# DeepSeek V4.1 Flash vs Ornith-1.5-122B (slim)

> DeepSeek V4.1 Flash and Ornith-1.5-122B score within a point of each other for open-weight models you run yourself, 61.5 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

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- 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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DeepSeek V4.1 Flash and Ornith-1.5-122B score within a point of each other on agent readiness, 61.5 (C) and 61 (C). Ornith-1.5-122B leads on reach and agent interface. Both do open-weight models you run yourself.

- DeepSeek V4.1 Flash: C 61.5, rank #35 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-1-flash.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
- DeepSeek V4.1 Flash, good for Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Ahead on Documentation and upkeep, 48 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; Agent interface, 85 against 75.

| Category | DeepSeek V4.1 Flash | Ornith-1.5-122B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 75 |
| Risk (20%) | 50 | 50 |
| Agent interface (10%) | 75 | 85 |
| Documentation and upkeep (10%) | 48 | 36 |

| Fact (where they differ) | DeepSeek V4.1 Flash | Ornith-1.5-122B |
| --- | --- | --- |
| Vendor | DeepSeek | Ornith |
| Last release | 2026-10-17 | 2026-10-24 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Ornith-1.5-122B +15: DeepSeek V4.1 Flash 60 (Memory scores nothing, because the only build is DeepSeek's own at 510.3 GB, about 612 GB with the fifth added, and nobody's runtime library holds a smaller one…); 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…)
- Documentation and upkeep, DeepSeek V4.1 Flash +12: DeepSeek V4.1 Flash 48 (The cards cover architecture, parameters and context but not intended use, limitations, languages or a cut-off, so 8. Training data is a general description wit…); 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.)
- Agent interface, Ornith-1.5-122B +10: DeepSeek V4.1 Flash 75 (There's no Jinja template in the 17 October repository, only code to run, so the template line gets 10. DeepSeek documents the DSML tool format both ways and sh…); Ornith-1.5-122B 85 (Chat template 20, a Jinja file in the repository.)