Ornith-1.5-397B

~403B total params, MoE - Ornith AI’s flagship self-improving agentic-coding model, released 2026-08-18. Built on continued pretraining of Qwen 3.5 / Gemma 4, then a self-improvement loop where the model proposes new tasks, generates task-specific scaffolds, and produces solution rollouts for RL (all three stages optimized jointly with GRPO). 262K context, MIT-licensed on HuggingFace at ornith-ai/Ornith-1.5-397B (FP8, GGUF, and NVFP4 quantizations).

• Coding (vendor self-reported, 5-run average): Terminal-Bench 2.1 86.1, SWE-bench Verified 86.0, SWE-bench Pro 65.1, DeepSWE 56.0, NL2Repo 59.5 - on par with Claude Opus 4.8 and ahead of GLM-5.2 and DeepSeek-V4-Flash-0731.
• Reasoning: HLE 44.6 (no tools) / 56.1 (with tools), GPQA-Diamond 92.8.
• Agentic: MCP-Atlas 80.0, Toolathlon-Verified 71.2, ClawEval 81.4.

Server-class only. ~403B MoE needs serious multi-GPU hardware (or a quantized GGUF on a workstation-class machine); it is not a single-RTX-5090 or DGX Spark model. Treat vendor benchmarks as claims until independently replicated on Artificial Analysis.