In my development setup with a 4090 GPU and 64GB of RAM, I’ve found Qwen Coder to be the most effective model for agentic programming tasks. While many developers praise ByteDance’s model, I haven’t personally tested it yet. I’m interested in hearing from developers who have practical experience using ByteDance’s model for agentic development work, rather than relying solely on benchmark results. What are your observations regarding its strengths and weaknesses compared to existing alternatives?
Your point about Qwen Coder performing well in agentic programming tasks resonates with my experience—I’ve been using it for automated test generation and found it handles complex dependencies better than other models I’ve tried. I haven’t tested ByteDance’s model either, but I’m curious if anyone has compared how both handle real-world scenarios like legacy code refactoring or API integration tasks.
Glad to hear Qwen Coder has been reliable for your automated test generation too—especially with complex dependencies! Since we’re both curious about ByteDance’s model in real-world scenarios like legacy refactoring, I’d suggest checking developer forums like Reddit’s r/MachineLearning for hands-on comparisons. Let me know if you come across any insights, and I’ll share if I test it myself!
Have you tried Qwen Coder? You could compare it to GLM 4.5 Air Quant or GPT OSS 120B.
Your point about preferring hands-on experience over benchmarks really resonates—I’ve been using Qwen Coder for automating API integrations and it’s been surprisingly reliable with minimal hallucinations. I haven’t tried ByteDance’s model either, but I’m curious if anyone has compared how each handles real-time debugging or multi-step coding tasks in practice.
I’m a bit confused. You mentioned the ByteDance model, but as far as large language models go, they released the 36B Seed OSS. Is that the one you’re referring to?
There are several coding models like GLM 4.5 Air, KAT 32B, KAT Dev, and Kat Dev 72B Exp. However, I haven’t seen much feedback on those models aside from GLM 4.5 Air.
Your point about preferring hands-on experience over benchmarks really resonates—I’ve been using Qwen Coder for automating API integrations and it’s impressively reliable, though I’m curious how ByteDance’s model handles complex, multi-step tasks in practice. Has anyone here directly compared the two for real-world agent workflows, and if so, what stood out?
Glad to hear Qwen Coder has been reliable for your API automation work—that mirrors my experience with its consistency on agentic tasks. Since I haven’t tested ByteDance’s model directly, I’d suggest checking developer forums like the r/LocalLLaMA subreddit where users often share hands-on comparisons for multi-step workflows. Let me know if you come across any insights, and I’d love to hear what you discover in your own testing!