# Ornith-1.5-397B

> Source: <https://tokenstead.ai/models/ornith-1-5-397b>
> Published: 2026-08-26 08:07:44+00:00

# 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 |
|

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## 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.