Ornith-1.5 launches a new 122B parameter variant: Built on the Qwen 3.5 architecture, it outperforms Qwen 3.8-Flash-Next in partial programming self-tests.
55 minutes ago
Beating AI Express, a news outlet from Dongcha, reports that DeepReinforce, an AI team founded by renowned NLP scholar Li Jiwei, has added a new 122B MoE model to the Ornith-1.5 series. The Ornith-1.5 series was first released back in August this year with three variants: 397B, 35B-A3B, and 9B; this update fills the gap for a mid-sized model. The new model’s weights are now publicly available, with versions offered for FP8, NVFP4, GGUF, and MLX. Ornith-1.5-122B uses the same architecture as Qwen3.5-122B-A10B, with a total of ~122B parameters and roughly 10B activated parameters per inference. It supports a 256K native context window, image understanding, tool calling, and inference. Official self-tests show the 122B model scored 84.5 on SWE-bench Verified (non-hack), outperforming Qwen3.8-Flash-Next’s 82.8. On CyberGym it scored 80 vs. 78, NL2Repo 51.2 vs. 45.2, and SWE-bench Pro (no-hack) 76.9 vs. 43.9. However, it still lags on Terminal-Bench 2.1 with scores of 81.2 vs. 83.4. Notably, Qwen’s official team previously scored 62.5 on SWE-bench Pro using Claude Code Harness, higher than Ornith’s 43.9 measured via OpenHands, as the two test conditions are not fully aligned.
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