96GB (V)RAM agentic coding users, gpt-oss-120b vs qwen3.5 27b/122b

Reddit r/LocalLLaMA / 3/12/2026

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Key Points

The Qwen3.5 family could challenge gpt-oss-120b for 96GB VRAM agentic coding users by offering vision, parallel tool calls, and double the context length, though results vary by task.
Qwen3.5 tends to be slower and exhibits higher quality variance due to its larger parameter count and novel architecture, impacting speed and consistency.
In practice, many users still rely on gpt-oss-120b for speed, with occasional use of Qwen3.5-122B UD_Q4_K_XL gguf in a secondary pass under specific settings.
The discussion remains ongoing, with questions about which model, quantization, and sampling settings provide the best balance of speed and quality for agentic coding use cases.

The Qwen3.5 model family appears to be the first real contender potentially beating gpt-oss-120b (high) in some/many tasks for 96GB (V)RAM agentic coding users; also bringing vision capability, parallel tool calls, and two times the context length of gpt-oss-120b. However, with Qwen3.5 there seems to be a higher variance of quality. Also Qwen3.5 is of course not as fast as gpt-oss-120b (because of the much higher active parameter count + novel architecture).

So, a couple of weeks and initial hype have passed: anyone who used gpt-oss-120b for agentic coding before is still returning to, or even staying with gpt-oss-120b? Or has one of the medium sized Qwen3.5 models replaced gpt-oss-120b completely for you? If yes: which model and quant? Thinking/non-thinking? Recommended or customized sampling settings?

Currently I am starting out with gpt-oss-120b and only sometimes switch to Qwen/Qwen3.5-122B UD_Q4_K_XL gguf, non-thinking, recommended sampling parameters for a second "pass"/opinion; but that's actually rare. For me/my use-cases the quality difference of the two models is not as pronounced as benchmarks indicate, hence I don't want to give up speed benefits of gpt-oss-120b.

submitted by /u/bfroemel
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