The coding-LLM contest has moved from "open-source vs. closed-source" to "open-source vs. open-source". In the first half of 2026, Z.ai's GLM-5.2, DeepSeek V4, and Alibaba's Qwen3.6 each released their weights in succession, pushing the "downloadable frontier" to a new stage. The deployment threshold differs dramatically. GLM-5.2 is a 753B-total / 40B-active MoE under MIT, requiring a few H100s to serve. DeepSeek V4 Pro goes further, with 1.6T total / 49B active, 1M context plus 384K output (the highest of the three), also MIT, and 1.6T is essentially never self-hosted — everyone goes through its $0.435 / $0.87 API. Qwen3.6-35B-A3B is a totally different picture: 35B total / 3B active, Apache 2.0, only 21GB VRAM after Q4 quantization, fitting on a 24GB consumer card — the only one of the three that runs on an ordinary workstation. Benchmarks have to be read by "cluster", not by "leaderboard". On SWE-bench Pro, GLM-5.2 reports 62.1 to lead; on Terminal-Bench 2.1, 81.0. DeepSeek V4 Pro-Max sits around 80.6% on SWE-bench Verified, but that's vendor-self-reported so discount accordingly. Qwen3.6-35B-A3B takes SWE-bench Verified at 73.4 — and this number is the one to remember, because it's achieved with 3B active parameters, an unmatched per-watt capability density among open-source models. So which to pick? For the strongest MIT-licensed public-weight score, GLM-5.2. For frontier quality with the lowest single-task cost via API, DeepSeek V4 Pro. For must-be-self-hosted, runs on a 24GB card, and data can't leave the box, Qwen3.6-35B-A3B. But for teams actually running at scale, the more honest mid-2026 answer isn't "pick one", it's layered routing: high-frequency lightweight on Qwen3.6-35B-A3B (zero marginal cost), hard problems on DeepSeek V4 Pro (cheap and strong), and GLM-5.2 as the open-weights best-quality fallback. The old "open vs. closed" narrative is over — the new question is "open-source you can actually run".