# DeepSeek-V4-Flash-0731 vs Ornith-1.5-122B (slim)

> DeepSeek-V4-Flash-0731 and Ornith-1.5-122B score within a point of each other for open-weight models you run yourself, 61.9 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-122b.md (~9,600 tokens) · this version ~580 tokens · JSON https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-122b.json · canonical https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-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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DeepSeek-V4-Flash-0731 and Ornith-1.5-122B score within a point of each other on agent readiness, 61.9 (C) and 61 (C). Ornith-1.5-122B leads on reach and agent interface. Both do open-weight models you run yourself.

- DeepSeek-V4-Flash-0731: C 61.9, rank #33 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.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-Flash-0731, good for Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context
- 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 70; Agent interface, 85 against 75.

| Category | DeepSeek-V4-Flash-0731 | Ornith-1.5-122B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 70 | 75 |
| Risk (20%) | 50 | 50 |
| Agent interface (10%) | 75 | 85 |
| Documentation and upkeep (10%) | 38 | 36 |

| Fact (where they differ) | DeepSeek-V4-Flash-0731 | Ornith-1.5-122B |
| --- | --- | --- |
| Vendor | DeepSeek | Ornith |
| Last release | 2026-07-31 | 2026-10-24 |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Agent interface, Ornith-1.5-122B +10: DeepSeek-V4-Flash-0731 75 (Chat template 10, there is no Jinja template and the format is given as Python to run.); Ornith-1.5-122B 85 (Chat template 20, a Jinja file in the repository.)
- Reach, Ornith-1.5-122B +5: DeepSeek-V4-Flash-0731 70 (Memory 5, because the smallest build of 4-bit or better from a runtime's own library is LM Studio's MXFP4 GGUF at 156.4 GB, which with a fifth added needs 187.7…); 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…)