XDA reported that local Qwen3.6-35B-A3B and Claude Sonnet 5.5 each passed the six requirements of the same GPU-logger task. Claude finished in 2 minutes 42 seconds, compared with 3 minutes 44 seconds for Qwen, and produced a more polished chart. XDA published its account on November 8, 2026; the test date was not stated.
The task asked both agents to build a Windows Python tool that logged GPU power, temperature, utilization, memory use and core clock to a timestamped CSV, then reported average and peak power and created a power-and-temperature chart. It also had to accept a run duration and show the result of a 30-second run. That is a useful test of one defined job, not a verdict on coding agents across different projects.
Passing one task is a concrete result; it does not settle how an agent will perform on the next one.
What the same-task comparison found
| Agent | Result on the six requirements | Reported completion time | Task-specific detail |
| Claude Code with Claude Sonnet 5.5 | Passed all six | 2 minutes 42 seconds | Repeated the 30-second test three times while correcting chart labels; produced a more polished chart. |
| OpenCode with Qwen3.6-35B-A3B | Passed all six | 3 minutes 44 seconds | Its usage notes omitted pandas and matplotlib, two dependencies needed for the logger. |
The times and task observations are from XDA’s account of this comparison. Claude’s shorter run and chart revisions were specific to this task; both agents met its listed requirements.
How Qwen ran locally on a 16GB GPU
XDA’s local setup paired Qwen3.6-35B-A3B, in Unsloth UD-IQ4_XS quantization, with OpenCode v2.2.0 and llama.cpp. The test PC used an NVIDIA GeForce RTX 4070 Ti Super with 16GB GDDR6X and an AMD Ryzen 5 7600X. The model file was 17.7GB, so the setup kept 12 layers’ worth of model experts in system RAM. XDA reported a 128K-token context and about 15GB of GPU memory use for this configuration.
That result describes a particular mix of model, quantization, runtime and hardware. The 16GB figure alone does not specify what another local setup would require. For this run, XDA says OpenCode was locked to the local llama-server and the PC’s network was disconnected.
Our earlier coverage of Qwen Code and Claude Code’s model flexibility looks at provider choice; this comparison focuses on what the two agents did on one defined coding task.
A different test on an RTX 5090 had mixed winners
In a separate four-task comparison, KGP Talkie’s Laxmi Kant tested local Qwen 3.8 27B on an NVIDIA GeForce RTX 5090 against Claude Sonnet 5.5 in Claude Code. Qwen won a Three.js city-building task; Claude won Pac-Man and Connect Four; and the two tied on a parking-app fix with 12 unit tests. The video’s setup used Qwen through Ollama with Q4 quantization and a 96K context, so it was not a repeat of XDA’s 16GB test.
Separate workflows add checks around the model
Reddit user u/hpiguyTR described a different local setup using Qwen3.8 Flash Next on an RTX PRO 6000. The user’s workflow retried malformed tool calls and server errors, required executable checks such as tests, type checks, builds or migrations to pass, and ran work inside a Docker sandbox and a separate Git worktree. Those safeguards belong to that user’s setup, not the Qwen3.6 configuration in XDA’s task.
