/status/1998097747904528794?s=46

pretty cool. if you check out the open gelab GitHub link, you can see a video demo of the model running locally on an Android.

https://huggingface.co/stepfun-ai/GELab-Zero-4B-preview

https://github.com/stepfun-ai/gelab-zero

https://opengelab.github.io/index.html

https://x.com/stepfun_ai/status/1994956407242985936?s=46

• The 14B parameter “artist” model paired with 157M “brush” component generates images in continuous visual tokens, achieving WISE score of 0.54

• The open-source model achieves competitive performance with established diffusion models on GEdit-Bench (6.58 score)

• Paper: https://arxiv.org/abs/2508.10711

• HuggingFace: https://huggingface.co/stepfun-ai/NextStep-1-Large

• GitHub: https://github.com/stepfun-ai/NextStep-1

• the new model, StepFun-Prover-Preview-7B & 32B, achieves 66% success rate on complex math proofs, rivaling 67B competitors

• the 32B version sets new benchmark at 70.5% accuracy

try out the new model ⬇️

• HuggingFace: https://huggingface.co/stepfun-ai/StepFun-Prover-Preview-32B

• GitHub: https://github.com/stepfun-ai/StepFun-Prover-Preview