# LFM2.5-2.6B vs Ling-3.1-flash-1019 (slim)

> Ling-3.1-flash-1019 scores 54.8 (C) to LFM2.5-2.6B's 50.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-1-flash-1019.md (~9,550 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-1-flash-1019.json · canonical https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-1-flash-1019
- 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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Ling-3.1-flash-1019 scores 54.8 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 0 of 4 scored categories. LFM2.5-2.6B leads on reach, risk and agent interface. Both do open-weight models you run yourself.

- LFM2.5-2.6B: D 50.3, rank #146 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md
- 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
- LFM2.5-2.6B, good for Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold Ahead on Reach, 90 against 60; Risk, 50 against 45; Agent interface, 75 against 60.
- 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

| Category | LFM2.5-2.6B | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 60 |
| Risk (20%) | 50 | 45 |
| Agent interface (10%) | 75 | 60 |
| Documentation and upkeep (10%) | 57 | 56 |

| Fact (where they differ) | LFM2.5-2.6B | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Vendor | Liquid AI | inclusionAI (Ant Group) |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | MIT |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-11-04 |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Reach, LFM2.5-2.6B +30: LFM2.5-2.6B 90 (Memory earns the full 30, since Liquid's own 4-bit GGUF is 1.59 GB and fits 8 GB with room to spare.); 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.)
- Agent interface, LFM2.5-2.6B +15: LFM2.5-2.6B 75 (The chat template earns 20 for chat_template.jinja in the repository.); 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.)
- Risk, LFM2.5-2.6B +5: LFM2.5-2.6B 50 (File safety earns 25, safetensors and GGUF only, and nothing from the repository runs to load it or build prompts.); 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…)