https://thot-experiment.github.io/gradient-gemma4-31b/
This is a relatively complex piece of tooling built entirely by Gemma 4 inside OpenCode where I manually intervened maybe only 4 times over the course of a few hours.
running Q6_K_XL, 128k context @ q8 ~ 800tok/s read 16tok/sec write
eagerly awaiting turboquant and MTP in llama.cpp, should take me to 256k and 25-30tok/s if the rumors are true
I went to the store to buy mixers and while I was out Gemma 4 31b got pretty far along with reverse engineering the bluetooth protocol of a desk thermometer I have. I forgot to turn on the web search tool, so it just went at it, writing more and more specific diagnostic logging/probing tools over the course of like 8 turns. It connected to the thermometer, scanned the characteristics and had made a dump of the bluetooth notification data. When I got back it was theorizing about how the data might be encoded in the bluetooth characteristics and it got into an infinite loop. (local models aren't perfect and i never said they were) I turned on the websearch tool and told it to "pick up the project where it left off", it read the directory, did a couple googles and had a working script to print temperature, humidity and battery state in like 3 turns. Reading back throught it's chain of thought I'm pretty sure it would have been able to get it eventually without googling.
idk, I thought I was a cool and smart engineer type for being able to do stuff like this, if my GPUs being able to do this more or less unsupervised isn't impressive I guess fuck me lol.
Built a basic authentication handler for this test just so it wouldn't be in the training data of either model. It had deliberately planted bugs. One was a hardcoded secret, another was a wrap-on-0xFFFFFFFF bug as a result of a malloc(length+1).
Qwen 3.6 found both, alongside two other issues I hadn't even considered, and the location of the magic value. GPT-5.4, though, missed the malloc issue (flagging memory exhaustion as the only risk), it missed a separate timing bug (it explicitly said the function was safe), and it hallucinated the location of the magic value. Qwen correctly identified the integer overflow. GPT-5.4 did not.
I then compared basic research between them using SearXNG for web search. For example, the current status of MTP in llama.cpp. Qwen 3.6 27B found the current PR, but flagged a related issue that shows the current implementation can be slower than just using a draft model right now. GPT-5.5 Thinking found the same PR, but didn't flag the downsides.
In a similar comparison, I asked both models how I should get started with ESPHome as a total beginner. ChatGPT suggested an ESP32-S3 and a BME280, which is... just not a good idea. It also talked about the ESP32-P4 not having Wi-Fi, and installing with HA or Docker. Meanwhile, Qwen3.6 27B said regular ESP32, DHT22, and mentioned HA, Docker, and pip as installation methods. While GPT was good, it was just throwing out jargon for a prompt that explicitly requested it for a beginner.
It kind of blew my mind that in all three of these, Qwen landed it better.
And we progress on so many different frontiers in parallel: Agent harness, Agent model, hardware etc.
Edit: For comparison with the other poster, same setup as above, but with Gemma 4 31B Instruct 8bit MLX (not sure if exactly the same model): time to first token 4.62s, 7.20 tokens/s; with a different prompt, 1.17s and 7.24 tokens/s.