Ornith-1.5-397B

Ornith AI released Ornith-1.5-397B, a 403B-parameter mixture-of-experts agentic-coding model, on 2026-08-18, claiming coding scores on par with Claude Opus 4.8 and ahead of GLM-5.2 and DeepSeek-V4-Flash-0731, with MIT licensing on HuggingFace. The model, built on continued pretraining of Qwen 3.5 and Gemma 4 with a self-improvement loop, requires server-class hardware and is not suitable for single-RTX-5090 or DGX Spark systems.

Ornith-1.5-397B 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 - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing no - cloud cloud-pricing Fit 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.