Qwen 2.5 Coder tops OpenClaw benchmark

This is the kind of benchmark AI developers actually need: less leaderboard theater, more “can this model finish a messy agent workflow without breaking JSON or losing context?”

• –The benchmark focuses on agent failure modes that matter in production, especially tool use, multi-step chains, and structured output
• –Qwen 2.5 Coder 32B beating newer 2025-2026 models suggests frontier reasoning gains do not automatically translate into better autonomous agents
• –The repo is practical, not just editorialized: it ships raw results plus a zero-dependency Node.js harness for llama.cpp and Anthropic testing
• –The strongest caveat is scope: this is one operator’s workflow mix on a 24GB consumer GPU, so the findings are useful but not universal
• –For self-hosters, the most actionable takeaway is that dense mid-size models still look like the reliability sweet spot for local agents