# Ornith-1.5-122B vs Qwen3-VL-30B-A3B-Instruct (slim)

> Ornith-1.5-122B scores 61 (C) to Qwen3-VL-30B-A3B-Instruct's 58 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct.md (~9,800 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct.json · canonical https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Ornith-1.5-122B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 1 of 4 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach and risk. Both do open-weight models you run yourself.

- Ornith-1.5-122B: C 61, rank #81 of 160 open-weight models · https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md
- Qwen3-VL-30B-A3B-Instruct: C 58, rank #104 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.min.md
- Ornith-1.5-122B, good for Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required Ahead on Agent interface, 85 against 60.
- Qwen3-VL-30B-A3B-Instruct, good for An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context Ahead on Reach, 95 against 75; Risk, 55 against 50.

| Category | Ornith-1.5-122B | Qwen3-VL-30B-A3B-Instruct |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 75 | 95 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 60 |
| Documentation and upkeep (10%) | 36 | 36 |

| Fact (where they differ) | Ornith-1.5-122B | Qwen3-VL-30B-A3B-Instruct |
| --- | --- | --- |
| Vendor | Ornith | Alibaba (Qwen team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-10-24 | 2025-11-01 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Agent interface, Ornith-1.5-122B +25: Ornith-1.5-122B 85 (Chat template 20, a Jinja file in the repository.); Qwen3-VL-30B-A3B-Instruct 60 (A Jinja chat template in the repository (20).)
- Reach, Qwen3-VL-30B-A3B-Instruct +20: Ornith-1.5-122B 75 (Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, a…); Qwen3-VL-30B-A3B-Instruct 95 (Alibaba's own Q4_K_M GGUF is 18.6 GB, so with a fifth added it needs about 22 GB, or 23 GB with the vision file, and fits 24 GB (25).)
- Risk, Qwen3-VL-30B-A3B-Instruct +5: Ornith-1.5-122B 50 (File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from…); Qwen3-VL-30B-A3B-Instruct 55 (Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25).)