Most of the benchmark improvements afaict are in agentic and instruction following benchmarks.
To the countrary, the reason Chinese models are excelling in the smaller area is because there's tons of fat in closed source models because of the crazy cash being thrown around.
There absolutely is space to improve intelligence and capabilities without lathering on more and more parameters.
If you want Qwen3.8-27B Serving Configs for the DGX Spark vLLM NVFP4 and RTX 4090 llama.cpp GGUF I added the setups here https://x.com/ErdalToprak/status/2088299678085308761?s=20
Funny side note. It successfully one shot the program, but it wasn't able to run it because there literally wasn't enough VRAM left to allocate CUDA memory. Watching it try to debug that was fascinating. I'm pretty sure it would have killed the llama-server (and thus itself) if it hadn't been running in a separate container.
This is the perfect candidate for just splattering it on your nvme and then reading it off there and into memory. All of these run perfectly fine on simple m4 silicone:
https://github.com/drumih/turbo-fieldfare
Does it come down to utilization and/or specific model tricks and efficiencies (attention, kv cache, etc.)?
DeepInfra prices:
Qwen 3.6 27B: $0.32 in / $3.20 out
Gemma 3 27B: $0.08 in / $0.16 out
DeepSeek V4 Flash 0731: $0.08 in / $0.18 out
Qwen 3.6 35B A3B: $0.10 in / $0.95 out
https://openrouter.ai/qwen/qwen3.6-27b
https://openrouter.ai/google/gemma-3-27b-it
dsv4f appears bigger and more memory/computationally expensive to run any way I look at it.
Opus at home
I hope there also will be a new ~10b variant
categorization, information retrieval, semantic search, image description
also with the model as part of an agentic system with tool calling
(edit: it is quite impressive what a small model in a feedback loop can do)