Hy-Memory-30B-A3B: open-sourced 2026-10-15 under Apache-2.0, a 30B-total / ~3B-active MoE with a 262,144-token context window, trained specifically to run the "write" path inside agent memory frameworks (Mem0, MemOS, Hy-Memory). Two-stage training — On-Policy Distillation plus Attributed Memory RL — raised the cross-harness macro average from 47.66 (its own base model) to 55.49, which Tencent frames as beating Claude Opus 5's 54.62. Broken down, though, the Mem0/Hy-Memory/MemOS margins are all under 0.6 points — essentially statistical noise — and the real gap comes from the single held-out EverMemOS harness (56.89 vs. 54.04, a 2.85-point difference), which accounts for over 80% of the overall lead. There's no quantized build and no hosted API yet, and the companion MemHub desktop app's install path isn't working end to end.
Youtu-Parsing-Omni: open-sourced 2026-10-09, HuggingFace ID `tencent/Youtu-Parsing-Omni`, 5B parameters, 1,048,576-token context window; OmniDocBench v1.6 overall score of 96.96 (a publicly reproducible leaderboard, over 4 points ahead of Gemini-3-Pro's 92.91), and an OmniParsingBench average of 75.08 — the highest among open-weight models, second only to Gemini-3-Pro's 77.44; a single Youtu-Omni-Encoder unifies seven input types (documents, natural images, charts, flowcharts, geometry figures, audio, video) into one OmniSchema JSON output, replacing the dual-tower architecture used by prior omni models; open weights, self-hosted only with no official API pricing, and its custom license explicitly excludes use within the EU
Tencent Hy4 preview: 770B total / 49B active parameters (MoE, 78 layers), 1,048,576-token context window; API pricing $0.834 input / $2.501 output per 1M tokens (cache hit $0.042); Apache 2.0 open weights on HuggingFace; a 163-engineer blind eval scores it 2.99/4.00, just ahead of GLM-5.3 (2.92) and Kimi K3 (2.94); third-party aggregator BenchLM scores it 79.2/100, ranked #7 of 228 models; Tencent discloses for the first time that the model helped optimize its own training pipeline and inference system, lifting throughput 31.8%