# Ling-3.0-tiny vs Qwen-AgentWorld-35B-A3B (slim)

> Ling-3.0-tiny scores 56.2 (C) to Qwen-AgentWorld-35B-A3B's 52.1 (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-qwen-agentworld-35b-a3b.md (~10,100 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b.json · canonical https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b
- 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 Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk. 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
- Qwen-AgentWorld-35B-A3B: D 52.1, rank #141 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.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 55; Agent interface, 60 against 50; Documentation and upkeep, 56 against 41.
- Qwen-AgentWorld-35B-A3B, good for Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine Ahead on Risk, 55 against 50.

| Category | Ling-3.0-tiny | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 95 | 55 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 60 | 50 |
| Documentation and upkeep (10%) | 56 | 41 |

| Fact (where they differ) | Ling-3.0-tiny | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Alibaba (Qwen team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-08-31 | 2026-06-25 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Ling-3.0-tiny +40: 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.); Qwen-AgentWorld-35B-A3B 55 (Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share.)
- Documentation and upkeep, Ling-3.0-tiny +15: Ling-3.0-tiny 56 (Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't.); Qwen-AgentWorld-35B-A3B 41 (Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length.)
- Agent interface, Ling-3.0-tiny +10: Ling-3.0-tiny 60 (Chat template 20, a Jinja file in the repository.); Qwen-AgentWorld-35B-A3B 50 (Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json.)
- Risk, Qwen-AgentWorld-35B-A3B +5: 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.); Qwen-AgentWorld-35B-A3B 55 (File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map.)