# Ling-3.1-flash-1019 vs Ornith-1.5-122B (slim)

> Ornith-1.5-122B scores 61 (C) to Ling-3.1-flash-1019's 54.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ornith-1-5-122b.md (~10,300 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ornith-1-5-122b.json · canonical https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ornith-1-5-122b
- 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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Ornith-1.5-122B scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.

- Ling-3.1-flash-1019: C 54.8, rank #126 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md
- Ornith-1.5-122B: C 61, rank #81 of 160 open-weight models · https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md
- Ling-3.1-flash-1019, good for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding Ahead on Documentation and upkeep, 56 against 36.
- 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 Reach, 75 against 60; Risk, 50 against 45; Agent interface, 85 against 60.

| Category | Ling-3.1-flash-1019 | Ornith-1.5-122B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 75 |
| Risk (20%) | 45 | 50 |
| Agent interface (10%) | 60 | 85 |
| Documentation and upkeep (10%) | 56 | 36 |

| Fact (where they differ) | Ling-3.1-flash-1019 | Ornith-1.5-122B |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Ornith |
| Last release | 2026-11-04 | 2026-10-24 |

Why the edge:
- Agent interface, Ornith-1.5-122B +25: Ling-3.1-flash-1019 60 (Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning.); Ornith-1.5-122B 85 (Chat template 20, a Jinja file in the repository.)
- Documentation and upkeep, Ling-3.1-flash-1019 +20: Ling-3.1-flash-1019 56 (Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claim…); Ornith-1.5-122B 36 (Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off.)
- Reach, Ornith-1.5-122B +15: Ling-3.1-flash-1019 60 (Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build.); 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…)
- Risk, Ornith-1.5-122B +5: Ling-3.1-flash-1019 45 (File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the…); 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…)