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OSS2026-07-20 · source-backed
Z.ai's 744B MoE is being ranked as July's strongest open-source model at a reported 91.2% GPQA Diamond and 62.1% SWE-bench Pro, at a fraction of frontier API pricing, and it's listed in Ollama's supported-model line. That's the distinction that matters this week: Kimi K3's weights don't ship until July 27, Qwen3.8-Max has no license at all, and GLM-5.2 is on disk today. Scores come from aggregator leaderboards rather than a first-party technical report, so provisional.
Each link below shares sources, entities, or timing with this story.
The open-weight race just changed constraint. Moonshot AI suspended all new consumer subscriptions on July 20, roughly 48 hours after Kimi K3 launched, because request volume pushed its compute cluster to capacity. Remaining GPUs are reserved for existing paid subscribers. Tec...
Alibaba released Qwen3.6-27B on April 22. Dense architecture. Open weights. 77.2% on SWE-bench Verified, within 3.7 points of Claude Opus 4.6. On SkillsBench, it scores 48.2% versus its own 397B MoE predecessor's 30.0%. That's a 77% improvement with 14.8x fewer parameters. Let...
GLM-5.1 from Zhipu AI scored 58.4% on SWE-bench Pro. GPT-5.4 scored 57.7%. Claude Opus 4.6 scored 57.3%. That's the first time an open-weight model has ever topped a major coding benchmark against the best proprietary models. The specs matter. GLM-5.1 is a 754B-parameter mixtu...
Moonshot AI dropped Kimi K2.7-Code on Hugging Face on June 12. The specs are loud: 1T-parameter MoE with 32B active across 384 experts, a 256K context window, Modified MIT license, tuned for long-horizon agentic software engineering (MarkTechPost). Moonshot reports +21.8% on K...
An open-weight Chinese frontier model is now a dropdown option in Microsoft's coding product. That happened before anyone finished characterizing what the model does. GitHub's changelog dated August 6 makes Kimi K3 generally available across Copilot Pro, Pro+, Max, Business an...
Moonshot AI dropped Kimi K2.6 today and the numbers are hard to ignore. One trillion parameters total, 32 billion active per token across 384 experts, 256K context window, and native multimodal input. It scores 58.6 on SWE-Bench Pro versus GPT-5.4's 57.7 and Claude Opus 4.6's...
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