# Ling-3.0-tiny vs LongCat-2.5 (slim)

> Ling-3.0-tiny scores 56.2 (C) to LongCat-2.5's 50.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-longcat-2-5.md (~9,500 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-longcat-2-5.json · canonical https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-longcat-2-5
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Ling-3.0-tiny scores 56.2 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface. Both do open-weight models you run yourself.

- Ling-3.0-tiny: C 56.2, rank #117 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-0-tiny.min.md
- LongCat-2.5: D 50.5, rank #145 of 160 open-weight models · https://www.anchorterminal.com/tools/longcat-2-5.min.md
- Ling-3.0-tiny, good for Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT Ahead on Reach, 95 against 35; Documentation and upkeep, 56 against 46.
- LongCat-2.5, good for Operators with NPU clusters, and anyone tracking what the open flagships can do Ahead on Risk, 60 against 50; Agent interface, 75 against 60.

| Category | Ling-3.0-tiny | LongCat-2.5 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 95 | 35 |
| Risk (20%) | 50 | 60 |
| Agent interface (10%) | 60 | 75 |
| Documentation and upkeep (10%) | 56 | 46 |

| Fact (where they differ) | Ling-3.0-tiny | LongCat-2.5 |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Meituan (LongCat) |
| Last release | 2026-08-31 | 2026-11-10 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Ling-3.0-tiny +60: Ling-3.0-tiny 95 (Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added.); LongCat-2.5 35 (Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB.)
- Agent interface, LongCat-2.5 +15: Ling-3.0-tiny 60 (Chat template 20, a Jinja file in the repository.); LongCat-2.5 75 (Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json.)
- Risk, LongCat-2.5 +10: Ling-3.0-tiny 50 (File safety gets 15, safetensors and GGUF only, but the card's vLLM command runs the repository's config code with --trust-remote-code.); LongCat-2.5 60 (File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository.)
- Documentation and upkeep, Ling-3.0-tiny +10: Ling-3.0-tiny 56 (Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't.); LongCat-2.5 46 (Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card.)