MoE workstation**~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.
- 403.0B
- 262k
- mit
- 🇺🇸 USA
- Aug 2026
Scores #
Run it locally #
Per-quant memory needs and a static "can you run it?" reference - no rig entry required
Can you run it? - reference rigs
| Rig | Q4_K_M | Q5_K_M | Q6_K | Q8_0 |
|---|---|---|---|---|
| NVIDIA Jetson Orin NX 16GB | ||||
no -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudno -> cloudFit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast >=20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark.
Download options #
Or run it in the cloud #
No per-token API provider pricing tracked for Ornith-1.5-397B yet.
For flagship list prices, see the
[calculator](/calculator).
Inference cost over time #
Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.