OPEN WEIGHTS

Z.ai's GLM-5.2 lands as a 753B open-weights coding model that beats GPT-5.5 on SWE-bench Pro for roughly one-sixth the per-token cost. Here is the real cost math, a worked cost-per-task example, and a when-to-use-which decision guide.

A data-rich, source-cited comparison of the open-weights coding models that matter in 2026: GLM-5.2, DeepSeek V4, Qwen3, and the new Kimi K3 frontier entrant. Benchmark table, per-token pricing, context windows, self-host footprint, and a clear pick-X-if decision matrix.

Open weights are free to download, but inference is not free to run. Here is the honest break-even math on when self-hosting GLM-5.2, DeepSeek V4, or Llama beats paying per-token API prices - GPU rental and ownership costs, real throughput, utilization, the crossover in tokens per month, and the hidden ops bill nobody budgets for.

DeepSeek V4-Flash costs $0.28 per million output tokens. Fable 5 costs $50. That 178x gap is real - but so is the quality difference. Here is where it matters and where it does not.

Pricing deadlines, infrastructure funding, a banking prompt injection case, and a 4x speed breakthrough - June 10 was one of the densest single days the AI dev tool market has ever produced.

A first-hand visit to DeepSeek HQ reveals something more interesting than benchmark scores: a 300-person company that treats AI as infrastructure, not eschatology - and what that means for API pricing everywhere.

Gemma 4 ships byte-for-byte open weights from Google DeepMind. How developers deploy it locally, fine-tune it, and ship agents on top of it.