# Ornith-1.5-122B vs Qwen3.5-122B-A10B (slim)

> Qwen3.5-122B-A10B scores 63.1 (B) to Ornith-1.5-122B's 61 (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-qwen3-5-122b-a10b.md (~9,450 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-122b-a10b.json · canonical https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-122b-a10b
- 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
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Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on agent interface. 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
- Qwen3.5-122B-A10B: B 63.1, rank #25 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.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 70.
- Qwen3.5-122B-A10B, good for Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence. Ahead on Reach, 80 against 75; Risk, 55 against 50.

| Category | Ornith-1.5-122B | Qwen3.5-122B-A10B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 75 | 80 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 36 | 34 |

| Fact (where they differ) | Ornith-1.5-122B | Qwen3.5-122B-A10B |
| --- | --- | --- |
| Vendor | Ornith | Alibaba (Qwen team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-10-24 | 2026-02-24 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Agent interface, Ornith-1.5-122B +15: Ornith-1.5-122B 85 (Chat template 20, a Jinja file in the repository.); Qwen3.5-122B-A10B 70 (The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20).)
- Reach, Qwen3.5-122B-A10B +5: 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…); Qwen3.5-122B-A10B 80 (Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band.)
- Risk, Qwen3.5-122B-A10B +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…); Qwen3.5-122B-A10B 55 (Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links t…)